@article{Baek2023,

title = {Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA},

author = {Minkyung Baek and Ryan McHugh and Ivan Anishchenko and Hanlun Jiang and David Baker and Frank DiMaio},

url = {https://www.nature.com/articles/s41592-023-02086-5, Nature Methods [Open Access]},

doi = {10.1038/s41592-023-02086-5},

issn = {1548-7105},

year  = {2023},

date = {2023-11-23},

urldate = {2023-11-23},

journal = {Nature Methods},

publisher = {Springer Science and Business Media LLC},

abstract = {Protein–RNA and protein–DNA complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein–nucleic acid complexes without homology to known complexes is a largely unsolved problem. Here we extend the RoseTTAFold machine learning protein-structure-prediction approach to additionally predict nucleic acid and protein–nucleic acid complexes. We develop a single trained network, RoseTTAFoldNA, that rapidly produces three-dimensional structure models with confidence estimates for protein–DNA and protein–RNA complexes. Here we show that confident predictions have considerably higher accuracy than current state-of-the-art methods. RoseTTAFoldNA should be broadly useful for modeling the structure of naturally occurring protein–nucleic acid complexes, and for designing sequence-specific RNA and DNA-binding proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2023,

title = {Accurate computational design of three-dimensional protein  crystals},

author = {Zhe Li and Shunzhi Wang and Una Nattermann and Asim K Bera and Andrew J Borst and Muammer Y Yaman and Matthew J Bick and Erin C Yang and William Sheffler and Byeongdu Lee and Soenke Seifert and Greg L Hura and Hannah Nguyen and Alex Kang and Radhika Dalal and Joshua M Lubner and Yang Hsia and Hugh Haddox and Alexis Courbet and Quinton Dowling and Marcos Miranda and Andrew Favor and Ali Etemadi and Natasha I Edman and Wei Yang and Connor Weidle and Banumathi Sankaran and Babak Negahdari and Michael B Ross and David S Ginger and David Baker},

url = {https://rdcu.be/doHL5, Nature Methods},

doi = {10.1038/s41563-023-01683-1},

year  = {2023},

date = {2023-10-16},

urldate = {2023-10-01},

journal = {Nature Materials},

abstract = {Protein crystallization plays a central role in structural biology. Despite this, the process of crystallization remains poorly understood and highly empirical, with crystal contacts, lattice packing arrangements and space group preferences being largely unpredictable. Programming protein crystallization through precisely engineered side-chain–side-chain interactions across protein–protein interfaces is an outstanding challenge. Here we develop a general computational approach for designing three-dimensional protein crystals with prespecified lattice architectures at atomic accuracy that hierarchically constrains the overall number of degrees of freedom of the system. We design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into >100 µm three-dimensional crystals. The structures of these crystals are nearly identical to the computational design models, closely corresponding in both overall architecture and the specific protein–protein interactions. The dimensions of the crystal unit cell can be systematically redesigned while retaining the space group symmetry and overall architecture, and the crystals are extremely porous and highly stable. Our approach enables the computational design of protein crystals with high accuracy, and the designed protein crystals, which have both structural and assembly information encoded in their primary sequences, provide a powerful platform for biological materials engineering.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{An2023,

title = {Hallucination of closed repeat proteins containing central pockets},

author = {An L, Hicks DR, Zorine D, Dauparas J, Wicky BIM, Milles LF, Courbet A, Bera AK, Nguyen H, Kang A, Carter L, Baker D},

url = {https://www.nature.com/articles/s41594-023-01112-6, Nature Structural & Molecular Biology [Open Access] },

doi = {10.1038/s41594-023-01112-6},

year  = {2023},

date = {2023-09-28},

urldate = {2023-09-28},

journal = {Nature Structural & Molecular Biology},

abstract = {In pseudocyclic proteins, such as TIM barrels, β barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Roy2023,

title = {De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8},

author = {Anindya Roy and Lei Shi and Ashley Chang and Xianchi Dong and Andres Fernandez and John C. Kraft and Jing Li and Viet Q. Le and Rebecca Viazzo Winegar and Gerald Maxwell Cherf and Dean Slocum and P. Daniel Poulson and Garrett E. Casper and Mary L. Vallecillo-Zúniga and Jonard Corpuz Valdoz and Marcos C. Miranda and Hua Bai and Yakov Kipnis and Audrey Olshefsky and Tanu Priya and Lauren Carter and Rashmi Ravichandran and Cameron M. Chow and Max R. Johnson and Suna Cheng and McKaela Smith and Catherine Overed-Sayer and Donna K. Finch and David Lowe and Asim K. Bera and Gustavo Matute-Bello and Timothy P. Birkland and Frank DiMaio and Ganesh Raghu and Jennifer R. Cochran and Lance J. Stewart and Melody G. Campbell and Pam M. Van Ry and Timothy Springer and David Baker},

url = {https://www.nature.com/articles/s41467-023-41272-z, Nature Communications [Open Access]},

doi = {10.1038/s41467-023-41272-z},

year  = {2023},

date = {2023-09-13},

urldate = {2023-12-00},

journal = {Nature Communications},

publisher = {Springer Science and Business Media LLC},

abstract = {The RGD (Arg-Gly-Asp)-binding integrins αvβ6 and αvβ8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between homologous αvβ6 and αvβ8 and other RGD integrins, stabilize specific conformational states, and have high thermal stability could have considerable therapeutic utility. Existing small molecule and antibody inhibitors do not have all these properties, and hence new approaches are needed. Here we describe a generalized method for computationally designing RGD-containing miniproteins selective for a single RGD integrin heterodimer and conformational state. We design hyperstable, selective αvβ6 and αvβ8 inhibitors that bind with picomolar affinity. CryoEM structures of the designed inhibitor-integrin complexes are very close to the computational design models, and show that the inhibitors stabilize specific conformational states of the αvβ6 and the αvβ8 integrins. In a lung fibrosis mouse model, the αvβ6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mansoor2023,

title = {Zero‐shot Mutation Effect Prediction on Protein Stability and Function using RoseTTAFold},

author = {Sanaa Mansoor and Minkyung Baek and David Juergens and Joseph L. Watson and David Baker},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4780, Protein Science

https://www.bakerlab.org/wp-content/uploads/2023/09/Protein-Science-2023-Mansoor.pdf, PDF},

doi = {10.1002/pro.4780},

year  = {2023},

date = {2023-09-11},

urldate = {2023-09-11},

journal = {Protein Science},

publisher = {Wiley},

abstract = {Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RFjoint, for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RFjoint to both another zero‐shot model (MSA Transformer) and a model which requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RFjoint was developed to address the protein design problem of scaffolding functional motifs, RFjoint acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RFjoint has a quite broad understanding of protein sequence‐structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bethel2023,

title = {Precisely patterned nanofibres made from extendable protein multiplexes},

author = {Neville P. Bethel and Andrew J. Borst and Fabio Parmeggiani and Matthew J. Bick and TJ Brunette and Hannah Nguyen and Alex Kang and Asim K. Bera and Lauren Carter and Marcos C. Miranda and Ryan D. Kibler and Mila Lamb and Xinting Li and Banumathi Sankaran and David Baker},

url = {https://rdcu.be/dloEi, Nature Chemistry [Open Access]},

doi = {10.1038/s41557-023-01314-x},

year  = {2023},

date = {2023-09-04},

urldate = {2023-09-04},

journal = {Nature Chemistry},

publisher = {Springer Science and Business Media LLC},

abstract = {Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha-helical coiled coils have such symmetric, extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Goldbach2023,

title = {De novo design of monomeric helical bundles for pH-controlled membrane lysis},

author = {Nicolas Goldbach and Issa Benna and Basile I. M. Wicky and Jacob T. Croft and Lauren Carter and Asim K. Bera and Hannah Nguyen and Alex Kang and Banumathi Sankaran and Erin C. Yang and Kelly K. Lee and David Baker},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4769, Protein Science

https://www.bakerlab.org/wp-content/uploads/2023/08/Protein-Science-2023-Goldbach.pdf, PDF},

doi = {https://doi.org/10.1002/pro.4769},

year  = {2023},

date = {2023-08-26},

urldate = {2023-08-26},

journal = {Protein Science},

abstract = {Targeted intracellular delivery via receptor-mediated endocytosis requires the delivered cargo to escape the endosome to prevent lysosomal degradation. This can in principle be achieved by membrane lysis tightly restricted to endosomal membranes upon internalization to avoid general membrane insertion and lysis. Here we describe the design of small monomeric proteins with buried histidine containing pH-responsive hydrogen bond networks and membrane permeating amphipathic helices. Of 30 designs that were experimentally tested, all expressed in E. coli, 13 were monomeric with the expected secondary structure, and 4 designs disrupted artificial liposomes in a pH-dependent manner. Mutational analysis showed that the buried histidine hydrogen bond networks mediate pH-responsiveness and control lysis of model membranes within a very narrow range of pH (6.0 - 5.5) with almost no lysis occurring at neutral pH. These tightly controlled lytic monomers could help mediate endosomal escape in designed targeted delivery platforms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Praetorius2023,

title = {Design of stimulus-responsive two-state hinge proteins},

author = {Florian Praetorius and Philip J. Y. Leung and Maxx H. Tessmer and Adam Broerman and Cullen Demakis and Acacia F. Dishman and Arvind Pillai and Abbas Idris and David Juergens and Justas Dauparas and Xinting Li and Paul M. Levine and Mila Lamb and Ryanne K. Ballard and Stacey R. Gerben and Hannah Nguyen and Alex Kang and Banumathi Sankaran and Asim K. Bera and Brian F. Volkman and Jeff Nivala and Stefan Stoll and David Baker},

url = {https://www.science.org/stoken/author-tokens/ST-1381/full, Science (Free Access)},

doi = {10.1126/science.adg7731},

year  = {2023},

date = {2023-08-17},

urldate = {2023-08-17},

journal = {Science},

abstract = {In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of “hinge” proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled. Natural proteins often adopt multiple conformational states, thereby changing their activity or binding partners in response to another protein, small molecule, or other stimulus. It has been difficult to engineer such conformational switching between two folded states in human-designed proteins. Praetorius et al. developed a hinge-like protein by simultaneously considering both desired states in the design process. The successful designs exhibited a large shift in conformation upon binding to a target peptide helix, which could be tailored for specificity. The authors characterized the protein structures, binding kinetics, and conformational equilibrium of the designs. This work provides the groundwork for generating protein switches that respond to biological triggers and can produce conformational changes that modulate protein assemblies. —Michael A. Funk A two-state design of protein switches that couple effector binding to a conformational change is discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Watson2023,

title = {De novo design of protein structure and function with RFdiffusion},

author = {Watson, Joseph L.

and Juergens, David

and Bennett, Nathaniel R.

and Trippe, Brian L.

and Yim, Jason

and Eisenach, Helen E.

and Ahern, Woody

and Borst, Andrew J.

and Ragotte, Robert J.

and Milles, Lukas F.

and Wicky, Basile I. M.

and Hanikel, Nikita

and Pellock, Samuel J.

and Courbet, Alexis

and Sheffler, William

and Wang, Jue

and Venkatesh, Preetham

and Sappington, Isaac

and Torres, Susana Vázquez

and Lauko, Anna

and De Bortoli, Valentin

and Mathieu, Emile

and Ovchinnikov, Sergey

and Barzilay, Regina

and Jaakkola, Tommi S.

and DiMaio, Frank

and Baek, Minkyung

and Baker, David},

url = {https://www.nature.com/articles/s41586-023-06415-8, Nature

https://www.bakerlab.org/wp-content/uploads/2023/07/s41586-023-06415-8_reference.pdf, PDF (29MB)},

doi = {10.1038/s41586-023-06415-8},

year  = {2023},

date = {2023-07-11},

journal = {Nature},

abstract = {There has been considerable recent progress in designing new proteins using deep learning methods1–9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryo-EM structure of a designed binder in complex with Influenza hemagglutinin which is nearly identical to the design model. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Design of Heme Enzymes with a Tunable Substrate Binding Pocket Adjacent to an Open Metal Coordination Site},

author = {Kalvet, Indrek

and Ortmayer, Mary

and Zhao, Jingming

and Crawshaw, Rebecca

and Ennist, Nathan M.

and Levy, Colin

and Roy, Anindya

and Green, Anthony P.

and Baker, David},

url = {https://pubs.acs.org/doi/full/10.1021/jacs.3c02742, ACS (Open Access)},

doi = {10.1021/jacs.3c02742},

year  = {2023},

date = {2023-07-05},

urldate = {2023-07-05},

journal = {J. Am. Chem. Soc.},

abstract = {The catalytic versatility of pentacoordinated iron is highlighted by the broad range of natural and engineered activities of heme enzymes such as cytochrome P450s, which position a porphyrin cofactor coordinating a central iron atom below an open substrate binding pocket. This catalytic prowess has inspired efforts to design de novo helical bundle scaffolds that bind porphyrin cofactors. However, such designs lack the large open substrate binding pocket of P450s, and hence, the range of chemical transformations accessible is limited. Here, with the goal of combining the advantages of the P450 catalytic site geometry with the almost unlimited customizability of de novo protein design, we design a high-affinity heme-binding protein, dnHEM1, with an axial histidine ligand, a vacant coordination site for generating reactive intermediates, and a tunable distal pocket for substrate binding. A 1.6 Å X-ray crystal structure of dnHEM1 reveals excellent agreement to the design model with key features programmed as intended. The incorporation of distal pocket substitutions converted dnHEM1 into a proficient peroxidase with a stable neutral ferryl intermediate. In parallel, dnHEM1 was redesigned to generate enantiocomplementary carbene transferases for styrene cyclopropanation (up to 93% isolated yield, 5000 turnovers, 97:3 e.r.) by reconfiguring the distal pocket to accommodate calculated transition state models. Our approach now enables the custom design of enzymes containing cofactors adjacent to binding pockets with an almost unlimited variety of shapes and functionalities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bennett2023,

title = {Improving de novo protein binder design with deep learning},

author = {Bennett, Nathaniel R.

and Coventry, Brian

and Goreshnik, Inna

and Huang, Buwei

and Allen, Aza

and Vafeados, Dionne

and Peng, Ying Po

and Dauparas, Justas

and Baek, Minkyung

and Stewart, Lance

and DiMaio, Frank

and De Munck, Steven

and Savvides, Savvas N.

and Baker, David},

url = {https://www.nature.com/articles/s41467-023-38328-5, Nature Communications (Open Access)},

doi = {10.1038/s41467-023-38328-5},

year  = {2023},

date = {2023-05-06},

journal = {Nature Communications},

abstract = {Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lutz2023,

title = {Top-down design of protein architectures with reinforcement learning},

author = {Lutz, Isaac D.

and Wang, Shunzhi

and Norn, Christoffer

and Courbet, Alexis

and Borst, Andrew J.

and Zhao, Yan Ting

and Dosey, Annie

and Cao, Longxing

and Xu, Jinwei

and Leaf, Elizabeth M.

and Treichel, Catherine

and Litvicov, Patrisia

and Li, Zhe

and Goodson, Alexander D.

and Rivera-Sánchez, Paula

and Bratovianu, Ana-Maria

and Baek, Minkyung

and King, Neil P.

and Ruohola-Baker, Hannele

and Baker, David},

url = {https://www.science.org/doi/10.1126/science.adf6591, Science

https://www.ipd.uw.edu/wp-content/uploads/2023/04/science.adf6591.pdf, PDF},

doi = {10.1126/science.adf6591},

year  = {2023},

date = {2023-04-20},

journal = {Science},

abstract = {As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a “top-down” reinforcement learning–based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo–electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2023,

title = {De novo design of modular peptide-binding proteins by superhelical matching},

author = {Wu, Kejia

and Bai, Hua

and Chang, Ya-Ting

and Redler, Rachel

and McNally, Kerrie E.

and Sheffler, William

and Brunette, T. J.

and Hicks, Derrick R.

and Morgan, Tomos E.

and Stevens, Tim J.

and Broerman, Adam

and Goreshnik, Inna

and DeWitt, Michelle

and Chow, Cameron M.

and Shen, Yihang

and Stewart, Lance

and Derivery, Emmanuel

and Silva, Daniel Adriano

and Bhabha, Gira

and Ekiert, Damian C.

and Baker, David},

url = {https://www.nature.com/articles/s41586-023-05909-9, Nature (Open-access)},

doi = {10.1038/s41586-023-05909-9},

year  = {2023},

date = {2023-04-05},

urldate = {2023-04-05},

journal = {Nature},

abstract = {General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1–3. Here, inspired by natural and re-engineered protein–peptide systems4–11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kim2023,

title = {De novo design of small beta barrel proteins},

author = {Kim, David E.

and Jensen, Davin R.

and Feldman, David

and Tischer, Doug

and Saleem, Ayesha

and Chow, Cameron M.

and Li, Xinting

and Carter, Lauren

and Milles, Lukas

and Nguyen, Hannah

and Kang, Alex

and Bera, Asim K.

and Peterson, Francis C.

and Volkman, Brian F.

and Ovchinnikov, Sergey

and Baker, David},

url = {https://www.pnas.org/doi/10.1073/pnas.2207974120, PNAS (Open Access)},

doi = {10.1073/pnas.2207974120},

year  = {2023},

date = {2023-03-10},

urldate = {2023-03-10},

journal = {Proceedings of the National Academy of Sciences},

abstract = {Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy–based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yeh2023,

title = {De novo design of luciferases using deep learning},

author = {Yeh, Andy Hsien-Wei

 Norn, Christoffer

 Kipnis, Yakov

 Tischer, Doug

 Pellock, Samuel J.

 Evans, Declan

 Ma, Pengchen

 Lee, Gyu Rie

 Zhang, Jason Z.

 Anishchenko, Ivan

 Coventry, Brian

 Cao, Longxing

 Dauparas, Justas

 Halabiya, Samer

 DeWitt, Michelle

 Carter, Lauren

 Houk, K. N.

 Baker, David},

url = {https://www.nature.com/articles/s41586-023-05696-3, Nature (Open Access)},

doi = {10.1038/s41586-023-05696-3},

year  = {2023},

date = {2023-02-22},

journal = {Nature},

abstract = {De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence–structure relationships. Here we describe a deep-learning-based ‘family-wide hallucination’ approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M−1 s−1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Peptide-binding specificity prediction using fine-tuned protein structure prediction networks},

author = {Amir Motmaen, Justas Dauparas, Minkyung Baek, Mohamad H. Abedi, David Baker, Philip Bradley},

url = {https://www.pnas.org/doi/10.1073/pnas.2216697120, PNAS (Open Access)},

doi = {10.1073/pnas.2216697120},

year  = {2023},

date = {2023-02-21},

urldate = {2023-02-21},

journal = {Proceedings of the National Academy of Sciences},

abstract = {Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence-only models and should be particularly powerful for systems where less experimental data are available.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gerben2023,

title = {Design of Diverse Asymmetric Pockets in De Novo Homo-oligomeric Proteins},

author = {Gerben, Stacey R

and Borst, Andrew J

and Hicks, Derrick R

and Moczygemba, Isabelle

and Feldman, David

and Coventry, Brian

and Yang, Wei

and Bera, Asim K.

and Miranda, Marcos

and Kang, Alex

and Nguyen, Hannah

and Baker, David},

url = {https://pubs.acs.org/doi/full/10.1021/acs.biochem.2c00497, Biochemistry

https://www.bakerlab.org/wp-content/uploads/2023/01/Gerben_Biochemistry2023.pdf, PDF},

doi = {10.1021/acs.biochem.2c00497},

year  = {2023},

date = {2023-01-17},

journal = {Biochemistry},

abstract = {A challenge for design of protein–small-molecule recognition is that incorporation of cavities with size, shape, and composition suitable for specific recognition can considerably destabilize protein monomers. This challenge can be overcome through binding pockets formed at homo-oligomeric interfaces between folded monomers. Interfaces surrounding the central homo-oligomer symmetry axes necessarily have the same symmetry and so may not be well suited to binding asymmetric molecules. To enable general recognition of arbitrary asymmetric substrates and small molecules, we developed an approach to designing asymmetric interfaces at off-axis sites on homo-oligomers, analogous to those found in native homo-oligomeric proteins such as glutamine synthetase. We symmetrically dock curved helical repeat proteins such that they form pockets at the asymmetric interface of the oligomer with sizes ranging from several angstroms, appropriate for binding a single ion, to up to more than 20 Å across. Of the 133 proteins tested, 84 had soluble expression in E. coli, 47 had correct oligomeric states in solution, 35 had small-angle X-ray scattering (SAXS) data largely consistent with design models, and 8 had negative-stain electron microscopy (nsEM) 2D class averages showing the structures coming together as designed. Both an X-ray crystal structure and a cryogenic electron microscopy (cryoEM) structure are close to the computational design models. The nature of these proteins as homo-oligomers allows them to be readily built into higher-order structures such as nanocages, and the asymmetric pockets of these structures open rich possibilities for small-molecule binder design free from the constraints associated with monomer destabilization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hutchinson2023,

title = {Nanoparticle display of prefusion coronavirus spike elicits S1-focused cross-reactive antibody response against diverse coronavirus subgenera},

author = {Hutchinson, Geoffrey B.

and Abiona, Olubukola M.

and Ziwawo, Cynthia T.

and Werner, Anne P.

and Ellis, Daniel

and Tsybovsky, Yaroslav

and Leist, Sarah R.

and Palandjian, Charis

and West, Ande

and Fritch, Ethan J.

and Wang, Nianshuang

and Wrapp, Daniel

and Boyoglu-Barnum, Seyhan

and Ueda, George

and Baker, David

and Kanekiyo, Masaru

and McLellan, Jason S.

and Baric, Ralph S.

and King, Neil P.

and Graham, Barney S.

and Corbett-Helaire, Kizzmekia S.},

url = {https://www.nature.com/articles/s41467-023-41661-4, Nature Communications (Open Access)},

doi = {10.1038/s41467-023-41661-4},

year  = {2023},

date = {2023-10-04},

journal = {Nature Communications},

abstract = {Multivalent antigen display is a fast-growing area of interest toward broadly protective vaccines. Current nanoparticle-based vaccine candidates demonstrate the ability to confer antibody-mediated immunity against divergent strains of notably mutable viruses. In coronaviruses, this work is predominantly aimed at targeting conserved epitopes of the receptor binding domain. However, targeting conserved non-RBD epitopes could limit the potential for antigenic escape. To explore new potential targets, we engineered protein nanoparticles displaying coronavirus prefusion-stabilized spike (CoV_S-2P) trimers derived from MERS-CoV, SARS-CoV-1, SARS-CoV-2, hCoV-HKU1, and hCoV-OC43 and assessed their immunogenicity in female mice. Monotypic SARS-1 nanoparticles elicit cross-neutralizing antibodies against MERS-CoV and protect against MERS-CoV challenge. MERS and SARS nanoparticles elicit S1-focused antibodies, revealing a conserved site on the S N-terminal domain. Moreover, mosaic nanoparticles co-displaying distinct CoV_S-2P trimers elicit antibody responses to distant cross-group antigens and protect male and female mice against MERS-CoV challenge. Our findings will inform further efforts toward the development of pan-coronavirus vaccines.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Watson2023b,

title = {Synthetic Par polarity induces cytoskeleton asymmetry in unpolarized mammalian cells},

author = {Joseph L. Watson, Lara K. Kruger, Ariel J. Ben-Sasson, Alice Bittleston, Marta N. Shahbazi, Vicente Jose Planelles-Herrero, Joseph E. Chambers, James D. Manton, David Baker, and Emmanuel Derivery},

url = {https://www.cell.com/cell/fulltext/S0092-8674(23)00968-6, Cell [Open Access]},

year  = {2023},

date = {2023-09-28},

journal = {Cell},

abstract = {Polarized cells rely on a polarized cytoskeleton to function. Yet, how cortical polarity cues induce cytoskeleton polarization remains elusive. Here, we capitalized on recently established designed 2D protein arrays to ectopically engineer cortical polarity of virtually any protein of interest during mitosis in various cell types. This enables direct manipulation of polarity signaling and the identification of the cortical cues sufficient for cytoskeleton polarization. Using this assay, we dissected the logic of the Par complex pathway, a key regulator of cytoskeleton polarity during asymmetric cell division. We show that cortical clustering of any Par complex subunit is sufficient to trigger complex assembly and that the primary kinetic barrier to complex assembly is the relief of Par6 autoinhibition. Further, we found that inducing cortical Par complex polarity induces two hallmarks of asymmetric cell division in unpolarized mammalian cells: spindle orientation, occurring via Par3, and central spindle asymmetry, depending on aPKC activity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ALGHADEER2023,

title = {Single-cell census of human tooth development enables generation of human enamel},

author = {Ammar Alghadeer and Sesha Hanson-Drury and Anjali P. Patni and Devon D. Ehnes and Yan Ting Zhao and Zicong Li and Ashish Phal and Thomas Vincent and Yen C. Lim and Diana O’Day and Cailyn H. Spurrell and Aishwarya A. Gogate and Hai Zhang and Arikketh Devi and Yuliang Wang and Lea Starita and Dan Doherty and Ian A. Glass and Jay Shendure and Benjamin S. Freedman and David Baker and Mary C. Regier and Julie Mathieu and Hannele Ruohola-Baker},

url = {https://www.cell.com/developmental-cell/fulltext/S1534-5807(23)00360-X, Developmental Cell

https://www.bakerlab.org/wp-content/uploads/2023/08/PIIS153458072300360X.pdf, PDF},

doi = {https://doi.org/10.1016/j.devcel.2023.07.013},

year  = {2023},

date = {2023-08-14},

urldate = {2023-08-14},

journal = {Developmental Cell},

abstract = {Summary 

Tooth enamel secreted by ameloblasts (AMs) is the hardest material in the human body, acting as a shield to protect the teeth. However, the enamel is gradually damaged or partially lost in over 90% of adults and cannot be regenerated due to a lack of ameloblasts in erupted teeth. Here, we use single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) to establish a spatiotemporal single-cell census for the developing human tooth and identify regulatory mechanisms controlling the differentiation process of human ameloblasts. We identify key signaling pathways involved between the support cells and ameloblasts during fetal development and recapitulate those findings in human ameloblast in vitro differentiation from induced pluripotent stem cells (iPSCs). We furthermore develop a disease model of amelogenesis imperfecta in a three-dimensional (3D) organoid system and show AM maturation to mineralized structure in vivo. These studies pave the way for future regenerative dentistry.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploiting conformational dynamics to modulate the function of designed proteins},

author = {Enrico Rennella, Danny D. Sahtoe, David Baker, and Lewis E. Kay

},

url = {https://www.pnas.org/doi/10.1073/pnas.2303149120, PNAS},

doi = {10.1073/pnas.2303149120},

year  = {2023},

date = {2023-04-24},

journal = {Proceedings of the National Academy of Sciences},

abstract = {With the recent success in calculating protein structures from amino acid sequences using artificial intelligence-based algorithms, an important next step is to decipher how dynamics is encoded by the primary protein sequence so as to better predict function. Such dynamics information is critical for protein design, where strategies could then focus not only on sequences that fold into particular structures that perform a given task, but would also include low-lying excited protein states that could influence the function of the designed protein. Herein, we illustrate the importance of dynamics in modulating the function of C34, a designed α/β protein that captures β-strands of target ligands and is a member of a family of proteins designed to sequester β-strands and β hairpins of aggregation-prone molecules that lead to a variety of pathologies. Using a strategy to “see” regions of apo C34 that are invisible to NMR spectroscopy as a result of pervasive conformational exchange, as well as a mutagenesis approach whereby C34 molecules are stabilized into a single conformer, we determine the structures of the predominant conformations that are sampled by C34 and show that these attenuate the affinity for cognate peptide. Subsequently, the observed motion is exploited to develop an allosterically regulated peptide binder whose binding affinity can be controlled through the addition of a second molecule. Our study emphasizes the unique role that NMR can play in directing the design process and in the construction of new molecules with more complex functionality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Watson0000,

title = {Macrocyclic Octapeptide Binding and Inferences on Protein Substrate Binding to Histone Deacetylase 6},

author = {Watson, Paris R.

and Gupta, Suchetana

and Hosseinzadeh, Parisa

and Brown, Benjamin P.

and Baker, David

and Christianson, David W.},

url = {https://pubs.acs.org/doi/full/10.1021/acschembio.3c00113, ACS Chem. Biol.

https://www.bakerlab.org/wp-content/uploads/2023/04/acschembio.3c00113.pdf, PDF},

doi = {10.1021/acschembio.3c00113},

year  = {2023},

date = {2023-04-07},

urldate = {2023-04-07},

journal = {ACS Chemical Biology},

abstract = {Histone deacetylases (HDACs) are essential for the regulation of myriad biological processes, and their aberrant function is implicated in cancer, neurodegeneration, and other diseases. The cytosolic isozyme HDAC6 is unique among the greater family of deacetylases in that it contains two catalytic domains, CD1 and CD2. HDAC6 CD2 is responsible for tubulin deacetylase and tau deacetylase activities, inhibition of which is a key goal as new therapeutic approaches are explored. Of particular interest as HDAC inhibitors are naturally occurring cyclic tetrapeptides such as Trapoxin A or HC Toxin, or the cyclic depsipeptides Largazole and Romidepsin. Even more intriguing are larger, computationally designed macrocyclic peptide inhibitors. Here, we report the 2.0 Å resolution crystal structure of HDAC6 CD2 complexed with macrocyclic octapeptide 1. Comparison with the previously reported structure of the complex with macrocyclic octapeptide 2 reveals that a potent thiolate–zinc interaction made by the unnatural amino acid (S)-2-amino-7-sulfanylheptanoic acid contributes to nanomolar inhibitory potency for each inhibitor. Apart from this zinc-binding residue, octapeptides adopt strikingly different overall conformations and make few direct hydrogen bonds with the protein. Intermolecular interactions are dominated by water-mediated hydrogen bonds; in essence, water molecules appear to cushion the enzyme–octapeptide interface. In view of the broad specificity observed for protein substrates of HDAC6 CD2, we suggest that the binding of macrocyclic octapeptides may mimic certain features of the binding of macromolecular protein substrates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2023,

title = {Design of cell-type-specific hyperstable IL-4 mimetics via modular de novo scaffolds},

author = {Yang, Huilin

and Ulge, Umut Y.

and Quijano-Rubio, Alfredo

and Bernstein, Zachary J.

and Maestas, David R.

and Chun, Jung-Ho

and Wang, Wentao

and Lin, Jian-Xin

and Jude, Kevin M.

and Singh, Srujan

and Orcutt-Jahns, Brian T.

and Li, Peng

and Mou, Jody

and Chung, Liam

and Kuo, Yun-Huai

and Ali, Yasmin H.

and Meyer, Aaron S.

and Grayson, Warren L.

and Heller, Nicola M.

and Garcia, K. Christopher

and Leonard, Warren J.

and Silva, Daniel-Adriano

and Elisseeff, Jennifer H.

and Baker, David

and Spangler, Jamie B.},

url = {https://www.nature.com/articles/s41589-023-01313-6, Nature Chemical Biology

https://www.bakerlab.org/wp-content/uploads/2023/05/s41589-023-01313-6-1.pdf, PDF},

doi = {10.1038/s41589-023-01313-6},

year  = {2023},

date = {2023-04-06},

journal = {Nature Chemical Biology},

abstract = {The interleukin-4 (IL-4) cytokine plays a critical role in modulating immune homeostasis. Although there is great interest in harnessing this cytokine as a therapeutic in natural or engineered formats, the clinical potential of native IL-4 is limited by its instability and pleiotropic actions. Here, we design IL-4 cytokine mimetics (denoted Neo-4) based on a de novo engineered IL-2 mimetic scaffold and demonstrate that these cytokines can recapitulate physiological functions of IL-4 in cellular and animal models. In contrast with natural IL-4, Neo-4 is hyperstable and signals exclusively through the type I IL-4 receptor complex, providing previously inaccessible insights into differential IL-4 signaling through type I versus type II receptors. Because of their hyperstability, our computationally designed mimetics can directly incorporate into sophisticated biomaterials that require heat processing, such as three-dimensional-printed scaffolds. Neo-4 should be broadly useful for interrogating IL-4 biology, and the design workflow will inform targeted cytokine therapeutic development.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2023,

title = {Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains},

author = {Wang, Jing Yang (John)

and Khmelinskaia, Alena

and Sheffler, William

and Miranda, Marcos C.

and Antanasijevic, Aleksandar

and Borst, Andrew J.

and Torres, Susana V.

and Shu, Chelsea

and Hsia, Yang

and Nattermann, Una

and Ellis, Daniel

and Walkey, Carl

and Ahlrichs, Maggie

and Chan, Sidney

and Kang, Alex

and Nguyen, Hannah

and Sydeman, Claire

and Sankaran, Banumathi

and Wu, Mengyu

and Bera, Asim K.

and Carter, Lauren

and Fiala, Brooke

and Murphy, Michael

and Baker, David

and Ward, Andrew B.

and King, Neil P.},

url = {https://www.pnas.org/doi/10.1073/pnas.2214556120, PNAS (Open Access)},

doi = {10.1073/pnas.2214556120},

year  = {2023},

date = {2023-03-08},

urldate = {2023-03-08},

journal = {Proceedings of the National Academy of Sciences},

abstract = {Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lin2023,

title = {Time-tagged ticker tapes for intracellular recordings},

author = {Lin, Dingchang

and Li, Xiuyuan

and Moult, Eric

and Park, Pojeong

and Tang, Benjamin

and Shen, Hao

and Grimm, Jonathan B.

and Falco, Natalie

and Jia, Bill Z.

and Baker, David

and Lavis, Luke D.

and Cohen, Adam E.},

url = {https://www.nature.com/articles/s41587-022-01524-7, Nature Biotechnology

https://www.bakerlab.org/wp-content/uploads/2023/01/s41587-022-01524-7.pdf, PDF},

doi = {10.1038/s41587-022-01524-7},

year  = {2023},

date = {2023-01-02},

journal = {Nature Biotechnology},

abstract = {Recording transcriptional histories of a cell would enable deeper understanding of cellular developmental trajectories and responses to external perturbations. Here we describe an engineered protein fiber that incorporates diverse fluorescent marks during its growth to store a ticker tape-like history. An embedded HaloTag reporter incorporates user-supplied dyes, leading to colored stripes that map the growth of each individual fiber to wall clock time. A co-expressed eGFP tag driven by a promoter of interest records a history of transcriptional activation. High-resolution multi-spectral imaging on fixed samples reads the cellular histories, and interpolation of eGFP marks relative to HaloTag timestamps provides accurate absolute timing. We demonstrate recordings of doxycycline-induced transcription in HEK cells and cFos promoter activation in cultured neurons, with a single-cell absolute accuracy of 30–40 minutes over a 12-hour recording. The protein-based ticker tape design we present here could be generalized to achieve massively parallel single-cell recordings of diverse physiological modalities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1126/science.adg7731b,

title = {Design of stimulus-responsive two-state hinge proteins},

author = {Florian Praetorius and Philip J. Y. Leung and Maxx H. Tessmer and Adam Broerman and Cullen Demakis and Acacia F. Dishman and Arvind Pillai and Abbas Idris and David Juergens and Justas Dauparas and Xinting Li and Paul M. Levine and Mila Lamb and Ryanne K. Ballard and Stacey R. Gerben and Hannah Nguyen and Alex Kang and Banumathi Sankaran and Asim K. Bera and Brian F. Volkman and Jeff Nivala and Stefan Stoll and David Baker},

url = {https://www.science.org/doi/abs/10.1126/science.adg7731},

doi = {10.1126/science.adg7731},

year  = {2023},

date = {2023-01-01},

journal = {Science},

volume = {381},

number = {6659},

pages = {754-760},

abstract = {In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of “hinge” proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled. Natural proteins often adopt multiple conformational states, thereby changing their activity or binding partners in response to another protein, small molecule, or other stimulus. It has been difficult to engineer such conformational switching between two folded states in human-designed proteins. Praetorius et al. developed a hinge-like protein by simultaneously considering both desired states in the design process. The successful designs exhibited a large shift in conformation upon binding to a target peptide helix, which could be tailored for specificity. The authors characterized the protein structures, binding kinetics, and conformational equilibrium of the designs. This work provides the groundwork for generating protein switches that respond to biological triggers and can produce conformational changes that modulate protein assemblies. —Michael A. Funk A two-state design of protein switches that couple effector binding to a conformational change is discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bermeo2022,

title = {De novo design of obligate ABC-type heterotrimeric proteins},

author = {Bermeo, Sherry

and Favor, Andrew

and Chang, Ya-Ting

and Norris, Andrew

and Boyken, Scott E.

and Hsia, Yang

and Haddox, Hugh K.

and Xu, Chunfu

and Brunette, T. J.

and Wysocki, Vicki H.

and Bhabha, Gira

and Ekiert, Damian C.

and Baker, David},

url = {https://www.nature.com/articles/s41594-022-00879-4, Nature Structural & Molecular Biology (Open Access)},

doi = {10.1038/s41594-022-00879-4},

year  = {2022},

date = {2022-12-15},

journal = {Nature Structural & Molecular Biology},

abstract = {The de novo design of three protein chains that associate to form a heterotrimer (but not any of the possible two-chain heterodimers) and that can drive the assembly of higher-order branching structures is an important challenge for protein design. We designed helical heterotrimers with specificity conferred by buried hydrogen bond networks and large aromatic residues to enhance shape complementary packing. We obtained ten designs for which all three chains cooperatively assembled into heterotrimers with few or no other species present. Crystal structures of a helical bundle heterotrimer and extended versions, with helical repeat proteins fused to individual subunits, showed all three chains assembling in the designed orientation. We used these heterotrimers as building blocks to construct larger cyclic oligomers, which were structurally validated by electron microscopy. Our three-way junction designs provide new routes to complex protein nanostructures and enable the scaffolding of three distinct ligands for modulation of cell signaling.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kipnis2022,

title = {Design and optimization of enzymatic activity in a de novo β-barrel scaffold},

author = {Kipnis, Yakov

and Chaib, Anissa Ouald

and Vorobieva, Anastassia A.

and Cai, Guangyang

and Reggiano, Gabriella

and Basanta, Benjamin

and Kumar, Eshan

and Mittl, Peer R.E.

and Hilvert, Donald

and Baker, David},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/pro.4405, Protein Science

https://www.bakerlab.org/wp-content/uploads/2022/10/Protein-Science-2022-Kipnis-Design-and-optimization-of-enzymatic-activity-in-a-de-novo-‐barrel-scaffold.pdf, PDF},

doi = {10.1002/pro.4405},

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

journal = {Protein Science},

abstract = {While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded ?-barrel protein to create catalysts for a retro-aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Quijano-Rubio2022,

title = {A split, conditionally active mimetic of IL-2 reduces the toxicity of systemic cytokine therapy},

author = {Quijano-Rubio, Alfredo

and Bhuiyan, Aladdin M.

and Yang, Huilin

and Leung, Isabel

and Bello, Elisa

and Ali, Lestat R.

and Zhangxu, Kevin

and Perkins, Jilliane

and Chun, Jung-Ho

and Wang, Wentao

and Lajoie, Marc J.

and Ravichandran, Rashmi

and Kuo, Yun-Huai

and Dougan, Stephanie K.

and Riddell, Stanley R.

and Spangler, Jamie B.

and Dougan, Michael

and Silva, Daniel-Adriano

and Baker, David},

url = {https://www.nature.com/articles/s41587-022-01510-z, Nature Biotechnology

https://www.bakerlab.org/wp-content/uploads/2022/11/s41587-022-01510-z.pdf, PDF},

doi = {10.1038/s41587-022-01510-z},

year  = {2022},

date = {2022-10-31},

journal = {Nature Biotechnology},

abstract = {The therapeutic potential of recombinant cytokines has been limited by the severe side effects of systemic administration. We describe a strategy to reduce the dose-limiting toxicities of monomeric cytokines by designing two components that require colocalization for activity and that can be independently targeted to restrict activity to cells expressing two surface markers. We demonstrate the approach with a previously designed mimetic of cytokines interleukin-2 and interleukin-15—Neoleukin-2/15 (Neo-2/15)—both for trans-activating immune cells surrounding targeted tumor cells and for cis-activating directly targeted immune cells. In trans-activation mode, tumor antigen targeting of the two components enhanced antitumor activity and attenuated toxicity compared with systemic treatment in syngeneic mouse melanoma models. In cis-activation mode, immune cell targeting of the two components selectively expanded CD8+ T cells in a syngeneic mouse melanoma model and promoted chimeric antigen receptor T cell activation in a lymphoma xenograft model, enhancing antitumor efficacy in both cases.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Said2022,

title = {Exploration of Structured Symmetric Cyclic Peptides as Ligands for Metal-Organic Frameworks},

author = {Said, Meerit Y. and Kang, Christine S. and Wang, Shunzhi and Sheffler, William and Salveson, Patrick J. and Bera, Asim K. and Kang, Alex and Nguyen, Hannah and Ballard, Ryanne and Li, Xinting and Bai, Hua and Stewart, Lance and Levine, Paul and Baker, David},

url = {https://pubs.acs.org/doi/10.1021/acs.chemmater.2c02597, Chem. Mater.

https://www.bakerlab.org/wp-content/uploads/2022/10/Said_etal_ChemMater2022_CyclicPeptideMOFs.pdf, PDF},

doi = {/10.1021/acs.chemmater.2c02597},

year  = {2022},

date = {2022-10-25},

urldate = {2022-10-25},

journal = {Chemistry of Materials},

abstract = {Despite remarkable advances in the assembly of highly structured coordination polymers and metal–organic frameworks, the rational design of such materials using more conformationally flexible organic ligands such as peptides remains challenging. In an effort to make the design of such materials fully programmable, we first developed a computational design method for generating metal-mediated 3D frameworks using rigid and symmetric peptide macrocycles with metal-coordinating sidechains. We solved the structures of six crystalline networks involving conformationally constrained 6 to 12 residue cyclic peptides with C2, C3, and S2 internal symmetry and three different types of metals (Zn2+, Co2+, or Cu2+) by single-crystal X-ray diffraction, which reveals how the peptide sequences, backbone symmetries, and metal coordination preferences drive the assembly of the resulting structures. In contrast to smaller ligands, these peptides associate through peptide–peptide interactions without full coordination of the metals, contrary to one of the assumptions underlying our computational design method. The cyclic peptides are the largest peptidic ligands reported to form crystalline coordination polymers with transition metals to date, and while more work is required to develop methods for fully programming their crystal structures, the combination of high chemical diversity with synthetic accessibility makes them attractive building blocks for engineering a broader set of new crystalline materials for use in applications such as sensing, asymmetric catalysis, and chiral separation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chidyausiku2022,

title = {De novo design of immunoglobulin-like domains},

author = {Chidyausiku, Tamuka M.

and Mendes, Soraia R.

and Klima, Jason C.

and Nadal, Marta

and Eckhard, Ulrich

and Roel-Touris, Jorge

and Houliston, Scott

and Guevara, Tibisay

and Haddox, Hugh K.

and Moyer, Adam

and Arrowsmith, Cheryl H.

and Gomis-Rüth, F. Xavier

and Baker, David

and Marcos, Enrique},

url = {https://www.nature.com/articles/s41467-022-33004-6, Nature Communications

https://www.bakerlab.org/wp-content/uploads/2022/10/Chidyausiku_etal_NatComm_Design_of_innunoglobulin-like_domains.pdf, PDF},

year  = {2022},

date = {2022-10-03},

urldate = {2022-10-03},

journal = {Nature Communications},

abstract = {Antibodies, and antibody derivatives such as nanobodies, contain immunoglobulin-like (Ig) β-sandwich scaffolds which anchor the hypervariable antigen-binding loops and constitute the largest growing class of drugs. Current engineering strategies for this class of compounds rely on naturally existing Ig frameworks, which can be hard to modify and have limitations in manufacturability, designability and range of action. Here, we develop design rules for the central feature of the Ig fold architecture—the non-local cross-β structure connecting the two β-sheets—and use these to design highly stable Ig domains de novo, confirm their structures through X-ray crystallography, and show they can correctly scaffold functional loops. Our approach opens the door to the design of antibody-like scaffolds with tailored structures and superior biophysical properties.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wicky2022,

title = {Hallucinating symmetric protein assemblies},

author = {B. I. M. Wicky and L. F. Milles and A. Courbet and R. J. Ragotte and J. Dauparas and E. Kinfu and S. Tipps and R. D. Kibler and M. Baek and F. DiMaio and X. Li and L. Carter and A. Kang and H. Nguyen and A. K. Bera and D. Baker},

url = {https://www.science.org/doi/abs/10.1126/science.add1964, Science

https://www.bakerlab.org/wp-content/uploads/2022/09/Wicky_etal_Science2022_Hallucinating_symmetric_protein_assemblies.pdf, PDF

},

doi = {10.1126/science.add1964},

year  = {2022},

date = {2022-09-15},

journal = {Science},

abstract = {Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 Å), as are 3 cryoEM structures of giant 10 nanometer rings with up to 1550 residues and C33 symmetry; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be generated using deep learning, and pave the way for the design of increasingly complex components for nanomachines and biomaterials.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dauparas2022,

title = {Robust deep learning–based protein sequence design using ProteinMPNN},

author = {Dauparas, J.

and Anishchenko, I.

and Bennett, N.

and Bai, H.

and Ragotte, R. J.

and Milles, L. F.

and Wicky, B. I. M.

and Courbet, A.

and de Haas, R. J.

and Bethel, N.

and Leung, P. J. Y.

and Huddy, T. F.

and Pellock, S.

and Tischer, D.

and Chan, F.

and Koepnick, B.

and Nguyen, H.

and Kang, A.

and Sankaran, B.

and Bera, A. K.

and King, N. P.

and Baker, D.},

url = {https://www.science.org/doi/abs/10.1126/science.add2187, Science

https://www.bakerlab.org/wp-content/uploads/2022/09/Dauparas_etal_Science2022_Sequence_design_via_ProteinMPNN.pdf, PDF},

doi = {10.1126/science.add2187},

year  = {2022},

date = {2022-09-15},

journal = {Science},

abstract = {While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning–based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bhardwaj2022,

title = {Accurate de novo design of membrane-traversing macrocycles},

author = {Gaurav Bhardwaj and Jacob O’Connor and Stephen Rettie and Yen-Hua Huang and Theresa A. Ramelot and Vikram Khipple Mulligan and Gizem Gokce Alpkilic and Jonathan Palmer and Asim K. Bera and Matthew J. Bick and Maddalena {Di Piazza} and Xinting Li and Parisa Hosseinzadeh and Timothy W. Craven and Roberto Tejero and Anna Lauko and Ryan Choi and Calina Glynn and Linlin Dong and Robert Griffin and Wesley C. {van Voorhis} and Jose Rodriguez and Lance Stewart and Gaetano T. Montelione and David Craik and David Baker},

url = {https://www.sciencedirect.com/science/article/pii/S0092867422009229?via%3Dihub, Cell

https://www.bakerlab.org/wp-content/uploads/2022/08/1-s2.0-S0092867422009229-main.pdf, PDF},

doi = {10.1016/j.cell.2022.07.019},

year  = {2022},

date = {2022-08-29},

urldate = {2022-08-29},

journal = {Cell},

abstract = {We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed 184 6–12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6–12 residue size range cross membranes with an apparent permeability greater than 1 × 10−6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2022,

title = {Increasing Computational Protein Design Literacy through Cohort-Based Learning for Undergraduate Students},

author = {Yang, Erin C.

and Divine, Robby

and Kang, Christine S.

and Chan, Sidney

and Arenas, Elijah

and Subol, Zoe

and Tinker, Peter

and Manninen, Hayden

and Feichtenbiner, Alicia

and Mustafa, Talal

and Hallowell, Julia

and Orr, Isiac

and Haddox, Hugh

and Koepnick, Brian

and O’Connor, Jacob

and Haydon, Ian C.

and Herpoldt, Karla-Luise

and Wormer, Kandise Van

and Abell, Celine

and Baker, David

and Khmelinskaia, Alena

and King, Neil P.},

url = {https://pubs.acs.org/doi/full/10.1021/acs.jchemed.2c00500, Journal of Chemical Education

https://www.bakerlab.org/wp-content/uploads/2022/08/Yang2022acs.jchemed.2c00500.pdf, PDF},

year  = {2022},

date = {2022-08-05},

urldate = {2022-08-05},

journal = {Journal of Chemical Education},

abstract = {Undergraduate research experiences can improve student success in graduate education and STEM careers. During the COVID-19 pandemic, undergraduate researchers at our institution and many others lost their work–study research positions due to interruption of in-person research activities. This imposed a financial burden on the students and eliminated an important learning opportunity. To address these challenges, we created a paid, fully remote, cohort-based research curriculum in computational protein design. Our curriculum used existing protein design methods as a platform to first educate and train undergraduate students and then to test research hypotheses. In the first phase, students learned computational methods to assess the stability of designed protein assemblies. In the second phase, students used a larger data set to identify factors that could improve the accuracy of current protein design algorithms. This cohort-based program created valuable new research opportunities for undergraduates at our institute and enhanced the undergraduates’ feeling of connection with the lab. Students learned transferable and useful skills such as literature review, programming basics, data analysis, hypothesis testing, and scientific communication. Our program provides a model of structured computational research training opportunities for undergraduate researchers in any field for organizations looking to expand educational access.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hicks2022,

title = {De novo design of protein homodimers containing tunable symmetric protein pockets},

author = {Derrick R. Hicks and Madison A. Kennedy and Kirsten A. Thompson and Michelle DeWitt and Brian Coventry and Alex Kang and Asim K. Bera and T. J. Brunette and Banumathi Sankaran and Barry Stoddard and David Baker},

url = {https://www.pnas.org/doi/abs/10.1073/pnas.2113400119, PNAS

https://www.bakerlab.org/wp-content/uploads/2022/07/pnas.2113400119.pdf, Download PDF},

doi = {10.1073/pnas.2113400119},

year  = {2022},

date = {2022-07-21},

urldate = {2022-07-21},

journal = {Proceedings of the National Academy of Sciences},

abstract = {Proteins capable of binding arbitrary small molecules could enable the generation of new biosensors or medicines. While considerable progress has been made in recent years to design proteins from scratch capable of binding asymmetric molecules, little work has been done to facilitate the binding of symmetric molecules. Here, we present a method for generating libraries of C2 symmetric proteins with diverse central cavities that could be functionalized in the future to bind a range of C2 symmetric small molecules for applications such as ligand controllable cell engineering. We show that 31% of our designed proteins fold to the desired quaternary state, when experimentally characterized, and are hyperstable. Function follows form in biology, and the binding of small molecules requires proteins with pockets that match the shape of the ligand. For design of binding to symmetric ligands, protein homo-oligomers with matching symmetry are advantageous as each protein subunit can make identical interactions with the ligand. Here, we describe a general approach to designing hyperstable C2 symmetric proteins with pockets of diverse size and shape. We first designed repeat proteins that sample a continuum of curvatures but have low helical rise, then docked these into C2 symmetric homodimers to generate an extensive range of C2 symmetric cavities. We used this approach to design thousands of C2 symmetric homodimers, and characterized 101 of them experimentally. Of these, the geometry of 31 were confirmed by small angle X-ray scattering and 2 were shown by crystallographic analyses to be in close agreement with the computational design models. These scaffolds provide a rich set of starting points for binding a wide range of C2 symmetric compounds.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2022,

title = {Scaffolding protein functional sites using deep learning},

author = {Jue Wang and Sidney Lisanza and David Juergens and Doug Tischer and Joseph L. Watson and Karla M. Castro and Robert Ragotte and Amijai Saragovi and Lukas F. Milles and Minkyung Baek and Ivan Anishchenko and Wei Yang and Derrick R. Hicks and Marc Expòsit and Thomas Schlichthaerle and Jung-Ho Chun and Justas Dauparas and Nathaniel Bennett and Basile I. M. Wicky and Andrew Muenks and Frank DiMaio and Bruno Correia and Sergey Ovchinnikov and David Baker },

url = {https://www.science.org/doi/abs/10.1126/science.abn2100, Science

https://www.ipd.uw.edu/wp-content/uploads/2022/07/science.abn2100.pdf, Download PDF},

doi = {10.1126/science.abn2100},

year  = {2022},

date = {2022-07-21},

urldate = {2022-07-21},

journal = {Science},

abstract = {The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second approach, “inpainting,” starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests. Protein design has had success in finding sequences that fold into a desired conformation, but designing functional proteins remains challenging. Wang et al. describe two deep-learning methods to design proteins that contain prespecified functional sites. In the first, they found sequences predicted to fold into stable structures that contain the functional site. In the second, they retrained a structure prediction network to recover the sequence and full structure of a protein given only the functional site. The authors demonstrate their methods by designing proteins containing a variety of functional motifs. —VV Deep-learning methods enable the scaffolding of desired functional residues within a well-folded designed protein.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2022,

title = {Thermodynamically coupled biosensors for detecting neutralizing antibodies against SARS-CoV-2 variants},

author = {Zhang, Jason Z.

and Yeh, Hsien-Wei

and Walls, Alexandra C.

and Wicky, Basile I. M.

and Sprouse, Kaitlin R.

and VanBlargan, Laura A.

and Treger, Rebecca

and Quijano-Rubio, Alfredo

and Pham, Minh N.

and Kraft, John C.

and Haydon, Ian C.

and Yang, Wei

and DeWitt, Michelle

and Bowen, John E.

and Chow, Cameron M.

and Carter, Lauren

and Ravichandran, Rashmi

and Wener, Mark H.

and Stewart, Lance

and Veesler, David

and Diamond, Michael S.

and Greninger, Alexander L.

and Koelle, David M.

and Baker, David},

url = {https://www.nature.com/articles/s41587-022-01280-8, Nature Biotechnology

https://www.bakerlab.org/wp-content/uploads/2022/04/Zhang_etal_NatureBiotech_Thermodynamically_coupled_biosensors_for_detecting_nAbs_against_SARSCoV2_variants.pdf, Download PDF},

year  = {2022},

date = {2022-04-28},

urldate = {2022-04-28},

journal = {Nature Biotechnology},

abstract = {We designed a protein biosensor that uses thermodynamic coupling for sensitive and rapid detection of neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in serum. The biosensor is a switchable, caged luciferase–receptor-binding domain (RBD) construct that detects serum-antibody interference with the binding of virus RBD to angiotensin-converting enzyme 2 (ACE-2) as a proxy for neutralization. Our coupling approach does not require target modification and can better distinguish sample-to-sample differences in analyte binding affinity and abundance than traditional competition-based assays.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Courbet2022,

title = {Computational design of mechanically coupled axle-rotor protein assemblies},

author = {A. Courbet and J. Hansen and Y. Hsia and N. Bethel and Y.-J. Park and C. Xu and A. Moyer and S. E. Boyken and G. Ueda and U. Nattermann and D. Nagarajan and D. Silva and W. Sheffler and J. Quispe and A. Nord and N. King and P. Bradley and D. Veesler and J. Kollman and D. Baker},

url = {https://www.science.org/doi/abs/10.1126/science.abm1183, Science

https://www.bakerlab.org/wp-content/uploads/2022/04/science.abm1183.pdf, Download PDF},

year  = {2022},

date = {2022-04-21},

urldate = {2022-04-21},

journal = {Science},

abstract = {Natural molecular machines contain protein components that undergo motion relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein machinery from designed axle and rotor components with internal cyclic or dihedral symmetry. We find that the axle-rotor systems assemble in vitro and in vivo as designed. Using cryo–electron microscopy, we find that these systems populate conformationally variable relative orientations reflecting the symmetry of the coupled components and the computationally designed interface energy landscape. These mechanical systems with internal degrees of freedom are a step toward the design of genetically encodable nanomachines. Protein rotary machines such as ATP synthase contain axle-like and ring-like components and couple biochemical energy to the mechanical work of rotating the components relative to each other. Courbet et al. have taken a step toward designing such axel-rotor nanomachines. A structural requirement is that interactions between the components must be strong enough to allow assembly but still allow different rotational states to be populated. The authors met this design challenge and computationally designed ring-like protein topologies (rotors) with a range of inner diameters that accommodate designed axle-like binding partners. The systems assemble and populate the different rotational states anticipated by the designs. These rotational energy landscapes provide one of two needed elements for a directional motor. —VV Computationally designed self-assembling axle-rotor protein systems populate multiple rotational states.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hunt2022,

title = {Multivalent designed proteins neutralize SARS-CoV-2 variants of concern and confer protection against infection in mice},

author = {Andrew C. Hunt and James Brett Case and Young-Jun Park and Longxing Cao and Kejia Wu and Alexandra C. Walls and Zhuoming Liu and John E. Bowen and Hsien-Wei Yeh and Shally Saini and Louisa Helms and Yan Ting Zhao and Tien-Ying Hsiang and Tyler N. Starr and Inna Goreshnik and Lisa Kozodoy and Lauren Carter and Rashmi Ravichandran and Lydia B. Green and Wadim L. Matochko and Christy A. Thomson and Bastian Vögeli and Antje Krüger and Laura A. VanBlargan and Rita E. Chen and Baoling Ying and Adam L. Bailey and Natasha M. Kafai and Scott E. Boyken and Ajasja Ljubetič and Natasha Edman and George Ueda and Cameron M. Chow and Max Johnson and Amin Addetia and Mary Jane Navarro and Nuttada Panpradist and Michael Gale and Benjamin S. Freedman and Jesse D. Bloom and Hannele Ruohola-Baker and Sean P. J. Whelan and Lance Stewart and Michael S. Diamond and David Veesler and Michael C. Jewett and David Baker},

url = {https://www.science.org/doi/abs/10.1126/scitranslmed.abn1252, Science Translational Medicine

https://www.bakerlab.org/wp-content/uploads/2022/04/scitranslmed.abn1252.pdf, Download PDF},

doi = {10.1126/scitranslmed.abn1252},

year  = {2022},

date = {2022-04-12},

urldate = {2022-04-12},

journal = {Science Translational Medicine},

abstract = {New variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to arise and prolong the coronavirus disease 2019 (COVID-19) pandemic. Here we used a cell-free expression workflow to rapidly screen and optimize constructs containing multiple computationally designed miniprotein inhibitors of SARS-CoV-2. We found the broadest efficacy with a homo-trimeric version of the 75-residue angiotensin converting enzyme 2 (ACE2) mimic AHB2 (TRI2-2) designed to geometrically match the trimeric spike architecture. In the cryo-electron microscopy structure, TRI2 formed a tripod on top of the spike protein which engaged all three receptor binding domains (RBDs) simultaneously as in the design model. TRI2-2 neutralized Omicron (B.1.1.529), Delta (B.1.617.2), and all other variants tested with greater potency than that of monoclonal antibodies used clinically for the treatment of COVID-19. TRI2-2 also conferred prophylactic and therapeutic protection against SARS-CoV-2 challenge when administered intranasally in mice. Designed miniprotein receptor mimics geometrically arrayed to match pathogen receptor binding sites could be a widely applicable antiviral therapeutic strategy with advantages over antibodies and native receptor traps. By comparison, the designed proteins have resistance to viral escape and antigenic drift by construction, precisely tuned avidity, and greatly reduced chance of autoimmune responses. Computationally designed trivalent minibinders provide therapeutic protection in mice against emerging SARS-CoV-2 variants of concern.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cao2022,

title = {Design of protein binding proteins from target structure alone},

author = {Cao, Longxing and Coventry, Brian and Goreshnik, Inna and Huang, Buwei and Park, Joon Sung and Jude, Kevin M. and Marković, Iva and Kadam, Rameshwar U. and Verschueren, Koen H. G. and Verstraete, Kenneth and Walsh, Scott Thomas Russell and Bennett, Nathaniel and Phal, Ashish and Yang, Aerin and Kozodoy, Lisa and DeWitt, Michelle and Picton, Lora and Miller, Lauren and Strauch, Eva-Maria and DeBouver, Nicholas D. and Pires, Allison and Bera, Asim K. and Halabiya, Samer and Hammerson, Bradley and Yang, Wei and Bernard, Steffen and Stewart, Lance and Wilson, Ian A. and Ruohola-Baker, Hannele and Schlessinger, Joseph and Lee, Sangwon and Savvides, Savvas N. and Garcia, K. Christopher and Baker, David},

url = {https://www.nature.com/articles/s41586-022-04654-9, Nature

https://www.bakerlab.org/wp-content/uploads/2022/03/Cao_etal_Nature2022_Design_of_binders_from_target_structure_alone.pdf, Download PDF},

doi = {10.1038/s41586-022-04654-9},

year  = {2022},

date = {2022-03-24},

urldate = {2022-03-24},

journal = {Nature},

abstract = {The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains an outstanding challenge1–5. We describe a general solution to this problem which starts with a broad exploration of the very large space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate its very broad applicability by de novo design of binding proteins to 12 diverse protein targets with very different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and following experimental optimization bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five are very close to the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvement of both. Our approach now enables targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Generation of Potent and Stable GLP-1 Analogues Via “Serine Ligation”},

author = {Levine, Paul M. and Craven, Timothy W. and Li, Xinting and Balana, Aaron T. and Bird, Gregory H. and Godes, Marina and Salveson, Patrick J. and Erickson, Patrick W. and Lamb, Mila and Ahlrichs, Maggie and Murphy, Michael and Ogohara, Cassandra and Said, Meerit Y. and Walensky, Loren D. and Pratt, Matthew R. and Baker, David},

url = {https://pubs.acs.org/doi/abs/10.1021/acschembio.2c00075, ACS Chemical Biology

https://www.bakerlab.org/wp-content/uploads/2022/03/Levine_etal_ACSChemBio2022_GLP-1_ananlogues_by_serine_ligation.pdf, Download PDF},

doi = {10.1021/acschembio.2c00075},

year  = {2022},

date = {2022-03-23},

journal = {ACS Chemical Biology},

abstract = {Peptide and protein bioconjugation technologies have revolutionized our ability to site-specifically or chemoselectively install a variety of functional groups for applications in chemical biology and medicine, including the enhancement of bioavailability. Here, we introduce a site-specific bioconjugation strategy inspired by chemical ligation at serine that relies on a noncanonical amino acid containing a 1-amino-2-hydroxy functional group and a salicylaldehyde ester. More specifically, we harness this technology to generate analogues of glucagon-like peptide-1 that resemble Semaglutide, a long-lasting blockbuster drug currently used in the clinic to regulate glucose levels in the blood. We identify peptides that are more potent than unmodified peptide and equipotent to Semaglutide in a cell-based activation assay, improve the stability in human serum, and increase glucose disposal efficiency in vivo. This approach demonstrates the potential of “serine ligation” for various applications in chemical biology, with a particular focus on generating stabilized peptide therapeutics.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baek2022,

title = {Deep learning and protein structure modeling},

author = {Minkyung Baek and David Baker},

url = {https://www.nature.com/articles/s41592-021-01360-8, Nature Methods

https://www.bakerlab.org/wp-content/uploads/2022/01/Baek_Baker_NatureMethods2022_Deep_Learning_and_Protein_Structure_Modeling.pdf, Download PDF

},

doi = {10.1038/s41592-021-01360-8},

year  = {2022},

date = {2022-01-22},

urldate = {2022-01-22},

journal = {Nature Methods},

abstract = {Deep learning has transformed protein structure modeling. Here we relate AlphaFold and RoseTTAFold to classical physically based approaches to protein structure prediction, and discuss the many areas of structural biology that are likely to be affected by further advances in deep learning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sahtoe2022,

title = {Reconfigurable asymmetric protein assemblies through implicit negative design},

author = {Danny D. Sahtoe and Florian Praetorius and Alexis Courbet and Yang Hsia and Basile I. M. Wicky and Natasha I. Edman and Lauren M. Miller and Bart J. R. Timmermans and Justin Decarreau and Hana M. Morris and Alex Kang and Asim K. Bera and David Baker},

url = {https://www.science.org/doi/pdf/10.1126/science.abj7662

https://www.bakerlab.org/wp-content/uploads/2022/01/Sahtoe_etal_Science2022_Diverse_protein_assemblies_by_implicit_negative_design.pdf},

doi = {10.1126/science.abj7662},

year  = {2022},

date = {2022-01-21},

urldate = {2022-01-21},

journal = {Science},

abstract = {Asymmetric multiprotein complexes that undergo subunit exchange play central roles in biology but present a challenge for design because the components must not only contain interfaces that enable reversible association but also be stable and well behaved in isolation. We use implicit negative design to generate β sheet–mediated heterodimers that can be assembled into a wide variety of complexes. The designs are stable, folded, and soluble in isolation and rapidly assemble upon mixing, and crystal structures are close to the computational models. We construct linearly arranged hetero-oligomers with up to six different components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub and demonstrate that such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems. Protein complexes play important roles in biological processes, and many complexes are dynamic, with subunits exchanging to facilitate different functions. It has been challenging to design stable and soluble monomeric proteins that reversibly associate into hetero-oligomers. Sahtoe et al. used a strategy called implicit negative design to construct proteins with interaction interfaces that drive association with a selected partner but not self-association. The resulting designs are stably folded in solution and provide the modules for assembly into a wide variety of complexes. They can be functionalized, allowing target proteins to be displayed in defined geometries, and complex subunits can be exchanged by varying the available concentrations of components. —VV De novo designed protein building blocks can be modularly combined to create customized protein assemblies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Isoform-specific inhibition of FGFR signaling achieved by a de-novo-designed mini-protein},

author = {Joon Sung Park and Jungyuen Choi and Longxing Cao and Jyotidarsini Mohanty and Yoshihisa Suzuki and Andy Park and David Baker and Joseph Schlessinger and Sangwon Lee},

url = {https://www.sciencedirect.com/science/article/pii/S2211124722014012#!, Cell Reports

https://www.bakerlab.org/wp-content/uploads/2022/10/1-s2.0-S2211124722014012-main.pdf, PDF},

doi = {10.1016/j.celrep.2022.111545},

year  = {2022},

date = {2022-10-25},

journal = {Cell Reports},

abstract = {Cellular signaling by fibroblast growth factor receptors (FGFRs) is a highly regulated process mediated by specific interactions between distinct subsets of fibroblast growth factor (FGF) ligands and two FGFR isoforms generated by alternative splicing: an epithelial b- and mesenchymal c-isoforms. Here, we investigate the properties of a mini-protein, mb7, developed by an in silico design strategy to bind to the ligand-binding region of FGFR2. We describe structural, biophysical, and cellular analyses demonstrating that mb7 binds with high affinity to the c-isoforms of FGFR, resulting in inhibition of cellular signaling induced by a subset of FGFs that preferentially activate c-isoforms of FGFR. Notably, as mb7 blocks interaction between FGFR with Klotho proteins, it functions as an antagonist of the metabolic hormones FGF19 and FGF21, providing mechanistic insights and strategies for the development of therapeutics for diseases driven by aberrantly activated FGFRs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sen2022,

title = {Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs},

author = {Sen, Neeladri

and Anishchenko, Ivan

and Bordin N

and Sillitoe, Ian

and Velankar, Sameer

and Baker, David

and Orengo, Christine},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294430/},

doi = {10.1093/bib/bbac187},

year  = {2022},

date = {2022-07-18},

journal = {Briefings in Bioinformatics},

abstract = {Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Macé2022,

title = {Cryo-EM structure of a type IV secretion system},

author = {Macé, Kévin

and Vadakkepat, Abhinav K.

and Redzej, Adam

and Lukoyanova, Natalya

and Oomen, Clasien

and Braun, Nathalie

and Ukleja, Marta

and Lu, Fang

and Costa, Tiago R. D.

and Orlova, Elena V.

and Baker, David

and Cong, Qian

and Waksman, Gabriel},

url = {https://www.nature.com/articles/s41586-022-04859-y, Nature

https://www.bakerlab.org/wp-content/uploads/2022/08/Mace2022s41586-022-04859-y.pdf, PDF},

doi = {10.1038/s41586-022-04859-y},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

journal = {Nature},

abstract = {Bacterial conjugation is the fundamental process of unidirectional transfer of DNAs, often plasmid DNAs, from a donor cell to a recipient cell1. It is the primary means by which antibiotic resistance genes spread among bacterial populations2,3. In Gram-negative bacteria, conjugation is mediated by a large transport apparatus—the conjugative type IV secretion system (T4SS)—produced by the donor cell and embedded in both its outer and inner membranes. The T4SS also elaborates a long extracellular filament—the conjugative pilus—that is essential for DNA transfer4,5. Here we present a high-resolution cryo-electron microscopy (cryo-EM) structure of a 2.8 megadalton T4SS complex composed of 92 polypeptides representing 8 of the 10 essential T4SS components involved in pilus biogenesis. We added the two remaining components to the structural model using co-evolution analysis of protein interfaces, to enable the reconstitution of the entire system including the pilus. This structure describes the exceptionally large protein–protein interaction network required to assemble the many components that constitute a T4SS and provides insights on the unique mechanism by which they elaborate pili.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Agarwal2022,

title = {Rapid and Sensitive Detection of Antigen from SARS-CoV-2 Variants of Concern by a Multivalent Minibinder-Functionalized Nanomechanical Sensor},

author = {Agarwal, Dilip Kumar

and Hunt, Andrew C.

and Shekhawat, Gajendra S.

and Carter, Lauren

and Chan, Sidney

and Wu, Kejia

and Cao, Longxing

and Baker, David

and Lorenzo-Redondo, Ramon

and Ozer, Egon A.

and Simons, Lacy M.

and Hultquist, Judd F.

and Jewett, Michael C.

and Dravid, Vinayak P.},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9211039/, Analytical Chemistry},

year  = {2022},

date = {2022-06-06},

urldate = {2022-06-06},

journal = {Analytical Chemistry},

abstract = {New platforms for the rapid and sensitive detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern are urgently needed. Here we report the development of a nanomechanical sensor based on the deflection of a microcantilever capable of detecting the SARS-CoV-2 spike (S) glycoprotein antigen using computationally designed multivalent minibinders immobilized on a microcantilever surface. The sensor exhibits rapid (<5 min) detection of the target antigens down to concentrations of 0.05 ng/mL (362 fM) and is more than an order of magnitude more sensitive than an antibody-based cantilever sensor. Validation of the sensor with clinical samples from 33 patients, including 9 patients infected with the Omicron (BA.1) variant observed detection of antigen from nasopharyngeal swabs with cycle threshold (Ct) values as high as 39, suggesting a limit of detection similar to that of the quantitative reverse transcription polymerase chain reaction (RT-qPCR). Our findings demonstrate the use of minibinders and nanomechanical sensors for the rapid and sensitive detection of SARS-CoV-2 and potentially other disease markers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lovelock2022,

title = {The road to fully programmable protein catalysis},

author = {Sarah L. Lovelock and Rebecca Crawshaw and Sophie Basler and Colin Levy and David Baker and Donald Hilvert and Anthony P. Green 

},

url = {https://www.nature.com/articles/s41586-022-04456-z, Nature

https://www.bakerlab.org/wp-content/uploads/2022/06/s41586-022-04456-z.pdf, Download PDF},

doi = {10.1038/s41586-022-04456-z},

year  = {2022},

date = {2022-06-01},

journal = {Nature},

abstract = {The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yao2022,

title = {De novo design and directed folding of disulfide-bridged peptide heterodimers},

author = {Yao, Sicong and Moyer, Adam and Zheng, Yiwu and Shen, Yang and Meng, Xiaoting and Yuan, Chong and Zhao, Yibing and Yao, Hongwei and Baker, David and Wu, Chuanliu},

url = {https://www.nature.com/articles/s41467-022-29210-x, Nature Communications

https://www.bakerlab.org/wp-content/uploads/2022/03/Yao_etal_NatComms2022_Design_of_directed_folding_of_disulfile_bridged_peptide_heterodimers.pdf, Download PDF},

year  = {2022},

date = {2022-03-22},

urldate = {2022-03-22},

journal = {Nature Communications},

abstract = {Peptide heterodimers are prevalent in nature, which are not only functional macromolecules but molecular tools for chemical and synthetic biology. Computational methods have also been developed to design heterodimers of advanced functions. However, these peptide heterodimers are usually formed through noncovalent interactions, which are prone to dissociate and subject to concentration-dependent nonspecific aggregation. Heterodimers crosslinked with interchain disulfide bonds are more stable, but it represents a formidable challenge for both the computational design of heterodimers and the manipulation of disulfide pairing for heterodimer synthesis and applications. Here, we report the design, synthesis and application of interchain disulfide-bridged peptide heterodimers with mutual orthogonality by combining computational de novo designs with a directed disulfide pairing strategy. These heterodimers can be used as not only scaffolds for generating functional molecules but chemical tools or building blocks for protein labeling and construction of crosslinking hybrids. This study thus opens the door for using this unexplored dimeric structure space for many biological applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Singer2022,

title = {Large-scale design and refinement of stable proteins using sequence-only models},

author = {Singer, Jedediah M. and Novotney, Scott and Strickland, Devin and Haddox, Hugh K. and Leiby, Nicholas and Rocklin, Gabriel J. and Chow, Cameron M. and Roy, Anindya and Bera, Asim K. and Motta, Francis C. and Cao, Longxing and Strauch, Eva-Maria and Chidyausiku, Tamuka M. and Ford, Alex and Ho, Ethan and Zaitzeff, Alexander and Mackenzie, Craig O. and Eramian, Hamed and DiMaio, Frank and Grigoryan, Gevorg and Vaughn, Matthew and Stewart, Lance J. and Baker, David and Klavins, Eric

},

doi = {doi.org/10.1371/journal.pone.0265020},

year  = {2022},

date = {2022-03-14},

urldate = {2022-03-14},

journal = {PLoS ONE},

abstract = {Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model—despite weaknesses including a noisy data set—can be used to substantially increase the stability of both expert-designed and model-generated proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Levy2022,

title = {dCas9 fusion to computer-designed PRC2 inhibitor reveals functional TATA box in distal promoter region},

author = {Shiri Levy and Logeshwaran Somasundaram and Infencia Xavier Raj and Diego Ic-Mex and Ashish Phal and Sven Schmidt and Weng I. Ng and Daniel Mar and Justin Decarreau and Nicholas Moss and Ammar Alghadeer and Henrik Honkanen and Jay Sarthy and Nicholas Vitanza and R. David Hawkins and Julie Mathieu and Yuliang Wang and David Baker and Karol Bomsztyk and Hannele Ruohola-Baker},

url = {https://www.sciencedirect.com/science/article/pii/S221112472200184X, Cell Reports

https://www.bakerlab.org/wp-content/uploads/2022/03/1-s2.0-S221112472200184X-main.pdf, Download PDF},

doi = {10.1016/j.celrep.2022.110457},

year  = {2022},

date = {2022-03-01},

journal = {Cell Reports},

abstract = {Bifurcation of cellular fates, a critical process in development, requires histone 3 lysine 27 methylation (H3K27me3) marks propagated by the polycomb repressive complex 2 (PRC2). However, precise chromatin loci of functional H3K27me3 marks are not yet known. Here, we identify critical PRC2 functional sites at high resolution. We fused a computationally designed protein, EED binder (EB), which competes with EZH2 and thereby inhibits PRC2 function, to dCas9 (EBdCas9) to allow for PRC2 inhibition at a precise locus using gRNA. Targeting EBdCas9 to four different genes (TBX18, p16, CDX2, and GATA3) results in precise H3K27me3 and EZH2 reduction, gene activation, and functional outcomes in the cell cycle (p16) or trophoblast transdifferentiation (CDX2 and GATA3). In the case of TBX18, we identify a PRC2-controlled, functional TATA box >500 bp upstream of the TBX18 transcription start site (TSS) using EBdCas9. Deletion of this TATA box eliminates EBdCas9-dependent TATA binding protein (TBP) recruitment and transcriptional activation. EBdCas9 technology may provide a broadly applicable tool for epigenomic control of gene regulation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Veling2022,

title = {Natural and Designed Proteins Inspired by Extremotolerant Organisms Can Form Condensates and Attenuate Apoptosis in Human Cells},

author = {Mike T. Veling and Dan T. Nguyen and Nicole N. Thadani and Michela E. Oster and Nathan J. Rollins and Kelly P. Brock and Neville P. Bethel and Samuel Lim, David Baker and Jeffrey C. Way and Debora S. Marks and Roger L. Chang and and Pamela A. Silver},

url = {https://pubs.acs.org/doi/abs/10.1021/acssynbio.1c00572, ACS Synthetic Biology

https://www.bakerlab.org/wp-content/uploads/2022/02/Veling_etal_ACSSynBio_Feb2022.pdf, Download PDF},

doi = {10.1021/acssynbio.1c00572},

year  = {2022},

date = {2022-02-18},

journal = {ACS Synthetic Biology},

abstract = {Many organisms can survive extreme conditions and successfully recover to normal life. This extremotolerant behavior has been attributed in part to repetitive, amphipathic, and intrinsically disordered proteins that are upregulated in the protected state. Here, we assemble a library of approximately 300 naturally occurring and designed extremotolerance-associated proteins to assess their ability to protect human cells from chemically induced apoptosis. We show that several proteins from tardigrades, nematodes, and the Chinese giant salamander are apoptosis-protective. Notably, we identify a region of the human ApoE protein with similarity to extremotolerance-associated proteins that also protects against apoptosis. This region mirrors the phase separation behavior seen with such proteins, like the tardigrade protein CAHS2. Moreover, we identify a synthetic protein, DHR81, that shares this combination of elevated phase separation propensity and apoptosis protection. Finally, we demonstrate that driving protective proteins into the condensate state increases apoptosis protection, and highlights the ability of DHR81 condensates to sequester caspase-7. Taken together, this work draws a link between extremotolerance-associated proteins, condensate formation, and designing human cellular protection.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nguyen2022,

title = {Competitive Displacement of De Novo Designed HeteroDimers Can Reversibly Control Protein–Protein Interactions and Implement Feedback in Synthetic Circuits},

author = {Taylor H. Nguyen and Galen Dods and Mariana Gomez-Schiavon and Muziyue Wu and Zibo Chen and Ryan Kibler and David Baker and Hana El-Samad and Andrew H. Ng},

url = {https://www.liebertpub.com/doi/10.1089/genbio.2021.0011, GEN Biotechnology

},

doi = {10.1089/genbio.2021.0011},

year  = {2022},

date = {2022-02-16},

urldate = {2022-02-16},

journal = {GEN Biotechnology},

abstract = {Dynamic dimerization is a common regulatory interaction between biological molecules, underpinning many signaling functions. Because of its ubiquity, many biological engineering efforts have focused on building dimerizing proteins, such as the SYNZIPs and de novo Designed HeteroDimers (DHDs). Using the DHDs as a model system, we show that low-affinity protein interactions can be competitively displaced by a high-affinity “dominant negative” heterodimer. We demonstrate the utility of this signaling motif by using competitive displacement to implement negative feedback in a synthetic circuit. Competitive displacement could be extended to other heterodimer systems to expand the functionality of protein circuits and enable new biotechnology applications.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Linder2022,

title = {Interpreting neural networks for biological sequences by learning stochastic masks},

author = {Linder, Johannes and La Fleur, Alyssa and Chen, Zibo and Ljubetič, Ajasja and Baker, David and Kannan, Sreeram and Seelig, Georg},

url = {https://www.nature.com/articles/s42256-021-00428-6, Nature Machine Intelligence},

doi = {10.1038/s42256-021-00428-6},

year  = {2022},

date = {2022-01-25},

urldate = {2022-01-25},

journal = {Nature Machine Intelligence},

abstract = {Sequence-based neural networks can learn to make accurate predictions from large biological datasets, but model interpretation remains challenging. Many existing feature attribution methods are optimized for continuous rather than discrete input patterns and assess individual feature importance in isolation, making them ill-suited for interpreting nonlinear interactions in molecular sequences. Here, building on work in computer vision and natural language processing, we developed an approach based on deep learning—scrambler networks—wherein the most important sequence positions are identified with learned input masks. Scramblers learn to predict position-specific scoring matrices where unimportant nucleotides or residues are scrambled by raising their entropy. We apply scramblers to interpret the effects of genetic variants, uncover nonlinear interactions between cis-regulatory elements, explain binding specificity for protein–protein interactions, and identify structural determinants of de novo-designed proteins. We show that scramblers enable efficient attribution across large datasets and result in high-quality explanations, often outperforming state-of-the-art methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/cbic.202100435,

title = {Varying the Directionality of Protein Catalysts for Aldol and Retro-Aldol Reactions},

author = {Toshifumi Fujioka and Nobutaka Numoto and Hiroyuki Akama and Kola Shilpa and Michiko Oka and Prodip K. Roy and Yarkali Krishna and Nobutoshi Ito and David Baker and Masayuki Oda and Fujie Tanaka},

url = {https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/cbic.202100435},

doi = {https://doi.org/10.1002/cbic.202100435},

year  = {2022},

date = {2022-01-01},

journal = {ChemBioChem},

volume = {23},

number = {2},

pages = {e202100435},

abstract = {Abstract Natural aldolase enzymes and created retro-aldolase protein catalysts often catalyze both aldol and retro-aldol reactions depending on the concentrations of the reactants and the products. Here, we report that the directionality of protein catalysts can be altered by replacing one amino acid. The protein catalyst derived from a scaffold of a previously reported retro-aldolase catalyst, catalyzed aldol reactions more efficiently than the previously reported retro-aldolase catalyst. The retro-aldolase catalyst efficiently catalyzed the retro-aldol reaction but was less efficient in catalyzing the aldol reaction. The results indicate that protein catalysts with varying levels of directionality in usually reversibly catalyzed aldol and retro-aldol reactions can be generated from the same protein scaffold.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/pro.4401,

title = {Randomized gates eliminate bias in sort-seq assays},

author = {Brian L. Trippe and Buwei Huang and Erika A. DeBenedictis and Brian Coventry and Nicholas Bhattacharya and Kevin K. Yang and David Baker and Lorin Crawford},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4401},

doi = {https://doi.org/10.1002/pro.4401},

year  = {2022},

date = {2022-01-01},

journal = {Protein Science},

volume = {31},

number = {9},

pages = {e4401},

abstract = {Abstract Sort-seq assays are a staple of the biological engineering toolkit, allowing researchers to profile many groups of cells based on any characteristic that can be tied to fluorescence. However, current approaches, which segregate cells into bins deterministically based on their measured fluorescence, introduce systematic bias. We describe a surprising result: one can obtain unbiased estimates by incorporating randomness into sorting. We validate this approach in simulation and experimentally, and describe extensions for both estimating group level variances and for using multi-bin sorters.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Anishchenko2021,

title = {De novo protein design by deep network hallucination},

author = {Anishchenko, Ivan

and Pellock, Samuel J.

and Chidyausiku, Tamuka M.

and Ramelot, Theresa A.

and Ovchinnikov, Sergey

and Hao, Jingzhou

and Bafna, Khushboo

and Norn, Christoffer

and Kang, Alex

and Bera, Asim K.

and DiMaio, Frank

and Carter, Lauren

and Chow, Cameron M.

and Montelione, Gaetano T.

and Baker, David},

url = {https://www.nature.com/articles/s41586-021-04184-w

https://www.bakerlab.org/wp-content/uploads/2022/01/Anishchenko_etal_Nature2021_DeepNetworkHallucination.pdf},

doi = {10.1038/s41586-021-04184-w},

year  = {2021},

date = {2021-12-01},

urldate = {2021-12-01},

journal = {Nature},

abstract = {There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences1–3. Here we investigate whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occurring proteins used in training the models. We generate random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting residue–residue distance maps, which, as expected, are quite featureless. We then carry out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (Kullback–Leibler divergence) between the inter-residue distance distributions predicted by the network and background distributions averaged over all proteins. Optimization from different random starting points resulted in novel proteins spanning a wide range of sequences and predicted structures. We obtained synthetic genes encoding 129 of the network-‘hallucinated’ sequences, and expressed and purified the proteins in Escherichia coli; 27 of the proteins yielded monodisperse species with circular dichroism spectra consistent with the hallucinated structures. We determined the three-dimensional structures of three of the hallucinated proteins, two by X-ray crystallography and one by NMR, and these closely matched the hallucinated models. Thus, deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute alongside traditional physics-based models to the de novo design of proteins with new functions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Humphreys2012,

title = {Computed structures of core eukaryotic protein complexes},

author = {Ian R. Humphreys and Jimin Pei and Minkyung Baek and Aditya Krishnakumar and Ivan Anishchenko and Sergey Ovchinnikov and Jing Zhang and Travis J. Ness and Sudeep Banjade and Saket R. Bagde and Viktoriya G. Stancheva and Xiao-Han Li and Kaixian Liu and Zhi Zheng and Daniel J. Barrero and Upasana Roy and Jochen Kuper and Israel S. Fernández and Barnabas Szakal and Dana Branzei and Josep Rizo and Caroline Kisker and Eric C. Greene and Sue Biggins and Scott Keeney and Elizabeth A. Miller and J. Christopher Fromme and Tamara L. Hendrickson and Qian Cong and David Baker},

url = {https://www.science.org/doi/10.1126/science.abm4805, Science

https://www.bakerlab.org/wp-content/uploads/2022/06/science.abm4805.pdf, Download PDF},

doi = {10.1126/science.abm4805},

year  = {2021},

date = {2021-11-11},

urldate = {2021-11-11},

journal = {Science},

abstract = {Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning-based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1,505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as 5 subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Woodall2021,

title = {De novo design of tyrosine and serine kinase-driven protein switches},

author = {Woodall, Nicholas B.

and Weinberg, Zara

and Park, Jesslyn

and Busch, Florian

and Johnson, Richard S.

and Feldbauer, Mikayla J.

and Murphy, Michael

and Ahlrichs, Maggie

and Yousif, Issa

and MacCoss, Michael J.

and Wysocki, Vicki H.

and El-Samad, Hana

and Baker, David},

url = {https://www.nature.com/articles/s41594-021-00649-8, Nature Structural & Molecular Biology

https://www.bakerlab.org/wp-content/uploads/2021/09/De-novo-design-of-tyrosine-and-serine-kinase-driven-protein-switches.pdf, Download PDF},

doi = {10.1038/s41594-021-00649-8},

year  = {2021},

date = {2021-09-13},

urldate = {2021-09-13},

journal = {Nature Structural & Molecular Biology},

abstract = {Kinases play central roles in signaling cascades, relaying information from the outside to the inside of mammalian cells. De novo designed protein switches capable of interfacing with tyrosine kinase signaling pathways would open new avenues for controlling cellular behavior, but, so far, no such systems have been described. Here we describe the de novo design of two classes of protein switch that link phosphorylation by tyrosine and serine kinases to protein-protein association. In the first class, protein-protein association is required for phosphorylation by the kinase, while in the second class, kinase activity drives protein-protein association. We design systems that couple protein binding to kinase activity on the immunoreceptor tyrosine-based activation motif central to T-cell signaling, and kinase activity to reconstitution of green fluorescent protein fluorescence from fragments and the inhibition of the protease calpain. The designed switches are reversible and function in vitro and in cells with up to 40-fold activation of switching by phosphorylation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baek2021b,

title = {Protein oligomer modeling guided by predicted inter-chain contacts in CASP14},

author = {Minkyung Baek and Ivan Anishchenko and Hahnbeom Park and Ian R. Humphreys and David Baker},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.26197, Proteins},

doi = {10.1002/prot.26197},

year  = {2021},

date = {2021-07-29},

urldate = {2021-07-29},

journal = {Proteins},

abstract = {For CASP14, we developed deep learning-based methods for predicting homo-oligomeric and hetero-oligomeric contacts and used them for oligomer modeling. To build structure models, we developed an oligomer structure generation method that utilizes predicted inter-chain contacts to guide iterative restrained minimization from random backbone structures. We supplemented this gradient-based fold-and-dock method with template-based and ab initio docking approaches using deep learning-based subunit predictions on 29 assembly targets. These methods produced oligomer models with summed Z-scores 5.5 units higher than the next best group, with the fold-and-dock method having the best relative performance. Over the eight targets for which this method was used, the best of the five submitted models had average oligomer TM-score of 0.71 (average oligomer TM-score of the next best group: 0.64), and explicit modeling of inter-subunit interactions improved modeling of six out of 40 individual domains (ΔGDT-TS > 2.0).



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baek2021,

title = {Accurate prediction of protein structures and interactions using a three-track neural network},

author = {Baek, Minkyung

and DiMaio, Frank

and Anishchenko, Ivan

and Dauparas, Justas

and Ovchinnikov, Sergey

and Lee, Gyu Rie

and Wang, Jue

and Cong, Qian

and Kinch, Lisa N.

and Schaeffer, R. Dustin

and Millán, Claudia

and Park, Hahnbeom

and Adams, Carson

and Glassman, Caleb R.

and DeGiovanni, Andy

and Pereira, Jose H.

and Rodrigues, Andria V.

and van Dijk, Alberdina A.

and Ebrecht, Ana C.

and Opperman, Diederik J.

and Sagmeister, Theo

and Buhlheller, Christoph

and Pavkov-Keller, Tea

and Rathinaswamy, Manoj K.

and Dalwadi, Udit

and Yip, Calvin K.

and Burke, John E.

and Garcia, K. Christopher

and Grishin, Nick V.

and Adams, Paul D.

and Read, Randy J.

and Baker, David},

url = {http://science.sciencemag.org/content/early/2021/07/14/science.abj8754, Science

https://www.ipd.uw.edu/wp-content/uploads/2021/07/Baek_etal_Science2021_RoseTTAFold.pdf, Download PDF},

doi = {10.1126/science.abj8754},

year  = {2021},

date = {2021-07-15},

urldate = {2021-07-15},

journal = {Science},

abstract = {DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a three-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Koga2021,

title = {Role of backbone strain in de novo design of complex α/β protein structures},

author = {Nobuyasu Koga and Rie Koga and Gaohua Liu and Javier Castellanos and Gaetano T. Montelione and David Baker 

},

url = {https://www.nature.com/articles/s41467-021-24050-7, Nature Communications

https://www.bakerlab.org/wp-content/uploads/2021/07/Koga_NatComm2021.pdf, Download PDF},

doi = {10.1038/s41467-021-24050-7},

year  = {2021},

date = {2021-06-24},

urldate = {2021-06-24},

journal = {Nature Communications},

abstract = {We previously elucidated principles for designing ideal proteins with completely consistent local and non-local interactions which have enabled the design of a wide range of new αβ-proteins with four or fewer β-strands. The principles relate local backbone structures to supersecondary-structure packing arrangements of α-helices and β-strands. Here, we test the generality of the principles by employing them to design larger proteins with five- and six- stranded β-sheets flanked by α-helices. The initial designs were monomeric in solution with high thermal stability, and the nuclear magnetic resonance (NMR) structure of one was close to the design model, but for two others the order of strands in the β-sheet was swapped. Investigation into the origins of this strand swapping suggested that the global structures of the design models were more strained than the NMR structures. We incorporated explicit consideration of global backbone strain into the design methodology, and succeeded in designing proteins with the intended unswapped strand arrangements. These results illustrate the value of experimental structure determination in guiding improvement of de novo design, and the importance of consistency between local, supersecondary, and global tertiary interactions in determining protein topology. The augmented set of principles should inform the design of larger functional proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Case2021,

title = {Ultrapotent miniproteins targeting the SARS-CoV-2 receptor-binding domain protect against infection and disease},

author = {Case, James Brett

and Chen, Rita E.

and Cao, Longxing

and Ying, Baoling

and Winkler, Emma S.

and Johnson, Max

and Goreshnik, Inna

and Pham, Minh N.

and Shrihari, Swathi

and Kafai, Natasha M.

and Bailey, Adam L.

and Xie, Xuping

and Shi, Pei-Yong

and Ravichandran, Rashmi

and Carter, Lauren

and Stewart, Lance

and Baker, David

and Diamond, Michael S.},

url = {https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(21)00286-9, Cell Host & Microbe

https://www.bakerlab.org/wp-content/uploads/2021/07/Case_etal_CellHostMicrobe_Ultrapotent-miniproteins-targeting-the-SARS-CoV-2-receptor-binding-domain-protect-against-infection-and-disease.pdf, Download PDF},

doi = {10.1016/j.chom.2021.06.008},

year  = {2021},

date = {2021-06-18},

urldate = {2021-06-18},

journal = {Cell Host & Microbe},

abstract = {Despite the introduction of public health measures and spike protein-based vaccines to mitigate the COVID-19 pandemic, SARS-CoV-2 infections and deaths continue to have a global impact. Previously, we used a structural design approach to develop picomolar range miniproteins targeting the SARS-CoV-2 spike receptor binding domain. Here, we investigated the capacity of modified versions of one lead miniprotein, LCB1, to protect against SARS-CoV-2-mediated lung disease in mice. Systemic administration of LCB1-Fc reduced viral burden, diminished immune cell infiltration and inflammation, and completely prevented lung disease and pathology. A single intranasal dose of LCB1v1.3 reduced SARS-CoV-2 infection in the lung when given as many as five days before or two days after virus inoculation. Importantly, LCB1v1.3 protected in vivo against a historical strain (WA1/2020), an emerging B.1.1.7 strain, and a strain encoding key E484K and N501Y spike protein substitutions. These data support development of LCB1v1.3 for prevention or treatment of SARS-CoV-2 infection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bryan2021,

title = {Computational design of a synthetic PD-1 agonist},

author = {Bryan, Cassie M.

and Rocklin, Gabriel J.

and Bick, Matthew J.

and Ford, Alex

and Majri-Morrison, Sonia

and Kroll, Ashley V.

and Miller, Chad J.

and Carter, Lauren

and Goreshnik, Inna

and Kang, Alex

and DiMaio, Frank

and Tarbell, Kristin V.

and Baker, David},

url = {https://www.pnas.org/content/118/29/e2102164118, PNAS

https://www.bakerlab.org/wp-content/uploads/2021/07/Bryan_etal_PNAS2021_DeNovo_PD1_agonist.pdf, Download PDF},

year  = {2021},

date = {2021-06-11},

urldate = {2021-06-11},

journal = {Proceedings of the National Academy of Sciences},

volume = {118},

number = {29},

abstract = {Programmed cell death protein-1 (PD-1) expressed on activated T cells inhibits T cell function and proliferation to prevent an excessive immune response, and disease can result if this delicate balance is shifted in either direction. Tumor cells often take advantage of this pathway by overexpressing the PD-1 ligand PD-L1 to evade destruction by the immune system. Alternatively, if there is a decrease in function of the PD-1 pathway, unchecked activation of the immune system and autoimmunity can result. Using a combination of computation and experiment, we designed a hyperstable 40-residue miniprotein, PD-MP1, that specifically binds murine and human PD-1 at the PD-L1 interface with a Kd of ∼100 nM. The apo crystal structure shows that the binder folds as designed with a backbone RMSD of 1.3 Å to the design model. Trimerization of PD-MP1 resulted in a PD-1 agonist that strongly inhibits murine T cell activation. This small, hyperstable PD-1 binding protein was computationally designed with an all-beta interface, and the trimeric agonist could contribute to treatments for autoimmune and inflammatory diseases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vulovic2021,

title = {Generation of ordered protein assemblies using rigid three-body fusion},

author = {Vulovic, Ivan and Yao, Qing and Park, Young-Jun and Courbet, Alexis and Norris, Andrew and Busch, Florian and Sahasrabuddhe, Aniruddha and Merten, Hannes and Sahtoe, Danny D. and Ueda, George and Fallas, Jorge A. and Weaver, Sara J. and Hsia, Yang and Langan, Robert A. and Pl{"u}ckthun, Andreas and Wysocki, Vicki H. and Veesler, David and Jensen, Grant J. and Baker, David},

url = {https://www.pnas.org/content/118/23/e2015037118, PNAS

},

doi = {10.1073/pnas.2015037118},

year  = {2021},

date = {2021-06-08},

urldate = {2021-06-08},

journal = {Proceedings of the National Academy of Sciences},

volume = {118},

number = {23},

abstract = {Protein nanomaterial design is an emerging discipline with applications in medicine and beyond. A long-standing design approach uses genetic fusion to join protein homo-oligomer subunits via α-helical linkers to form more complex symmetric assemblies, but this method is hampered by linker flexibility and a dearth of geometric solutions. Here, we describe a general computational method for rigidly fusing homo-oligomer and spacer building blocks to generate user-defined architectures that generates far more geometric solutions than previous approaches. The fusion junctions are then optimized using Rosetta to minimize flexibility. We apply this method to design and test 92 dihedral symmetric protein assemblies using a set of designed homodimers and repeat protein building blocks. Experimental validation by native mass spectrometry, small-angle X-ray scattering, and negative-stain single-particle electron microscopy confirms the assembly states for 11 designs. Most of these assemblies are constructed from designed ankyrin repeat proteins (DARPins), held in place on one end by α-helical fusion and on the other by a designed homodimer interface, and we explored their use for cryogenic electron microscopy (cryo-EM) structure determination by incorporating DARPin variants selected to bind targets of interest. Although the target resolution was limited by preferred orientation effects and small scaffold size, we found that the dual anchoring strategy reduced the flexibility of the target-DARPIN complex with respect to the overall assembly, suggesting that multipoint anchoring of binding domains could contribute to cryo-EM structure determination of small proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hosseinzadeh2021,

title = {Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites},

author = {Hosseinzadeh, Parisa

and Watson, Paris R.

and Craven, Timothy W.

and Li, Xinting

and Rettie, Stephen

and Pardo-Avila, Fátima

and Bera, Asim K.

and Mulligan, Vikram Khipple

and Lu, Peilong

and Ford, Alexander S.

and Weitzner, Brian D.

and Stewart, Lance J.

and Moyer, Adam P.

and Di Piazza, Maddalena

and Whalen, Joshua G.

and Greisen, Per Jr.

and Christianson, David W.

and Baker, David},

url = {https://www.nature.com/articles/s41467-021-23609-8, Nature Communications

https://www.bakerlab.org/wp-content/uploads/2021/06/Hosseinzadeh_etal_NatureComms2021_AnchorExtention.pdf, Download PDF},

doi = {10.1038/s41467-021-23609-8},

year  = {2021},

date = {2021-06-07},

urldate = {2021-06-07},

journal = {Nature Communications},

abstract = {Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational “anchor extension” methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sahtoe2021,

title = {Transferrin receptor targeting by de novo sheet extension},

author = {Sahtoe, Danny D. and Coscia, Adrian and Mustafaoglu, Nur and Miller, Lauren M. and Olal, Daniel and Vulovic, Ivan and Yu, Ta-Yi and Goreshnik, Inna and Lin, Yu-Ru and Clark, Lars and Busch, Florian and Stewart, Lance and Wysocki, Vicki H. and Ingber, Donald E. and Abraham, Jonathan and Baker, David},

url = {https://www.pnas.org/content/118/17/e2021569118, PNAS

},

doi = {10.1073/pnas.2021569118},

year  = {2021},

date = {2021-04-27},

urldate = {2021-04-27},

journal = {Proceedings of the National Academy of Sciences},

abstract = {The de novo design of proteins that bind natural target proteins is useful for a variety of biomedical and biotechnological applications. We describe a design strategy to target proteins containing an exposed beta edge strand. We use the approach to design binders to the human transferrin receptor which shuttles back and forth across the blood{textendash}brain barrier. Such binders could be useful for the delivery of therapeutics into the brain.The de novo design of polar protein{textendash}protein interactions is challenging because of the thermodynamic cost of stripping water away from the polar groups. Here, we describe a general approach for designing proteins which complement exposed polar backbone groups at the edge of beta sheets with geometrically matched beta strands. We used this approach to computationally design small proteins that bind to an exposed beta sheet on the human transferrin receptor (hTfR), which shuttles interacting proteins across the blood{textendash}brain barrier (BBB), opening up avenues for drug delivery into the brain. We describe a design which binds hTfR with a 20 nM Kd, is hyperstable, and crosses an in vitro microfluidic organ-on-a-chip model of the human BBB. Our design approach provides a general strategy for creating binders to protein targets with exposed surface beta edge strands.Crystal structures have been deposited in the RCSB PDB with the accession nos. 6WRX, 6WRW, and 6WRV. Additional supporting data has been deposited in the online Zenodo repository (https://zenodo.org/record/4594115) (47). All other study data are included in the article and/or supporting information.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hsia2012,

title = {Design of multi-scale protein complexes by hierarchical building block fusion},

author = {Hsia, Yang

and Mout, Rubul

and Sheffler, William

and Edman, Natasha I.

and Vulovic, Ivan

and Park, Young-Jun

and Redler, Rachel L.

and Bick, Matthew J.

and Bera, Asim K.

and Courbet, Alexis

and Kang, Alex

and Brunette, T. J.

and Nattermann, Una

and Tsai, Evelyn

and Saleem, Ayesha

and Chow, Cameron M.

and Ekiert, Damian

and Bhabha, Gira

and Veesler, David

and Baker, David},

url = {https://www.nature.com/articles/s41467-021-22276-z, Nature Communications

https://www.bakerlab.org/wp-content/uploads/2021/04/Hsia_etal_NatComms_WORMS.pdf, Download PDF},

doi = {10.1038/s41467-021-22276-z},

year  = {2021},

date = {2021-04-16},

urldate = {2021-04-16},

journal = {Nature Communications},

abstract = {A systematic and robust approach to generating complex protein nanomaterials would have broad utility. We develop a hierarchical approach to designing multi-component protein assemblies from two classes of modular building blocks: designed helical repeat proteins (DHRs) and helical bundle oligomers (HBs). We first rigidly fuse DHRs to HBs to generate a large library of oligomeric building blocks. We then generate assemblies with cyclic, dihedral, and point group symmetries from these building blocks using architecture guided rigid helical fusion with new software named WORMS. X-ray crystallography and cryo-electron microscopy characterization show that the hierarchical design approach can accurately generate a wide range of assemblies, including a 43 nm diameter icosahedral nanocage. The computational methods and building block sets described here provide a very general route to de novo designed protein nanomaterials.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Divine2021,

title = {Designed proteins assemble antibodies into modular nanocages},

author = {Divine, Robby and Dang, Ha V. and Ueda, George and Fallas, Jorge A. and Vulovic, Ivan and Sheffler, William and Saini, Shally and Zhao, Yan Ting and Raj, Infencia Xavier and Morawski, Peter A. and Jennewein, Madeleine F. and Homad, Leah J. and Wan, Yu-Hsin and Tooley, Marti R. and Seeger, Franziska and Etemadi, Ali and Fahning, Mitchell L. and Lazarovits, James and Roederer, Alex and Walls, Alexandra C. and Stewart, Lance and Mazloomi, Mohammadali and King, Neil P. and Campbell, Daniel J. and McGuire, Andrew T. and Stamatatos, Leonidas and Ruohola-Baker, Hannele and Mathieu, Julie and Veesler, David and Baker, David},

url = {https://science.sciencemag.org/content/372/6537/eabd9994.full.pdf, Science

https://www.bakerlab.org/wp-content/uploads/2021/04/Divine_etal_Science2021_Antibody_nanocages.pdf, Download PDF},

doi = {10.1126/science.abd9994},

year  = {2021},

date = {2021-04-02},

urldate = {2021-04-02},

journal = {Science},

volume = {372},

number = {6537},

abstract = {Antibodies are broadly used in therapies and as research tools because they can be generated against a wide range of targets. Efficacy can often be increased by clustering antibodies in multivalent assemblies. Divine et al. designed antibody nanocages from two components: One is an antibody-binding homo-oligomic protein and the other is the antibody itself. Computationally designed proteins drive the assembly of antibody nanocages in a range of architectures, allowing control of the symmetry and the antibody valency. The multivalent display enhances antibody-dependent signaling, and nanocages displaying antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein effectively neutralize pseudovirus.Science, this issue p. eabd9994INTRODUCTIONAntibodies that bind tightly to targets of interest play central roles in biological research and medicine. Clusters of antibodies, typically generated by fusing antibodies to polymers or genetically linking antibody fragments together, can enhance signaling. Currently lacking are approaches for making antibody assemblies with a range of precisely specified architectures and valencies.RATIONALEWe set out to computationally design proteins that assemble antibodies into precise architectures with different valencies and symmetries. We developed an approach to designing proteins that position antibodies or Fc-fusions on the twofold symmetry axes of regular dihedral and polyhedral architectures. We hypothesized that such designs could robustly drive arbitrary antibodies into homogeneous and structurally well-defined nanocages and that such assemblies could have pronounced effects on cell signaling.RESULTSAntibody cage (AbC){textendash}forming designs were created by rigidly fusing antibody constant domain{textendash}binding modules to cyclic oligomers through helical spacer domains such that the symmetry axes of the dimeric antibody and cyclic oligomer are at orientations that generate different dihedral or polyhedral (e.g., tetrahedral, octahedral, or icosahedral) architectures. The junction regions between the connected building blocks were optimized to fold to the designed structures. Synthetic genes encoding the designs were expressed in bacterial cultures; of 48 structurally characterized designs, eight assemblies matched the design models. Successful designs encompass D2 dihedral (three designs), T32 tetrahedral (two designs), O42 octahedral (one design), and I52 icosahedral (two designs) architectures; these contain 2, 6, 12, or 30 antibodies, respectively.We investigated the effects of AbCs on cell signaling. AbCs formed with a death receptor{textendash}targeting antibody induced apoptosis of tumor cell lines that were unaffected by the soluble antibody or the native ligand. Angiopoietin pathway signaling, CD40 signaling, and T cell proliferation were all enhanced by assembling Fc-fusions or antibodies in AbCs. AbC formation also enhanced in vitro viral neutralization of a severe acute respiratory syndrome coronavirus 2 pseudovirus.CONCLUSIONWe have designed multiple antibody cage{textendash}forming proteins that precisely cluster any protein A{textendash}binding antibody into nanocages with controlled valency and geometry. AbCs can be formed with 2, 6, 12, or 30 antibodies simply by mixing the antibody with the corresponding designed protein, without the need for any covalent modification of the antibody. Incorporating receptor binding or virus-neutralizing antibodies into AbCs enhanced their biological activity across a range of cell systems. We expect that our rapid and robust approach for assembling antibodies into homogeneous and ordered nanocages without the need for covalent modification will have broad utility in research and medicine.Designed proteins assemble antibodies into large symmetric architectures.Designed antibody-clustering proteins (light gray) assemble antibodies (purple) into diverse nanocage architectures (top). Antibody nanocages enhance cell signaling compared with free antibodies (bottom).IMAGE: IAN HAYDON, INSTITUTE FOR PROTEIN DESIGNMultivalent display of receptor-engaging antibodies or ligands can enhance their activity. Instead of achieving multivalency by attachment to preexisting scaffolds, here we unite form and function by the computational design of nanocages in which one structural component is an antibody or Fc-ligand fusion and the second is a designed antibody-binding homo-oligomer that drives nanocage assembly. Structures of eight nanocages determined by electron microscopy spanning dihedral, tetrahedral, octahedral, and icosahedral architectures with 2, 6, 12, and 30 antibodies per nanocage, respectively, closely match the corresponding computational models. Antibody nanocages targeting cell surface receptors enhance signaling compared with free antibodies or Fc-fusions in death receptor 5 (DR5){textendash}mediated apoptosis, angiopoietin-1 receptor (Tie2){textendash}mediated angiogenesis, CD40 activation, and T cell proliferation. Nanocage assembly also increases severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudovirus neutralization by α-SARS-CoV-2 monoclonal antibodies and Fc{textendash}angiotensin-converting enzyme 2 (ACE2) fusion proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mulligan2021,

title = {Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1},

author = {Mulligan, Vikram Khipple and Workman, Sean and Sun, Tianjun and Rettie, Stephen and Li, Xinting and Worrall, Liam J. and Craven, Timothy W. and King, Dustin T. and Hosseinzadeh, Parisa and Watkins, Andrew M. and Renfrew, P. Douglas and Guffy, Sharon and Labonte, Jason W. and Moretti, Rocco and Bonneau, Richard and Strynadka, Natalie C. J. and Baker, David},

url = {https://www.pnas.org/content/118/12/e2012800118.full, PNAS

https://www.bakerlab.org/wp-content/uploads/2021/03/Mulligen_etal_PNAS2021_Macrocycle_inhibitors.pdf, Download PDF},

doi = {10.1073/pnas.2012800118},

year  = {2021},

date = {2021-03-23},

urldate = {2021-03-23},

journal = {Proceedings of the National Academy of Sciences},

volume = {118},

number = {12},

abstract = {The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of L- and D-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Norn2021,

title = {Protein sequence design by conformational landscape optimization},

author = {Norn, Christoffer and Wicky, Basile I. M. and Juergens, David and Liu, Sirui and Kim, David and Tischer, Doug and Koepnick, Brian and Anishchenko, Ivan and Baker, David and Ovchinnikov, Sergey},

url = {https://www.pnas.org/content/118/11/e2017228118, PNAS

https://www.bakerlab.org/wp-content/uploads/2021/03/Norn_etal_PNAS2021_LandscapeOptimization.pdf, Download PDF},

doi = {10.1073/pnas.2017228118},

year  = {2021},

date = {2021-03-16},

urldate = {2021-03-16},

journal = {Proceedings of the National Academy of Sciences},

volume = {118},

number = {11},

abstract = {Almost all proteins fold to their lowest free energy state, which is determined by their amino acid sequence. Computational protein design has primarily focused on finding sequences that have very low energy in the target designed structure. However, what is most relevant during folding is not the absolute energy of the folded state but the energy difference between the folded state and the lowest-lying alternative states. We describe a deep learning approach that captures aspects of the folding landscape, in particular the presence of structures in alternative energy minima, and show that it can enhance current protein design methods.The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen{textquoteright}s thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Coventry2021,

title = {Protein sequence optimization with a pairwise decomposable penalty for buried unsatisfied hydrogen bonds},

author = {Coventry B and Baker D},

url = {https://doi.org/10.1371/journal.pcbi.1008061, PLoS Computational Biology

https://www.bakerlab.org/wp-content/uploads/2021/03/journal.pcbi_.1008061.pdf, Download PDF},

year  = {2021},

date = {2021-03-08},

urldate = {2021-03-08},

journal = {PLoS Computational Biology},

abstract = {In aqueous solution, polar groups make hydrogen bonds with water, and hence burial of such groups in the interior of a protein is unfavorable unless the loss of hydrogen bonds with water is compensated by formation of new ones with other protein groups. For this reason, buried “unsatisfied” polar groups making no hydrogen bonds are very rare in proteins. Efficiently representing the energetic cost of unsatisfied hydrogen bonds with a pairwise-decomposable energy term during protein design is challenging since whether or not a group is satisfied depends on all of its neighbors. Here we describe a method for assigning a pairwise-decomposable energy to sidechain rotamers such that following combinatorial sidechain packing, buried unsaturated polar atoms are penalized. The penalty can be any quadratic function of the number of unsatisfied polar groups, and can be computed very rapidly. We show that inclusion of this term in Rosetta sidechain packing calculations substantially reduces the number of buried unsatisfied polar groups.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vorobieva2021,

title = {De novo design of transmembrane beta barrels},

author = {Vorobieva, Anastassia A. and White, Paul and Liang, Binyong and Horne, Jim E. and Bera, Asim K. and Chow, Cameron M. and Gerben, Stacey and Marx, Sinduja and Kang, Alex and Stiving, Alyssa Q. and Harvey, Sophie R. and Marx, Dagan C. and Khan, G. Nasir and Fleming, Karen G. and Wysocki, Vicki H. and Brockwell, David J. and Tamm, Lukas K. and Radford, Sheena E. and Baker, David},

url = {https://science.sciencemag.org/content/371/6531/eabc8182, Science

https://www.bakerlab.org/wp-content/uploads/2021/02/Vorobieva_etal_Science2021_De_Novo_Transmembrane_beta_barrels.pdf, Download PDF},

doi = {10.1126/science.abc8182},

year  = {2021},

date = {2021-02-19},

urldate = {2021-02-19},

journal = {Science},

volume = {371},

number = {6531},

abstract = {Computational design offers the possibility of making proteins with customized structures and functions. The range of accessible protein scaffolds has expanded with the design of increasingly complex cytoplasmic proteins and, recently, helical membrane proteins. Vorobieva et al. describe the successful computational design of eight-stranded transmembrane β-barrel proteins (TMBs). Using an iterative approach, they show the importance of negative design to prevent off-target structures and gain insight into the sequence determinants of TMB folding. Twenty-three designs satisfied biochemical screens for a TMB structure, and two structures were experimentally validated by nuclear magnetic resonance spectroscopy or x-ray crystallography. This is a step toward the custom design of pores for applications such as single-molecule sequencing.Science, this issue p. eabc8182INTRODUCTIONDespite their key biological roles, only a few proteins that fold into lipid membranes have been designed de novo. A class of membrane proteins{textemdash}transmembrane β barrels (TMBs){textemdash}forms a continuous sheet that closes on itself in lipid membranes. In addition to the challenge of designing β-sheet proteins, which are prone to misfolding and aggregation if folding is not properly controlled, the computational design of TMBs is complicated by limited understanding of TMB folding. As a result, no TMB has been designed de novo to date.Although the folding of TMBs in vivo is catalyzed by the β-barrel assembly machinery (BAM), many TMBs can also fold spontaneously in synthetic membranes to form stable pores, making them attractive for biotechnology and single-molecule analytical applications. Hence, de novo design of TMBs has potential both for understanding the determinants of TMB folding and membrane insertion and for the custom engineering of TMB nanopores.RATIONALEWe used de novo protein design to distill key principles of TMB folding through several design-build-test cycles. We iterated between hypothesis formulation, its implementation into computational design methods, and experimental characterization of the resulting proteins. To focus on the fundamental principles of TMB folding in the absence of complications due to interactions with chaperones and BAM in vivo, we focused on the challenge of de novo design of eight-stranded TMBs, which can fold and assemble into synthetic lipid membranes.RESULTSWe used a combination of purely geometric models and explicit Rosetta protein structure simulations to determine the constraints that β-strand connectivity and membrane embedding place on the TMB architecture. Through a series of design-build-test cycles, we found that, unlike almost all other classes of proteins, locally destabilizing sequences are critical for expression and folding of TMBs, and that the β-turns that translocate through the bilayer during folding have to be destabilized to enable correct assembly in the membrane. Our results suggest that premature formation of β hairpins may result in off-target β-sheet structures that compete with proper membrane insertion and folding, and hence the β hairpins of TMBs must be designed such that they are only transiently formed prior to membrane insertion, when the protein is in an aqueous environment. In the hydrophobic environment of the lipid bilayer, the full TMB can assemble because the membrane-facing nonpolar residues, which would tend to cluster nonspecifically in an aqueous environment, instead make favorable interactions with the lipids. As the TMB assembles, the β hairpins are stabilized by interactions with the neighboring β strands.Using computational methods that incorporate the above insights, we designed TMB sequences that successfully fold and assemble into both detergent micelles and lipid bilayers. Two of the designs were highly stable and could fold into liposomes more rapidly and reversibly than the transmembrane domain of the model outer membrane protein A (tOmpA) of Escherichia coli. A nuclear magnetic resonance solution structure and a high-resolution crystal structure for two different designs closely match the design models, showing that the TMB design method developed here can generate new structures with atomic-level accuracy.CONCLUSIONThis study elucidates key principles for de novo design of transmembrane β barrels, ranging from constraints on β-barrel architecture and β-hairpin design, as well as local and global sequence features. Our designs provide starting points for the bottom-up elucidation of the molecular mechanisms underlying TMB folding and interactions with the cellular outer membrane folding and insertion machinery. More generally, our work demonstrates that TMBs can be designed with atomic-level accuracy and opens the door to custom design of nanopores tailored for applications such as single-molecule sensing and sequencing.De novo{textendash}designed eight-stranded transmembrane β barrels fold spontaneously and reversibly into synthetic lipid membranes.The illustration shows the crystal structure of the protein TMB2.17 designed in this study, which adopts a structure identical to the design model.Credit: Ian Haydon.Transmembrane β-barrel proteins (TMBs) are of great interest for single-molecule analytical technologies because they can spontaneously fold and insert into membranes and form stable pores, but the range of pore properties that can be achieved by repurposing natural TMBs is limited. We leverage the power of de novo computational design coupled with a {textquotedblleft}hypothesis, design, and test{textquotedblright} approach to determine TMB design principles, notably, the importance of negative design to slow β-sheet assembly. We design new eight-stranded TMBs, with no homology to known TMBs, that insert and fold reversibly into synthetic lipid membranes and have nuclear magnetic resonance and x-ray crystal structures very similar to the computational models. These advances should enable the custom design of pores for a wide range of applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Klima2021,

title = {Incorporation of sensing modalities into de novo designed fluorescence-activating proteins},

author = {Klima, Jason C.

and Doyle, Lindsey A.

and Lee, Justin Daho

and Rappleye, Michael

and Gagnon, Lauren A.

and Lee, Min Yen

and Barros, Emilia P.

and Vorobieva, Anastassia A.

and Dou, Jiayi

and Bremner, Samantha

and Quon, Jacob S.

and Chow, Cameron M.

and Carter, Lauren

and Mack, David L.

and Amaro, Rommie E.

and Vaughan, Joshua C.

and Berndt, Andre

and Stoddard, Barry L.

and Baker, David},

url = {https://www.nature.com/articles/s41467-020-18911-w, Nature Communications

https://www.bakerlab.org/wp-content/uploads/2021/02/Klima_etal_NatComm2021_Sensing_modalities_in_fluorescent_proteins.pdf, Download PDF},

doi = {10.1038/s41467-020-18911-w},

year  = {2021},

date = {2021-02-08},

urldate = {2021-02-08},

journal = {Nature Communications},

volume = {856},

number = {12},

pages = {2041–1723},

abstract = {Through the efforts of many groups, a wide range of fluorescent protein reporters and sensors based on green fluorescent protein and its relatives have been engineered in recent years. Here we explore the incorporation of sensing modalities into de novo designed fluorescence-activating proteins, called mini-fluorescence-activating proteins (mFAPs), that bind and stabilize the fluorescent cis-planar state of the fluorogenic compound DFHBI. We show through further design that the fluorescence intensity and specificity of mFAPs for different chromophores can be tuned, and the fluorescence made sensitive to pH and Ca2+ for real-time fluorescence reporting. Bipartite split mFAPs enable real-time monitoring of protein–protein association and (unlike widely used split GFP reporter systems) are fully reversible, allowing direct readout of association and dissociation events. The relative ease with which sensing modalities can be incorporated and advantages in smaller size and photostability make de novo designed fluorescence-activating proteins attractive candidates for optical sensor engineering.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Quijano-Rubio2021,

title = {De novo design of modular and tunable protein biosensors},

author = {Quijano-Rubio, Alfredo

and Yeh, Hsien-Wei

and Park, Jooyoung

and Lee, Hansol

and Langan, Robert A.

and Boyken, Scott E.

and Lajoie, Marc J.

and Cao, Longxing

and Chow, Cameron M.

and Miranda, Marcos C.

and Wi, Jimin

and Hong, Hyo Jeong

and Stewart, Lance

and Oh, Byung-Ha

and Baker, David},

url = {https://www.nature.com/articles/s41586-021-03258-z, Nature

https://www.bakerlab.org/wp-content/uploads/2021/02/Rubio_et_al_Nature_COVID_LOCKR_sensors.pdf, Download PDF},

doi = {10.1038/s41586-021-03258-z},

year  = {2021},

date = {2021-01-27},

urldate = {2021-01-27},

journal = {Nature},

abstract = {Naturally occurring protein switches have been repurposed for developing novel biosensors and reporters for cellular and clinical applications1, but the number of such switches is limited, and engineering them is often challenging as each is different. Here, we show that a very general class of protein-based biosensors can be created by inverting the flow of information through de novo designed protein switches in which binding of a peptide key triggers biological outputs of interest2. The designed sensors are modular molecular devices with a closed dark state and an open luminescent state; binding of the analyte of interest drives switching from the closed to the open state. Because the sensor is based purely on thermodynamic coupling of analyte binding to sensor activation, only one target binding domain is required, which simplifies sensor design and allows direct readout in solution. We demonstrate the modularity of this platform by creating biosensors that, with little optimization, sensitively detect the anti-apoptosis protein Bcl-2, the IgG1 Fc domain, the Her2 receptor, and Botulinum neurotoxin B, as well as biosensors for cardiac Troponin I and an anti-Hepatitis B virus (HBV) antibody that achieve the sub-nanomolar sensitivity necessary to detect clinically relevant concentrations of these molecules. Given the current need for diagnostic tools for tracking COVID-193, we used the approach to design sensors of antibodies against SARS-CoV-2 protein epitopes and of the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein. The latter, which incorporates a de novo designed RBD binder4, has a limit of detection of 15 pM and a signal over background of over 50-fold. The modularity and sensitivity of the platform should enable the rapid construction of sensors for a wide range of analytes and highlights the power of de novo protein design to create multi-state protein systems with new and useful functions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ben-Sasson2020,

title = {Design of biologically active binary protein 2D materials},

author = {Ben-Sasson, Ariel J. and Watson, Joseph L. and Sheffler, William and Johnson, Matthew Camp and Bittleston, Alice and Somasundaram, Logeshwaran and Decarreau, Justin and Jiao, Fang and Chen, Jiajun and Mela, Ioanna and Drabek, Andrew A. and Jarrett, Sanchez M. and Blacklow, Stephen C. and Kaminski, Clemens F. and Hura, Greg L. and De Yoreo, James J. and Kollman, Justin M. and Ruohola-Baker, Hannele and Derivery, Emmanuel and Baker, David},

url = {https://www.nature.com/articles/s41586-020-03120-8, Nature

https://www.bakerlab.org/wp-content/uploads/2021/02/Ben-Sasson_Nature2021_Binary_2D_arrays.pdf, Download PDF},

doi = {10.1038/s41586-020-03120-8},

year  = {2021},

date = {2021-01-06},

urldate = {2021-01-06},

journal = {Nature},

abstract = {Ordered two-dimensional arrays such as S-layers1,2 and designed analogues3–5 have intrigued bioengineers6,7, but with the exception of a single lattice formed with flexible linkers8, they are constituted from just one protein component. Materials composed of two components have considerable potential advantages for modulating assembly dynamics and incorporating more complex functionality9–12. Here we describe a computational method to generate co-assembling binary layers by designing rigid interfaces between pairs of dihedral protein building blocks, and use it to design a p6m lattice. The designed array components are soluble at millimolar concentrations, but when combined at nanomolar concentrations, they rapidly assemble into nearly crystalline micrometre-scale arrays nearly identical to the computational design model in vitro and in cells without the need for a two-dimensional support. Because the material is designed from the ground up, the components can be readily functionalized and their symmetry reconfigured, enabling formation of ligand arrays with distinguishable surfaces, which we demonstrate can drive extensive receptor clustering, downstream protein recruitment and signalling. Using atomic force microscopy on supported bilayers and quantitative microscopy on living cells, we show that arrays assembled on membranes have component stoichiometry and structure similar to arrays formed in vitro, and that our material can therefore impose order onto fundamentally disordered substrates such as cell membranes. In contrast to previously characterized cell surface receptor binding assemblies such as antibodies and nanocages, which are rapidly endocytosed, we find that large arrays assembled at the cell surface suppress endocytosis in a tunable manner, with potential therapeutic relevance for extending receptor engagement and immune evasion. Our work provides a foundation for a synthetic cell biology in which multi-protein macroscale materials are designed to modulate cell responses and reshape synthetic and living systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Crawshaw2021,

title = {Engineering an efficient and enantioselective enzyme for the Morita-Baylis-Hillman reaction},

author = {Crawshaw, Rebecca

and Crossley, Amy E.

and Johannissen, Linus

and Burke, Ashleigh J.

and Hay, Sam

and Levy, Colin

and Baker, David

and Lovelock, Sarah L.

and Green, Anthony P.},

url = {https://www.nature.com/articles/s41557-021-00833-9

https://www.bakerlab.org/wp-content/uploads/2022/01/Crawshaw_etal_NatChem_Engineering_enantioselective_enzyme_Morita-Baylis-Hillman_reaction.pdf},

doi = {10.1038/s41557-021-00833-9},

year  = {2021},

date = {2021-12-16},

journal = {Nature Chemistry},

abstract = {The combination of computational design and directed evolution could offer a general strategy to create enzymes with new functions. So far, this approach has delivered enzymes for a handful of model reactions. Here we show that new catalytic mechanisms can be engineered into proteins to accelerate more challenging chemical transformations. Evolutionary optimization of a primitive design afforded an efficient and enantioselective enzyme (BH32.14) for the Morita–Baylis–Hillman (MBH) reaction. BH32.14 is suitable for preparative-scale transformations, accepts a broad range of aldehyde and enone coupling partners and is able to promote selective monofunctionalizations of dialdehydes. Crystallographic, biochemical and computational studies reveal that BH32.14 operates via a sophisticated catalytic mechanism comprising a His23 nucleophile paired with a judiciously positioned Arg124. This catalytic arginine shuttles between conformational states to stabilize multiple oxyanion intermediates and serves as a genetically encoded surrogate of privileged bidentate hydrogen-bonding catalysts (for example, thioureas). This study demonstrates that elaborate catalytic devices can be built from scratch to promote demanding multi-step processes not observed in nature.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Friedlander2021,

title = {Treatment of experimental anthrax with pegylated circularly permuted capsule depolymerase},

author = {Patricia M. Legler

and Stephen F. Little

and Jeffrey Senft

and Rowena Schokman

and John H. Carra

and Jaimee R. Compton

and Donald Chabot

and Steven Tobery

and David P. Fetterer

and Justin B. Siegel

and David Baker

and Arthur M. Friedlander},

url = {https://www.science.org/doi/10.1126/scitranslmed.abh1682

https://www.bakerlab.org/wp-content/uploads/2022/01/Legler_etal_ScienceTransMed2021_Treatment_of_anthrax_by_capsule_depolymerase.pdf},

doi = {10.1126/scitranslmed.abh1682},

year  = {2021},

date = {2021-12-08},

journal = {Science Translational Medicine},

abstract = {Anthrax is considered one of the most dangerous bioweapon agents, and concern about multidrug-resistant strains has led to the development of alternative therapeutic approaches that target the antiphagocytic capsule, an essential virulence determinant of Bacillus anthracis, the causative agent. Capsule depolymerase is a γ-glutamyltransferase that anchors the capsule to the cell wall of B. anthracis. Encapsulated strains of B. anthracis can be treated with recombinant capsule depolymerase to enzymatically remove the capsule and promote phagocytosis and killing by human neutrophils. Here, we show that pegylation improved the pharmacokinetic and therapeutic properties of a previously described variant of capsule depolymerase, CapD-CP, when delivered 24 hours after exposure every 8 hours for 2 days for the treatment of mice infected with B. anthracis. Mice infected with 382 LD50 of B. anthracis spores from a nontoxigenic encapsulated strain were completely protected (10 of 10) after treatment with the pegylated PEG-CapD-CPS334C, whereas 10% of control mice (1 of 10) survived with control treatment using bovine serum albumin (P < 0.0001, log-rank analysis). Treatment of mice infected with five LD50 of a fully virulent toxigenic, encapsulated B. anthracis strain with PEG-CapD-CPS334C protected 80% (8 of 10) of the animals, whereas 20% of controls (2 of 10) survived (P = 0.0125, log-rank analysis). This strategy renders B. anthracis susceptible to innate immune responses and does not rely on antibiotics. These findings suggest that enzyme-catalyzed removal of the capsule may be a potential therapeutic strategy for the treatment of multidrug- or vaccine-resistant anthrax and other bacterial infections.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Du2021,

title = {The trRosetta server for fast and accurate protein structure prediction},

author = {Du, Zongyang

and Su, Hong

and Wang, Wenkai

and Ye, Lisha

and Wei, Hong

and Peng, Zhenling

and Anishchenko, Ivan

and Baker, David

and Yang, Jianyi},

url = {https://www.nature.com/articles/s41596-021-00628-9

https://www.bakerlab.org/wp-content/uploads/2022/01/Du_etal_NatProt2021_trRosetta_server.pdf},

doi = {10.1038/s41596-021-00628-9},

year  = {2021},

date = {2021-12-01},

urldate = {2021-12-01},

journal = {Nature Protocols},

abstract = {The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and accurate protein structure prediction, powered by deep learning and Rosetta. With the input of a protein’s amino acid sequence, a deep neural network is first used to predict the inter-residue geometries, including distance and orientations. The predicted geometries are then transformed as restraints to guide the structure prediction on the basis of direct energy minimization, which is implemented under the framework of Rosetta. The trRosetta server distinguishes itself from other similar structure prediction servers in terms of rapid and accurate de novo structure prediction. As an illustration, trRosetta was applied to two Pfam families with unknown structures, for which the predicted de novo models were estimated to have high accuracy. Nevertheless, to take advantage of homology modeling, homologous templates are used as additional inputs to the network automatically. In general, it takes ~1 h to predict the final structure for a typical protein with ~300 amino acids, using a maximum of 10 CPU cores in parallel in our cluster system. To enable large-scale structure modeling, a downloadable package of trRosetta with open-source codes is available as well. A detailed guidance for using the package is also available in this protocol. The server and the package are available at https://yanglab.nankai.edu.cn/trRosetta/ and https://yanglab.nankai.edu.cn/trRosetta/download/, respectively.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yaman2021,

title = {Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis},

author = {Muammer Y Yaman and Kathryn N Guye and Maxim Ziatdinov and Hao Shen and David Baker and Sergei V Kalinin and David S Ginger

},

url = {https://pubmed.ncbi.nlm.nih.gov/34128040/

https://www.bakerlab.org/wp-content/uploads/2021/06/Muammer_etal_SoftMatter2021_Aisngment_along_nanofibers.pdf},

doi = {10.1039/d1sm00645b},

year  = {2021},

date = {2021-06-15},

journal = {Soft Matter},

abstract = {In this study, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates. Using machine learning and automated image processing, we analyze scanning electron microscopy (SEM) images to study how the attachment density and alignment fidelity are influenced by variables such as the aspect ratio of the Au nanorods, and the salt concentration of the solution. We find that the Au nanorods prefer to align parallel to the protein nanofibers. This preference decreases with increasing salt concentration, but is only weakly sensitive to the nanorod aspect ratio. While the overall specific Au nanorod attachment density to the protein fibers increases with increasing solution ionic strength, this increase is dominated primarily by non-specific binding to the substrate background, and we find that greater specific attachment (nanorods attached to the nanofiber template as compared to the substrates) occurs at the lower studied salt concentrations, with the maximum ratio of specific to non-specific binding occurring when the protein fiber solutions are prepared in 75 mM NaCl concentration.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Boyoglu-Barnum2021,

title = {Quadrivalent influenza nanoparticle vaccines induce broad protection},

author = {Boyoglu-Barnum, Seyhan

and Ellis, Daniel

and Gillespie, Rebecca A.

and Hutchinson, Geoffrey B.

and Park, Young-Jun

and Moin, Syed M.

and Acton, Oliver J.

and Ravichandran, Rashmi

and Murphy, Mike

and Pettie, Deleah

and Matheson, Nick

and Carter, Lauren

and Creanga, Adrian

and Watson, Michael J.

and Kephart, Sally

and Ataca, Sila

and Vaile, John R.

and Ueda, George

and Crank, Michelle C.

and Stewart, Lance

and Lee, Kelly K.

and Guttman, Miklos

and Baker, David

and Mascola, John R.

and Veesler, David

and Graham, Barney S.

and King, Neil P.

and Kanekiyo, Masaru},

url = {https://www.nature.com/articles/s41586-021-03365-x

https://www.bakerlab.org/wp-content/uploads/2021/04/Nature2021_NanoparticleFluVaccine.pdf},

doi = {10.1038/s41586-021-03365-x},

year  = {2021},

date = {2021-03-24},

journal = {Nature},

abstract = {Influenza vaccines that confer broad and durable protection against diverse viral strains would have a major effect on global health, as they would lessen the need for annual vaccine reformulation and immunization. Here we show that computationally designed, two-component nanoparticle immunogens induce potently neutralizing and broadly protective antibody responses against a wide variety of influenza viruses. The nanoparticle immunogens contain 20 haemagglutinin glycoprotein trimers in an ordered array, and their assembly in vitro enables the precisely controlled co-display of multiple distinct haemagglutinin proteins in defined ratios. Nanoparticle immunogens that co-display the four haemagglutinins of licensed quadrivalent influenza vaccines elicited antibody responses in several animal models against vaccine-matched strains that were equivalent to or better than commercial quadrivalent influenza vaccines, and simultaneously induced broadly protective antibody responses to heterologous viruses by targeting the subdominant yet conserved haemagglutinin stem. The combination of potent receptor-blocking and cross-reactive stem-directed antibodies induced by the nanoparticle immunogens makes them attractive candidates for a supraseasonal influenza vaccine candidate with the potential to replace conventional seasonal vaccines.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hiranuma2021,

title = {Improved protein structure refinement guided by deep learning based accuracy estimation},

author = {Naozumi Hiranuma and Hahnbeom Park and Minkyung Baek and Ivan Anishchenko and Justas Dauparas and David Baker 

},

url = {https://www.nature.com/articles/s41467-021-21511-x, Nature Communications

https://www.bakerlab.org/wp-content/uploads/2021/02/Hiranuma_etal_NatureComms2021_DeepLearningStructureRefinement.pdf, Download PDF},

doi = {10.1038/s41467-021-21511-x},

year  = {2021},

date = {2021-02-26},

urldate = {2021-02-26},

journal = {Nature Communications},

volume = {12},

number = {1340},

abstract = {We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Park2021,

title = {Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein–Ligand Docking},

author = {Hahnbeom Park and Guangfeng Zhou and Minkyung Baek and David Baker and Frank DiMaio},

url = {https://pubs.acs.org/doi/full/10.1021/acs.jctc.0c01184

https://www.bakerlab.org/wp-content/uploads/2021/02/Park_etal_JCTC2021_Small_mol_force_field_optimization.pdf},

doi = {10.1021/acs.jctc.0c01184},

year  = {2021},

date = {2021-02-12},

journal = {Journal of Chemical Theory and Computation},

abstract = {Accurate and rapid calculation of protein-small molecule interaction free energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here, we describe a new approach in which small molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized, so native crystal lattice arrangements had the lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid-based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1112 complexes was improved by more than 10% over previously published methods, with solutions within <1 Å in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for guiding molecular force field development, and the improved Rosetta energy function should increase accuracy in a wide range of small molecule structure prediction and design studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ziatdinov2021,

title = {Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data},

author = {Ziatdinov, Maxim

and Zhang, Shuai

and Dollar, Orion

and Pfaendtner, Jim

and Mundy, Christopher J.

and Li, Xin

and Pyles, Harley

and Baker, David

and De Yoreo, James J.

and Kalinin, Sergei V.},

url = {https://pubs.acs.org/doi/10.1021/acs.nanolett.0c03447},

doi = {10.1021/acs.nanolett.0c03447},

year  = {2021},

date = {2021-01-13},

journal = {Nano Letters},

abstract = {The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mulligan2020,

title = {Computational design of mixed chirality peptide macrocycles with internal symmetry},

author = {Vikram Khipple Mulligan and Christine S. Kang and Michael R. Sawaya and Stephen Rettie and Xinting Li and Inna Antselovich and Timothy W. Craven and Andrew M. Watkins and Jason W. Labonte and Frank DiMaio and Todd O. Yeates and David Baker},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1002/pro.3974

https://www.bakerlab.org/wp-content/uploads/2020/10/Mulligan2020-Computational-design-of-mixed-chirality-peptide-macrocycles-with-internal-symmetry.pdf},

doi = {10.1002/pro.3974},

year  = {2020},

date = {2020-10-15},

journal = {Protein Science},

abstract = {Cyclic symmetry is frequent in protein and peptide homo‐oligomers, but extremely rare within a single chain, as it is not compatible with free N‐ and C‐termini. Here we describe the computational design of mixed‐chirality peptide macrocycles with rigid structures that feature internal cyclic symmetries or improper rotational symmetries inaccessible to natural proteins. Crystal structures of three C2‐ and C3‐symmetric macrocycles, and of six diverse S2‐symmetric macrocycles, match the computationally‐designed models with backbone heavy‐atom RMSD values of 1 å or better. Crystal structures of an S4‐symmetric macrocycle (consisting of a sequence and structure segment mirrored at each of three successive repeats) designed to bind zinc reveal a large‐scale zinc‐driven conformational change from an S4‐symmetric apo‐state to a nearly inverted S4‐symmetric holo‐state almost identical to the design model. This work demonstrates the power of computational design for exploring symmetries and structures not found in nature, and for creating synthetic switchable systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cao2020,

title = {De novo design of picomolar SARS-CoV-2 miniprotein inhibitors},

author = {Cao, Longxing and Goreshnik, Inna and Coventry, Brian and Case, James Brett and Miller, Lauren and Kozodoy, Lisa and Chen, Rita E. and Carter, Lauren and Walls, Alexandra C. and Park, Young-Jun and Strauch, Eva-Maria and Stewart, Lance and Diamond, Michael S. and Veesler, David and Baker, David},

url = {https://science.sciencemag.org/content/early/2020/09/08/science.abd9909

https://www.bakerlab.org/wp-content/uploads/2020/09/Cao_etal_Science_COVID_spike_binders.pdf},

doi = {10.1126/science.abd9909},

year  = {2020},

date = {2020-09-09},

journal = {Science},

abstract = {Targeting the interaction between the SARS-CoV-2 Spike protein and the human ACE2 receptor is a promising therapeutic strategy. We designed inhibitors using two de novo design approaches. Computer generated scaffolds were either built around an ACE2 helix that interacts with the Spike receptor binding domain (RBD), or docked against the RBD to identify new binding modes, and their amino acid sequences designed to optimize target binding, folding and stability. Ten designs bound the RBD with affinities ranging from 100pM to 10nM, and blocked ARS-CoV-2 infection of Vero E6 cells with IC 50 values between 24 pM and 35 nM; The most potent, with new binding modes, are 56 and 64 residue proteins (IC 50 ~ 0.16 ng/ml). Cryo-electron microscopy structures of these minibinders in complex with the SARS-CoV-2 spike ectodomain trimer with all three RBDs bound are nearly identical to the computational models. These hyperstable minibinders provide starting points for SARS-CoV-2 therapeutics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Xu2020,

title = {Computational design of transmembrane pores},

author = {Chunfu Xu and Peilong Lu and Tamer M. Gamal El-Din and Xue Y. Pei and Matthew C. Johnson and Atsuko Uyeda and Matthew J. Bick and Qi Xu and Daohua Jiang and Hua Bai and Gabriella Reggiano and Yang Hsia and T J Brunette and Jiayi Dou and Dan Ma and Eric M. Lynch and Scott E. Boyken and Po-Ssu Huang and Lance Stewart and Frank DiMaio and Justin M. Kollman and Ben F. Luisi and Tomoaki Matsuura and William A. Catterall and David Baker },

url = {https://www.bakerlab.org/wp-content/uploads/2020/08/Xuetal_Nature2020_DeNovoPores.pdf

https://www.nature.com/articles/s41586-020-2646-5},

doi = {10.1038/s41586-020-2646-5},

year  = {2020},

date = {2020-08-26},

journal = {Nature},

volume = {585},

pages = {129–134},

abstract = {Transmembrane channels and pores have key roles in fundamental biological processes and in biotechnological applications such as DNA nanopore sequencing, resulting in considerable interest in the design of pore-containing proteins. Synthetic amphiphilic peptides have been found to form ion channels, and there have been recent advances in de novo membrane protein design and in redesigning naturally occurring channel-containing proteins. However, the de novo design of stable, well-defined transmembrane protein pores that are capable of conducting ions selectively or are large enough to enable the passage of small-molecule fluorophores remains an outstanding challenge. Here we report the computational design of protein pores formed by two concentric rings of α-helices that are stable and monodisperse in both their water-soluble and their transmembrane forms. Crystal structures of the water-soluble forms of a 12-helical pore and a 16-helical pore closely match the computational design models. Patch-clamp electrophysiology experiments show that, when expressed in insect cells, the transmembrane form of the 12-helix pore enables the passage of ions across the membrane with high selectivity for potassium over sodium; ion passage is blocked by specific chemical modification at the pore entrance. When incorporated into liposomes using in vitro protein synthesis, the transmembrane form of the 16-helix pore—but not the 12-helix pore—enables the passage of biotinylated Alexa Fluor 488. A cryo-electron microscopy structure of the 16-helix transmembrane pore closely matches the design model. The ability to produce structurally and functionally well-defined transmembrane pores opens the door to the creation of designer channels and pores for a wide variety of applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lajoie2020,

title = {Designed protein logic to target cells with precise combinations of surface antigens },

author = {Lajoie, Marc J. and

Boyken, Scott E. and

Salter, Alexander I. and

Bruffey, Jilliane and

Rajan, Anusha and

Langan, Robert A. and

Olshefsky, Audrey and

Muhunthan, Vishaka and

Bick, Matthew J. and

Gewe, Mesfin and

Quijano-Rubio, Alfredo and

Johnson, JayLee and

Lenz, Garreck and

Nguyen, Alisha and

Pun, Suzie and

Correnti, Colin E. and

Riddell, Stanley R. and

Baker, David},

url = {https://science.sciencemag.org/content/early/2020/08/19/science.aba6527

https://www.bakerlab.org/wp-content/uploads/2020/08/Lajoie-coLOCKR2020.pdf},

doi = {10.1126/science.aba6527},

year  = {2020},

date = {2020-08-20},

journal = {Science},

abstract = {Precise cell targeting is challenging because most mammalian cell types lack a single surface marker that distinguishes them from other cells. A solution would be to target cells based on specific combinations of proteins present on their surfaces. We design colocalization-dependent protein switches (Co-LOCKR) that perform AND, OR, and NOT Boolean logic operations. These switches activate through a conformational change only when all conditions are met, generating rapid, transcription-independent responses at single-cell resolution within complex cell populations. We implement AND gates to redirect T cell specificity against tumor cells expressing two surface antigens while avoiding off-target recognition of single-antigen cells, and 3-input switches that add NOT or OR logic to avoid or include cells expressing a third antigen. Thus, de novo designed proteins can perform computations on the surface of cells, integrating multiple distinct binding interactions into a single output.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Basanta2020,

title = {An enumerative algorithm for de novo design of proteins with diverse pocket structures},

author = {Basanta, Benjamin and Bick, Matthew J. and Bera, Asim K. and Norn, Christoffer and Chow, Cameron M. and Carter, Lauren P. and Goreshnik, Inna and Dimaio, Frank and Baker, David},

url = {https://www.pnas.org/content/117/36/22135

https://www.bakerlab.org/wp-content/uploads/2020/12/Basanta_etal_2020_PNAS_enumerative-algorithm-for-de-novo-design-of-proteins-with-diverse-pocket-structures.pdf},

doi = {10.1073/pnas.2005412117},

isbn = {0027-8424},

year  = {2020},

date = {2020-08-11},

journal = {Proceedings of the National Academy of Sciences},

volume = {117},

number = {36},

pages = {22135–22145},

abstract = {Reengineering naturally occurring proteins to have new functions has had considerable impact on industrial and biomedical applications, but is limited by the finite number of known proteins. A promise of de novo protein design is to generate a larger and more diverse set of protein structures than is currently available. This vision has not yet been realized for small-molecule binder or enzyme design due to the complexity of pocket-containing structures. Here we present an algorithm that systematically generates NTF2-like protein structures with diverse pocket geometries. The scaffold sets, the insights gained from detailed structural characterization, and the computational method for generating unlimited numbers of structures should contribute to a new generation of de novo small-molecule binding proteins and catalysts.To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ueda2020,

title = {Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens},

author = {Ueda, George and Antanasijevic, Aleksandar and Fallas, Jorge A and Sheffler, William and Copps, Jeffrey and Ellis, Daniel and Hutchinson, Geoffrey B and Moyer, Adam and Yasmeen, Anila and Tsybovsky, Yaroslav and Park, Young-Jun and Bick, Matthew J and Sankaran, Banumathi and Gillespie, Rebecca A and Brouwer, Philip JM and Zwart, Peter H and Veesler, David and Kanekiyo, Masaru and Graham, Barney S and Sanders, Rogier W and Moore, John P and Klasse, Per Johan and Ward, Andrew B and King, Neil P and Baker, David},

url = {https://elifesciences.org/articles/57659},

doi = {10.7554/eLife.57659},

year  = {2020},

date = {2020-08-04},

journal = {eLife},

volume = {9},

pages = {e57659},

abstract = {Multivalent presentation of viral glycoproteins can substantially increase the elicitation of antigen-specific antibodies. To enable a new generation of anti-viral vaccines, we designed self-assembling protein nanoparticles with geometries tailored to present the ectodomains of influenza, HIV, and RSV viral glycoprotein trimers. We first textit{de novo} designed trimers tailored for antigen fusion, featuring N-terminal helices positioned to match the C termini of the viral glycoproteins. Trimers that experimentally adopted their designed configurations were incorporated as components of tetrahedral, octahedral, and icosahedral nanoparticles, which were characterized by cryo-electron microscopy and assessed for their ability to present viral glycoproteins. Electron microscopy and antibody binding experiments demonstrated that the designed nanoparticles presented antigenically intact prefusion HIV-1 Env, influenza hemagglutinin, and RSV F trimers in the predicted geometries. This work demonstrates that antigen-displaying protein nanoparticles can be designed from scratch, and provides a systematic way to investigate the influence of antigen presentation geometry on the immune response to vaccination.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brunette2020,

title = {Modular repeat protein sculpting using rigid helical junctions},

author = {Brunette, TJ and Bick, Matthew J. and Hansen, Jesse M. and Chow, Cameron M. and Kollman, Justin M. and Baker, David},

url = {https://www.bakerlab.org/wp-content/uploads/2020/04/Brunette2020_Junctions.pdf

https://www.pnas.org/content/early/2020/04/02/1908768117},

doi = {10.1073/pnas.1908768117},

year  = {2020},

date = {2020-04-02},

journal = {Proceedings of the National Academy of Sciences},

abstract = {The ability to precisely design large proteins with diverse shapes would enable applications ranging from the design of protein binders that wrap around their target to the positioning of multiple functional sites in specified orientations. We describe a protein backbone design method for generating a wide range of rigid fusions between helix-containing proteins and use it to design 75,000 structurally unique junctions between monomeric and homo-oligomeric de novo designed and ankyrin repeat proteins (RPs). Of the junction designs that were experimentally characterized, 82% have circular dichroism and solution small-angle X-ray scattering profiles consistent with the design models and are stable at 95 °C. Crystal structures of four designed junctions were in close agreement with the design models with rmsds ranging from 0.9 to 1.6 Å. Electron microscopic images of extended tetrameric structures and ∼10-nm-diameter “L” and “V” shapes generated using the junctions are close to the design models, demonstrating the control the rigid junctions provide for protein shape sculpting over multiple nanometer length scales.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wei2020,

title = {Computational design of closely related proteins that adopt two well-defined but structurally divergent folds},

author = {Wei, Kathy Y. and Moschidi, Danai and Bick, Matthew J. and Nerli, Santrupti and McShan, Andrew C. and Carter, Lauren P. and Huang, Po-Ssu and Fletcher, Daniel A. and Sgourakis, Nikolaos G. and Boyken, Scott E. and Baker, David

},

url = {https://www.pnas.org/content/early/2020/03/17/1914808117

https://www.ipd.uw.edu/wp-content/uploads/2020/03/Wei_PNAS_2020.pdf},

doi = {10.1073/pnas.1914808117},

year  = {2020},

date = {2020-03-17},

journal = {Proceedings of the National Academy of Sciences},

abstract = {Computational protein design has focused primarily on the design of sequences which fold to single stable states, but in biology many proteins adopt multiple states. We used de novo protein design to generate very closely related proteins that adopt two very different states—a short state and a long state, like a viral fusion protein—and then created a single molecule that can be found in both forms. Our proteins, poised between forms, are a starting point for the design of triggered shape changes.The plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used for de novo design of proteins that fold to a single state with a deep free-energy minimum, and to reengineer natural proteins to alter their dynamics or fold, the de novo design of closely related sequences which adopt well-defined but structurally divergent structures remains an outstanding challenge. We designed closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations — one short (~66 Å height) and the other long (~100 Å height) — reminiscent of the conformational transition of viral fusion proteins. Crystallographic and NMR spectroscopic characterization shows that both the short- and long-state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large-scale conformational switches between structurally divergent forms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2020,

title = {De novo design of protein logic gates},

author = {Chen, Zibo and Kibler, Ryan D. and Hunt, Andrew and Busch, Florian and Pearl, Jocelynn and Jia, Mengxuan and VanAernum, Zachary L. and Wicky, Basile I. M. and Dods, Galen and Liao, Hanna and Wilken, Matthew S. and Ciarlo, Christie and Green, Shon and El-Samad, Hana and Stamatoyannopoulos, John and Wysocki, Vicki H. and Jewett, Michael C. and Boyken, Scott E. and Baker, David},

url = {https://science.sciencemag.org/content/368/6486/78

https://www.bakerlab.org/wp-content/uploads/2020/04/Chen2020_DeNovoProteinLogicGates.pdf},

doi = {10.1126/science.aay2790},

year  = {2020},

date = {2020-03-04},

journal = {Science},

volume = {368},

number = {6486},

pages = {78-84},

abstract = {The design of modular protein logic for regulating protein function at the posttranscriptional level is a challenge for synthetic biology. Here, we describe the design of two-input AND, OR, NAND, NOR, XNOR, and NOT gates built from de novo–designed proteins. These gates regulate the association of arbitrary protein units ranging from split enzymes to transcriptional machinery in vitro, in yeast and in primary human T cells, where they control the expression of the TIM3 gene related to T cell exhaustion. Designed binding interaction cooperativity, confirmed by native mass spectrometry, makes the gates largely insensitive to stoichiometric imbalances in the inputs, and the modularity of the approach enables ready extension to three-input OR, AND, and disjunctive normal form gates. The modularity and cooperativity of the control elements, coupled with the ability to de novo design an essentially unlimited number of protein components, should enable the design of sophisticated posttranslational control logic over a wide range of biological functions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2020,

title = {Improved protein structure prediction using predicted interresidue orientations},

author = {Yang, Jianyi and Anishchenko, Ivan and Park, Hahnbeom and Peng, Zhenling and Ovchinnikov, Sergey and Baker, David},

url = {https://www.pnas.org/content/early/2020/01/01/1914677117

https://www.bakerlab.org/wp-content/uploads/2020/01/Yang2020_ImprovedStructurePredictionInterresidueOrientations.pdf

},

doi = {10.1073/pnas.1914677117},

isbn = {0027-8424},

year  = {2020},

date = {2020-01-02},

journal = {Proceedings of the National Academy of Sciences},

abstract = {Protein structure prediction is a longstanding challenge in computational biology. Through extension of deep learning-based prediction to interresidue orientations in addition to distances, and the development of a constrained optimization by Rosetta, we show that more accurate models can be generated. Results on a set of 18 de novo-designed proteins suggests the proposed method should be directly applicable to current challenges in de novo protein design.The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Caldwell2020,

title = {Tight and specific lanthanide binding in a de novo TIM barrel with a large internal cavity designed by symmetric domain fusion},

author = {Caldwell, Shane J. and Haydon, Ian C. and Piperidou, Nikoletta and Huang, Po-Ssu and Bick, Matthew J. and Sjöström, H. Sebastian and Hilvert, Donald and Baker, David and Zeymer, Cathleen

},

url = {https://www.bakerlab.org/wp-content/uploads/2020/11/Caldwell_et_al_PNAS_TIM_barrel_metal_binding.pdf

https://www.pnas.org/content/early/2020/11/13/2008535117},

doi = {10.1073/pnas.2008535117},

year  = {2020},

date = {2020-11-17},

journal = {Proceedings of the National Academy of Sciences},

abstract = {De novo protein design has succeeded in generating a large variety of globular proteins, but the construction of protein scaffolds with cavities that could accommodate large signaling molecules, cofactors, and substrates remains an outstanding challenge. The long, often flexible loops that form such cavities in many natural proteins are difficult to precisely program and thus challenging for computational protein design. Here we describe an alternative approach to this problem. We fused two stable proteins with C2 symmetry—a de novo designed dimeric ferredoxin fold and a de novo designed TIM barrel—such that their symmetry axes are aligned to create scaffolds with large cavities that can serve as binding pockets or enzymatic reaction chambers. The crystal structures of two such designs confirm the presence of a 420 cubic Ångström chamber defined by the top of the designed TIM barrel and the bottom of the ferredoxin dimer. We functionalized the scaffold by installing a metal-binding site consisting of four glutamate residues close to the symmetry axis. The protein binds lanthanide ions with very high affinity as demonstrated by tryptophan-enhanced terbium luminescence. This approach can be extended to other metals and cofactors, making this scaffold a modular platform for the design of binding proteins and biocatalysts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kirkpatrick2020,

title = {Conditional Recruitment to a DNA-Bound CRISPR–Cas Complex Using a Colocalization-Dependent Protein Switch},

author = {Robin L. Kirkpatrick and Kieran Lewis and Robert A. Langan and Marc J. Lajoie and Scott E. Boyken and Madeleine Eakman and David Baker and Jesse G. Zalatan},

url = {https://pubs.acs.org/doi/full/10.1021/acssynbio.0c00012

https://www.bakerlab.org/wp-content/uploads/2020/08/Kirkpatrick2020-LOCKR-CRISPR.pdf},

doi = {10.1021/acssynbio.0c00012},

year  = {2020},

date = {2020-08-20},

journal = {ACS Synthetic Biology},

abstract = {To spatially control biochemical functions at specific sites within a genome, we have engineered a synthetic switch that activates when bound to its DNA target site. The system uses two CRISPR–Cas complexes to colocalize components of a de novo-designed protein switch (Co-LOCKR) to adjacent sites in the genome. Colocalization triggers a conformational change in the switch from an inactive closed state to an active open state with an exposed functional peptide. We prototype the system in yeast and demonstrate that DNA binding triggers activation of the switch, recruitment of a transcription factor, and expression of a downstream reporter gene. This DNA-triggered Co-LOCKR switch provides a platform to engineer sophisticated functions that should only be executed at a specific target site within the genome, with potential applications in a wide range of synthetic systems including epigenetic regulation, imaging, and genetic logic circuits.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Park2019b,

title = {De novo design of a homo-trimeric amantadine-binding protein},

author = {Park, Jooyoung and Selvaraj, Brinda and McShan, Andrew C and Boyken, Scott E and Wei, Kathy Y and Oberdorfer, Gustav and DeGrado, William and Sgourakis, Nikolaos G and Cuneo, Matthew J and Myles, Dean AA and Baker, David

},

editor = {Wolberger, Cynthia and Fleishman, Sarel Jacob and Anderson, Ross},

url = {https://elifesciences.org/articles/47839.pdf},

doi = {10.7554/eLife.47839},

year  = {2019},

date = {2019-12-19},

journal = {eLife},

abstract = {The computational design of a symmetric protein homo-oligomer that binds a symmetry-matched small molecule larger than a metal ion has not yet been achieved. We used de novo protein design to create a homo-trimeric protein that binds the Ctextsubscript{3} symmetric small molecule drug amantadine with each protein monomer making identical interactions with each face of the small molecule. Solution NMR data show that the protein has regular three-fold symmetry and undergoes localized structural changes upon ligand binding. A high-resolution X-ray structure reveals a close overall match to the design model with the exception of water molecules in the amantadine binding site not included in the Rosetta design calculations, and a neutron structure provides experimental validation of the computationally designed hydrogen-bond networks. Exploration of approaches to generate a small molecule inducible homo-trimerization system based on the design highlight challenges that must be overcome to computationally design such systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Weitzner2019,

title = {A computational method for design of connected catalytic networks in proteins},

author = {Brian D. Weitzner, Yakov Kipnis, A. Gerard Daniel, Donald Hilvert, David Baker},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.3757

https://www.bakerlab.org/wp-content/uploads/2020/02/Weitzner_et_al-2019-Protein_Science-1.pdf},

doi = {DOI10.1002/pro .3757},

year  = {2019},

date = {2019-10-23},

journal = {Protein Science},

abstract = {Computational design of new active sites has generally proceeded by geometrically defining interactions between the reaction transition state(s) and surrounding side-chain functional groups which maximize transition-state stabilization, and then searching for sites in protein scaffolds where the specified side-chain–transition-state interactions can be realized. A limitation of this approach is that the interactions between the side chains themselves are not constrained. An extensive connected hydrogen bond network involving the catalytic residues was observed in a designed retroaldolase following directed evolution. Such connected networks could increase catalytic activity by preorganizing active site residues in catalytically competent orientations, and enabling concerted interactions between side chains during catalysis, for example proton shuffling. We developed a method for designing active sites in which the catalytic side chains, in addition to making interactions with the transition state, are also involved in extensive hydrogen bond networks. Because of the added constraint of hydrogen-bond connectivity between the catalytic side chains, to find solutions, a wider range of interactions between these side chains and the transition state must be considered. Our new method starts from a ChemDraw-like 2D representation of the transition state with hydrogen-bond donors, acceptors, and covalent interaction sites indicated, and all placements of side-chain functional groups that make the indicated interactions with the transition state, and are fully connected in a single hydrogen-bond network are systematically enumerated. The RosettaMatch method can then be used to identify realizations of these fully-connected active sites in protein scaffolds. The method generates many fully-connected active site solutions for a set of model reactions that are promising starting points for the design of fully-preorganized enzyme catalysts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Langan2019,

title = {De novo design of bioactive protein switches},

author = {Langan, Robert A.

 and Boyken, Scott E.

 and Ng, Andrew H.

 and Samson, Jennifer A.

 and Dods, Galen

 and Westbrook, Alexandra M.

 and Nguyen, Taylor H.

 and Lajoie, Marc J.

 and Chen, Zibo

 and Berger, Stephanie

 and Mulligan, Vikram Khipple

 and Dueber, John E.

 and Novak, Walter R. P.

 and El-Samad, Hana

 and Baker, David},

url = {https://doi.org/10.1038/s41586-019-1432-8

https://www.nature.com/articles/s41586-019-1432-8

https://www.bakerlab.org/wp-content/uploads/2019/07/Langan_LOCKR.pdf},

doi = {10.1038/s41586-019-1432-8},

year  = {2019},

date = {2019-07-24},

journal = {Nature},

abstract = {Allosteric regulation of protein function is widespread in biology, but is challenging for de novo protein design as it requires the explicit design of multiple states with comparable free energies. Here we explore the possibility of designing switchable protein systems de novo, through the modulation of competing inter- and intramolecular interactions. We design a static, five-helix ‘cage’ with a single interface that can interact either intramolecularly with a terminal ‘latch’ helix or intermolecularly with a peptide ‘key’. Encoded on the latch are functional motifs for binding, degradation or nuclear export that function only when the key displaces the latch from the cage. We describe orthogonal cage–key systems that function in vitro, in yeast and in mammalian cells with up to 40-fold activation of function by key. The ability to design switchable protein functions that are controlled by induced conformational change is a milestone for de novo protein design, and opens up new avenues for synthetic biology and cell engineering.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ng2019,

title = {Modular and tunable biological feedback control using a de novo protein switch},

author = {Ng, Andrew H.

and Nguyen, Taylor H.

and Gómez-Schiavon, Mariana

and Dods, Galen

and Langan, Robert A.

and Boyken, Scott E.

and Samson, Jennifer A.

and Waldburger, Lucas M.

and Dueber, John E.

and Baker, David

and El-Samad, Hana},

url = {https://doi.org/10.1038/s41586-019-1425-7

https://www.nature.com/articles/s41586-019-1425-7

https://www.bakerlab.org/wp-content/uploads/2019/07/Ng_LOCKR_circuits.pdf},

doi = {10.1038/s41586-019-1425-7},

year  = {2019},

date = {2019-07-24},

journal = {Nature},

abstract = {De novo-designed proteins1–3 hold great promise as building blocks for synthetic circuits, and can complement the use of engineered variants of natural proteins4–7. One such designer protein—degronLOCKR, which is based on ‘latching orthogonal cage–key proteins’ (LOCKR) technology8—is a switch that degrades a protein of interest in vivo upon induction by a genetically encoded small peptide. Here we leverage the plug-and-play nature of degronLOCKR to implement feedback control of endogenous signalling pathways and synthetic gene circuits. We first generate synthetic negative and positive feedback in the yeast mating pathway by fusing degronLOCKR to endogenous signalling molecules, illustrating the ease with which this strategy can be used to rewire complex endogenous pathways. We next evaluate feedback control mediated by degronLOCKR on a synthetic gene circuit9, to quantify the feedback capabilities and operational range of the feedback control circuit. The designed nature of degronLOCKR proteins enables simple and rational modifications to tune feedback behaviour in both the synthetic circuit and the mating pathway. The ability to engineer feedback control into living cells represents an important milestone in achieving the full potential of synthetic biology10,11,12. More broadly, this work demonstrates the large and untapped potential of de novo design of proteins for generating tools that implement complex synthetic functionalities in cells for biotechnological and therapeutic applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Park2019,

title = {High‐accuracy refinement using Rosetta in CASP13},

author = {Hahnbeom Park and Gyu Rie Lee and David E. Kim and Ivan Anishchanka and Qian Cong and David Baker},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25784},

doi = {10.1002/prot.25784},

year  = {2019},

date = {2019-07-20},

journal = {Proteins},

abstract = {Because proteins generally fold to their lowest free energy states, energy‐guided refinement in principle should be able to systematically improve the quality of protein structure models generated using homologous structure or co‐evolution derived information. However, because of the high dimensionality of the search space, there are far more ways to degrade the quality of a near native model than to improve it, and hence refinement methods are very sensitive to energy function errors. In CASP13, we sought to carry out a thorough search for low energy states in the neighborhood of a starting model using restraints to avoid straying too far. The approach was reasonably successful in improving both regions largely incorrect in the starting models as well core regions that started out closer to the correct structure. Models with GDT‐HA over 70 were obtained for five targets and for one of those, an accuracy of 0.5 å backbone RMSD was achieved. An important current challenge is to improve performance in refining oligomers and/or larger proteins, for which the search problem remains extremely difficult.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cong2019,

title = {Protein interaction networks revealed by proteome coevolution},

author = {Qian Cong and Ivan Anishchenko and Sergey Ovchinnikov and David Baker},

url = {https://science.sciencemag.org/content/365/6449/185

https://www.bakerlab.org/wp-content/uploads/2019/07/2019_Cong_ProteomeCoevolution.pdf},

doi = {10.1126/science.aaw6718},

year  = {2019},

date = {2019-07-11},

journal = {Science},

abstract = {Residue-residue coevolution has been observed across a number of protein-protein interfaces, but the extent of residue coevolution between protein families on the whole-proteome scale has not been systematically studied. We investigate coevolution between 5.4 million pairs of proteins in Escherichia coli and between 3.9 millions pairs in Mycobacterium tuberculosis. We find strong coevolution for binary complexes involved in metabolism and weaker coevolution for larger complexes playing roles in genetic information processing. We take advantage of this coevolution, in combination with structure modeling, to predict protein-protein interactions (PPIs) with an accuracy that benchmark studies suggest is considerably higher than that of proteome-wide two-hybrid and mass spectrometry screens. We identify hundreds of previously uncharacterized PPIs in E. coli and M. tuberculosis that both add components to known protein complexes and networks and establish the existence of new ones.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pyles2019,

title = {Controlling protein assembly on inorganic crystals through designed protein interfaces},

author = {Harley Pyles and Shuai Zhang and James J. De Yoreo and David Baker },

url = {https://www.nature.com/articles/s41586-019-1361-6

https://www.bakerlab.org/wp-content/uploads/2019/07/2019_Pyles_MicaBinder.pdf},

doi = {10.1038/s41586-019-1361-6},

year  = {2019},

date = {2019-07-10},

journal = {Nature},

abstract = {The ability of proteins and other macromolecules to interact with inorganic surfaces is essential to biological function. The proteins involved in these interactions are highly charged and often rich in carboxylic acid side chains, but the structures of most protein–inorganic interfaces are unknown. We explored the possibility of systematically designing structured protein–mineral interfaces, guided by the example of ice-binding proteins, which present arrays of threonine residues (matched to the ice lattice) that order clathrate waters into an ice-like structure6. Here we design proteins displaying arrays of up to 54 carboxylate residues geometrically matched to the potassium ion (K+) sublattice on muscovite mica (001). At low K+ concentration, individual molecules bind independently to mica in the designed orientations, whereas at high K+ concentration, the designs form two-dimensional liquid-crystal phases, which accentuate the inherent structural bias in the muscovite lattice to produce protein arrays ordered over tens of millimetres. Incorporation of designed protein–protein interactions preserving the match between the proteins and the K+ lattice led to extended self-assembled structures on mica: designed end-to-end interactions produced micrometre-long single-protein-diameter wires and a designed trimeric interface yielded extensive honeycomb arrays. The nearest-neighbour distances in these hexagonal arrays could be set digitally between 7.5 and 15.9 nanometres with 2.1-nanometre selectivity by changing the number of repeat units in the monomer. These results demonstrate that protein–inorganic lattice interactions can be systematically programmed and set the stage for designing protein–inorganic hybrid materials.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Koepnick2019,

title = {De novo protein design by citizen scientists},

author = {Koepnick, Brian

and Flatten, Jeff

and Husain, Tamir

and Ford, Alex

and Silva, Daniel-Adriano

and Bick, Matthew J.

and Bauer, Aaron

and Liu, Gaohua

and Ishida, Yojiro

and Boykov, Alexander

and Estep, Roger D.

and Kleinfelter, Susan

and Nørgård-Solano, Toke

and Wei, Linda

and Players, Foldit

and Montelione, Gaetano T.

and DiMaio, Frank

and Popović, Zoran

and Khatib, Firas

and Cooper, Seth

and Baker, David},

url = {https://doi.org/10.1038/s41586-019-1274-4

https://www.bakerlab.org/wp-content/uploads/2019/06/Koepnick_Nature2019_FolditDesign.pdf},

doi = {10.1038/s41586-019-1274-4},

year  = {2019},

date = {2019-06-05},

journal = {Nature},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Boyken2019,

title = {De novo design of tunable, pH-driven conformational changes},

author = {Boyken, Scott E. and Benhaim, Mark A. and Busch, Florian and Jia, Mengxuan and Bick, Matthew J. and Choi, Heejun and Klima, Jason C. and Chen, Zibo and Walkey, Carl and Mileant, Alexander and Sahasrabuddhe, Aniruddha and Wei, Kathy Y. and Hodge, Edgar A. and Byron, Sarah and Quijano-Rubio, Alfredo and Sankaran, Banumathi and King, Neil P. and Lippincott-Schwartz, Jennifer and Wysocki, Vicki H. and Lee, Kelly K. and Baker, David

},

url = {https://science.sciencemag.org/content/364/6441/658

https://www.bakerlab.org/wp-content/uploads/2019/06/Boyken_etal2019_pH_conformational_changes.pdf},

doi = {10.1126/science.aav7897},

year  = {2019},

date = {2019-05-17},

journal = {Science},

volume = {364},

number = {6441},

pages = {658-664},

abstract = {The ability of naturally occurring proteins to change conformation in response to environmental changes is critical to biological function. Although there have been advances in the de novo design of stable proteins with a single, deep free-energy minimum, the design of conformational switches remains challenging. We present a general strategy to design pH-responsive protein conformational changes by precisely preorganizing histidine residues in buried hydrogen-bond networks. We design homotrimers and heterodimers that are stable above pH 6.5 but undergo cooperative, large-scale conformational changes when the pH is lowered and electrostatic and steric repulsion builds up as the network histidine residues become protonated. The transition pH and cooperativity can be controlled through the number of histidine-containing networks and the strength of the surrounding hydrophobic interactions. Upon disassembly, the designed proteins disrupt lipid membranes both in vitro and after being endocytosed in mammalian cells. Our results demonstrate that environmentally triggered conformational changes can now be programmed by de novo protein design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dang2019,

title = {Receptor subtype discrimination using extensive shape complementary designed interfaces},

author = {Dang, Luke T. and Miao, Yi and Ha, Andrew and Yuki, Kanako and Park, Keunwan and Janda, Claudia Y. and Jude, Kevin M. and Mohan, Kritika and Ha, Nhi and Vallon, Mario and Yuan, Jenny and Vilches-Moure, José G. and Kuo, Calvin J. and Garcia, K. Christopher and Baker, David},

url = {https://doi.org/10.1038/s41594-019-0224-z

https://www.bakerlab.org/wp-content/uploads/2019/05/Dang2019_NSMB_ReceptorSubtypeDiscrimination.pdf},

doi = {10.1038/s41594-019-0224-z},

issn = {1545-9985},

year  = {2019},

date = {2019-05-13},

journal = {Nature Structural & Molecular Biology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baker2019,

title = {What has de novo protein design taught us about protein folding and biophysics?},

author = {David Baker},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/pro.3588

https://www.bakerlab.org/wp-content/uploads/2019/04/Baker-2019-Protein_Science.pdf},

doi = {10.1002/pro.3588},

year  = {2019},

date = {2019-02-12},

journal = {Protein Science},

volume = {28},

number = {4},

pages = {678-683},

abstract = {Recent progress in de novo protein design has led to an explosion of new protein structures, functions and assemblies. In this essay, I consider how the successes and failures in this new area inform our understanding of the proteins in nature and, more generally, the predictive computational modeling of biological systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Silva2019,

title = {De novo design of potent and selective mimics of IL-2 and IL-15},

author = {Silva, Daniel-Adriano and

Yu, Shawn and

Ulge, Umut Y. and

Spangler, Jamie B. and

Jude, Kevin M. and

Labão-Almeida, Carlos and

Ali, Lestat R. and

Quijano-Rubio, Alfredo and

Ruterbusch, Mikel and

Leung, Isabel and

Biary, Tamara and

Crowley, Stephanie J. and

Marcos, Enrique and

Walkey, Carl D. and

Weitzner, Brian D. and

Pardo-Avila, Fátima and

Castellanos, Javier and

Carter, Lauren and

Stewart, Lance and

Riddell, Stanley R. and

Pepper, Marion and

Bernardes, Gonçalo J. L. and

Dougan, Michael and

Garcia, K. Christopher and

Baker, David

},

url = {https://www.nature.com/articles/s41586-018-0830-7

https://www.bakerlab.org/wp-content/uploads/2019/01/Silva2018_IL2-15.pdf},

doi = {10.1038/s41586-018-0830-7},

issn = {1476-4687},

year  = {2019},

date = {2019-01-09},

journal = {Nature},

abstract = {We describe a de novo computational approach for designing proteins that recapitulate the binding sites of natural cytokines, but are otherwise unrelated in topology or amino acid sequence. We use this strategy to design mimics of the central immune cytokine interleukin-2 (IL-2) that bind to the IL-2 receptor βγc heterodimer (IL-2Rβγc) but have no binding site for IL-2Rα (also called CD25) or IL-15Rα (also known as CD215). The designs are hyper-stable, bind human and mouse IL-2Rβγc with higher affinity than the natural cytokines, and elicit downstream cell signalling independently of IL-2Rα and IL-15Rα. Crystal structures of the optimized design neoleukin-2/15 (Neo-2/15), both alone and in complex with IL-2Rβγc, are very similar to the designed model. Neo-2/15 has superior therapeutic activity to IL-2 in mouse models of melanoma and colon cancer, with reduced toxicity and undetectable immunogenicity. Our strategy for building hyper-stable de novo mimetics could be applied generally to signalling proteins, enabling the creation of superior therapeutic candidates.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Foight2019,

title = {Multi-input chemical control of protein dimerization for programming graded cellular responses},

author = {Foight, Glenna Wink and Wang, Zhizhi and Wei, Cindy T. and Jr Greisen, Per and Warner, Katrina M. and Cunningham-Bryant, Daniel and Park, Keunwan and Brunette, T. J. and Sheffler, William and Baker, David and Maly, Dustin J.},

url = {https://www.nature.com/articles/s41587-019-0242-8

https://www.bakerlab.org/wp-content/uploads/2020/06/Foight_et_al_2019_NatBiotech.pdf},

doi = {10.1038/s41587-019-0242-8},

isbn = {1546-1696},

year  = {2019},

date = {2019-09-09},

journal = {Nature Biotechnology},

volume = {37},

number = {10},

pages = {1209-1216},

abstract = {Chemical and optogenetic methods for post-translationally controlling protein function have enabled modulation and engineering of cellular functions. However, most of these methods only confer single-input, single-output control. To increase the diversity of post-translational behaviors that can be programmed, we built a system based on a single protein receiver that can integrate multiple drug inputs, including approved therapeutics. Our system translates drug inputs into diverse outputs using a suite of engineered reader proteins to provide variable dimerization states of the receiver protein. We show that our single receiver protein architecture can be used to program a variety of cellular responses, including graded and proportional dual-output control of transcription and mammalian cell signaling. We apply our tools to titrate the competing activities of the Rac and Rho GTPases to control cell morphology. Our versatile tool set will enable researchers to post-translationally program mammalian cellular processes and to engineer cell therapies.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2019,

title = {Protein contact prediction using metagenome sequence data and residual neural networks},

author = {Qi Wu and Zhenling Peng and Ivan Anishchenko and Qian Cong and David Baker and Jianyi Yang},

url = {https://academic.oup.com/bioinformatics/article/36/1/41/5512356},

doi = {10.1093/bioinformatics/btz477},

year  = {2019},

date = {2019-06-07},

journal = {Bioinformatics},

volume = {36},

number = {1},

abstract = {Motivation: Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects. Results: Based on the improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning-based contact prediction method. MapPred consists of two component methods, DeepMSA and DeepMeta, both trained with the residual neural networks. DeepMSA was inspired by the recent method DeepCov, which was trained on 441 matrices of covariance features. By considering the symmetry of contact map, we reduced the number of matrices to 231, which makes the training more efficient in DeepMSA. Experiments show that DeepMSA outperforms DeepCov by 10–13% in precision. DeepMeta works by combining predicted contacts and other sequence profile features. Experiments on three benchmark datasets suggest that the contribution from the metagenome sequence data is significant with P-values less than 4.04E-17. MapPred is shown to be complementary and comparable the state-of-the-art methods. The success of MapPred is attributed to three factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA and optimized training by the residual neural networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mohan2019,

title = {Topological control of cytokine receptor signaling induces differential effects in hematopoiesis},

author = {Mohan, Kritika and Ueda, George and Kim, Ah Ram and Jude, Kevin M. and Fallas, Jorge A. and Guo, Yu and Hafer, Maximillian and Miao, Yi and Saxton, Robert A. and Piehler, Jacob and Sankaran, Vijay G. and Baker, David and Garcia, K. Christopher

},

url = {https://science.sciencemag.org/content/364/6442/eaav7532

https://www.bakerlab.org/wp-content/uploads/2019/05/Mohan2019_Science_cytokinebinders.pdf},

doi = {10.1126/science.aav7532},

year  = {2019},

date = {2019-05-24},

journal = {Science},

volume = {364},

number = {6442},

abstract = {Although tunable signaling by G protein–coupled receptors can be exploited through medicinal chemistry, a comparable pharmacological approach has been lacking for the modulation of signaling through dimeric receptors, such as those for cytokines. We present a strategy to modulate cytokine receptor signaling output by use of a series of designed C2-symmetric cytokine mimetics, based on the designed ankyrin repeat protein (DARPin) scaffold, that can systematically control erythropoietin receptor (EpoR) dimerization orientation and distance between monomers. We sampled a range of EpoR geometries by varying intermonomer angle and distance, corroborated by several ligand-EpoR complex crystal structures. Across the range, we observed full, partial, and biased agonism as well as stage-selective effects on hematopoiesis. This surrogate ligand strategy opens access to pharmacological modulation of therapeutically important cytokine and growth factor receptor systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2019,

title = {Self-Assembling 2D Arrays with de Novo Protein Building Blocks},

author = {Chen, Zibo and Johnson, Matthew C. and Chen, Jiajun and Bick, Matthew J. and Boyken, Scott E. and Lin, Baihan and De Yoreo, James J. and Kollman, Justin M. and Baker, David and DiMaio, Frank},

url = {https://www.bakerlab.org/wp-content/uploads/2020/02/Chen2019_JACS_2Darrays.pdf

https://pubs.acs.org/doi/abs/10.1021/jacs.9b01978#},

doi = {10.1021/jacs.9b01978},

year  = {2019},

date = {2019-05-03},

journal = {Journal of the American Chemical Society},

abstract = {Modular self-assembly of biomolecules in two dimensions (2D) is straightforward with DNA but has been difficult to realize with proteins, due to the lack of modular specificity similar to Watson−Crick base pairing. Here we describe a general approach to design 2D arrays using de novo designed pseudosymmetric protein building blocks. A homodimeric helical bundle was reconnected into a monomeric building block, and the surface was redesigned in Rosetta to enable self-assembly into a 2D array in the C12 layer symmetry group. Two out of ten designed arrays assembled to micrometer scale under negative stain electron microscopy, and displayed the designed lattice geometry with assembly size up to 100 nm under atomic force microscopy. The design of 2D arrays with pseudosymmetric building blocks is an important step toward the design of programmable protein self-assembly via pseudosymmetric patterning of orthogonal binding interfaces.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marcandalli2019,

title = {Induction of Potent Neutralizing Antibody Responses by a Designed Protein Nanoparticle Vaccine for Respiratory Syncytial Virus},

author = {Jessica Marcandalli, Brooke Fiala, Sebastian Ols, Michela Perotti, Willem de van der Schueren, Joost Snijder, Edgar Hodge, Mark Benhaim, Rashmi Ravichandran, Lauren Carter, Will Sheffler, Livia Brunner, Maria Lawrenz, Patrice Dubois, Antonio Lanzavecchia, Federica Sallusto, Kelly K. Lee, David Veesler, Colin E. Correnti, Lance J. Stewart, David Baker, Karin Loré, Laurent Perez, Neil P. King,},

url = {https://www.cell.com/cell/pdf/S0092-8674(19)30109-6.pdf},

doi = {10.1016/j.cell.2019.01.046},

year  = {2019},

date = {2019-03-07},

journal = {Cell},

volume = {176},

number = {6},

pages = {1420-1431},

abstract = {Respiratory syncytial virus (RSV) is a worldwide public health concern for which no vaccine is available. Elucidation of the prefusion structure of the RSV F glycoprotein and its identification as the main target of neutralizing antibodies have provided new opportunities for development of an effective vaccine. Here, we describe the structure-based design of a self-assembling protein nanoparticle presenting a prefusion-stabilized variant of the F glycoprotein trimer (DS-Cav1) in a repetitive array on the nanoparticle exterior. The two-component nature of the nanoparticle scaffold enabled the production of highly ordered, monodisperse immunogens that display DS-Cav1 at controllable density. In mice and nonhuman primates, the full-valency nanoparticle immunogen displaying 20 DS-Cav1 trimers induced neutralizing antibody responses ∼10-fold higher than trimeric DS-Cav1. These results motivate continued development of this promising nanoparticle RSV vaccine candidate and establish computationally designed two-component nanoparticles as a robust and customizable platform for structure-based vaccine design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2018,

title = {Programmable design of orthogonal protein heterodimers},

author = {Chen, Zibo and

Boyken, Scott E. and

Jia, Mengxuan and

Busch, Florian and

Flores-Solis, David and

Bick, Matthew J. and

Lu, Peilong and

VanAernum, Zachary L. and

Sahasrabuddhe, Aniruddha and

Langan, Robert A. and

Bermeo, Sherry and

Brunette, T. J. and

Mulligan, Vikram Khipple and

Carter, Lauren P. and

DiMaio, Frank and

Sgourakis, Nikolaos G. and

Wysocki, Vicki H. and

Baker, David},

url = {https://doi.org/10.1038/s41586-018-0802-y

https://www.bakerlab.org/wp-content/uploads/2018/12/Chen2018_heterodimers.pdf},

doi = {10.1038/s41586-018-0802-y},

issn = {1476-4687},

year  = {2018},

date = {2018-12-19},

journal = {Nature},

abstract = {Specificity of interactions between two DNA strands, or between protein and DNA, is often achieved by varying bases or side chains coming off the DNA or protein backbone—for example, the bases participating in Watson–Crick pairing in the double helix, or the side chains contacting DNA in TALEN–DNA complexes. By contrast, specificity of protein–protein interactions usually involves backbone shape complementarity1, which is less modular and hence harder to generalize. Coiled-coil heterodimers are an exception, but the restricted geometry of interactions across the heterodimer interface (primarily at the heptad a and d positions2) limits the number of orthogonal pairs that can be created simply by varying side-chain interactions3,4. Here we show that protein–protein interaction specificity can be achieved using extensive and modular side-chain hydrogen-bond networks. We used the Crick generating equations5 to produce millions of four-helix backbones with varying degrees of supercoiling around a central axis, identified those accommodating extensive hydrogen-bond networks, and used Rosetta to connect pairs of helices with short loops and to optimize the remainder of the sequence. Of 97 such designs expressed in Escherichia coli, 65 formed constitutive heterodimers, and the crystal structures of four designs were in close agreement with the computational models and confirmed the designed hydrogen-bond networks. In cells, six heterodimers were fully orthogonal, and in vitro—following mixing of 32 chains from 16 heterodimer designs, denaturation in 5 M guanidine hydrochloride and reannealing—almost all of the interactions observed by native mass spectrometry were between the designed cognate pairs. The ability to design orthogonal protein heterodimers should enable sophisticated protein-based control logic for synthetic biology, and illustrates that nature has not fully explored the possibilities for programmable biomolecular interaction modalities.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shen2018,

title = {De novo design of self-assembling helical protein filaments},

author = {Shen, Hao and Fallas, Jorge A. and Lynch, Eric and Sheffler, William and Parry, Bradley and Jannetty, Nicholas and Decarreau, Justin and Wagenbach, Michael and Vicente, Juan Jesus and Chen, Jiajun and Wang, Lei and Dowling, Quinton and Oberdorfer, Gustav and Stewart, Lance and Wordeman, Linda and De Yoreo, James and Jacobs-Wagner, Christine and Kollman, Justin and Baker, David},

url = {http://science.sciencemag.org/content/362/6415/705

https://www.bakerlab.org/wp-content/uploads/2018/12/Shen2018_filaments.pdf},

doi = {10.1126/science.aau3775},

issn = {0036-8075},

year  = {2018},

date = {2018-11-09},

journal = {Science},

volume = {362},

number = {6415},

pages = {705–709},

abstract = {There has been some success in designing stable peptide filaments; however, mimicking the reversible assembly of many natural protein filaments is challenging. Dynamic filaments usually comprise independently folded and asymmetric proteins and using such building blocks requires the design of multiple intermonomer interfaces. Shen et al. report the design of self-assembling helical filaments based on previously designed stable repeat proteins. The filaments are micron scale, and their diameter can be tuned by varying the number of repeats in the monomer. Anchor and capping units, built from monomers that lack an interaction interface, can be used to control assembly and disassembly.Science, this issue p. 705We describe a general computational approach to designing self-assembling helical filaments from monomeric proteins and use this approach to design proteins that assemble into micrometer-scale filaments with a wide range of geometries in vivo and in vitro. Cryo{textendash}electron microscopy structures of six designs are close to the computational design models. The filament building blocks are idealized repeat proteins, and thus the diameter of the filaments can be systematically tuned by varying the number of repeat units. The assembly and disassembly of the filaments can be controlled by engineered anchor and capping units built from monomers lacking one of the interaction surfaces. The ability to generate dynamic, highly ordered structures that span micrometers from protein monomers opens up possibilities for the fabrication of new multiscale metamaterials.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marcos2018,

title = {De novo design of a non-local β-sheet protein with high stability and accuracy},

author = {Marcos, Enrique and

Chidyausiku, Tamuka M. and

McShan, Andrew C. and

Evangelidis, Thomas and

Nerli, Santrupti and

Carter, Lauren and

Nivón, Lucas G. and

Davis, Audrey and

Oberdorfer, Gustav and

Tripsianes, Konstantinos and

Sgourakis, Nikolaos G. and

Baker, David},

url = {https://doi.org/10.1038/s41594-018-0141-6

https://www.bakerlab.org/wp-content/uploads/2018/11/Marcos_etal_2018.pdf},

doi = {10.1038/s41594-018-0141-6},

issn = {1545-9985},

year  = {2018},

date = {2018-10-29},

journal = {Nature Structural & Molecular Biology},

abstract = {β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-β-sheet proteins from first principles lags far behind the design of all-α or mixed-αβ domains owing to their non-local nature and the tendency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local β-sheet protein structures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{1011,

title = {De novo design of a fluorescence-activating β-barrel},

author = {Jiayi Dou* and Anastassia A. Vorobieva* and William Sheffler and Lindsey A. Doyle and Hahnbeom Park and Matthew J. Bick and Binchen Mao and Glenna W. Foight and Min Yen Lee and Lauren A. Gagnon and Lauren Carter and Banumathi Sankaran and Sergey Ovchinnikov and Enrique Marcos and Po-Ssu Huang and Joshua C. Vaughan and Barry L. Stoddard and David Baker },

url = {https://www.nature.com/articles/s41586-018-0509-0

https://www.bakerlab.org/wp-content/uploads/2018/09/s41586-018-0509-0.pdf},

doi = {10.1038/s41586-018-0509-0},

issn = {1476-4687},

year  = {2018},

date = {2018-09-12},

journal = {Nature},

abstract = {The regular arrangements of β-strands around a central axis in β-barrels and of α-helices in coiled coils contrast with the irregular tertiary structures of most globular proteins, and have fascinated structural biologists since they were first discovered. Simple parametric models have been used to design a wide range of α-helical coiled-coil structures, but to date there has been no success with β-barrels. Here we show that accurate de novo design of β-barrels requires considerable symmetry-breaking to achieve continuous hydrogen-bond connectivity and eliminate backbone strain. We then build ensembles of β-barrel backbone models with cavity shapes that match the fluorogenic compound DFHBI, and use a hierarchical grid-based search method to simultaneously optimize the rigid-body placement of DFHBI in these cavities and the identities of the surrounding amino acids to achieve high shape and chemical complementarity. The designs have high structural accuracy and bind and fluorescently activate DFHBI in vitro and in Escherichia coli, yeast and mammalian cells. This de novo design of small-molecule binding activity, using backbones custom-built to bind the ligand, should enable the design of increasingly sophisticated ligand-binding proteins, sensors and catalysts that are not limited by the backbone geometries available in known protein structures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Park2018,

title = {Protein homology model refinement by large-scale energy optimization},

author = {Park, Hahnbeom and Ovchinnikov, Sergey and Kim, David E. and DiMaio, Frank and Baker, David},

url = {https://www.pnas.org/content/115/12/3054

https://www.bakerlab.org/wp-content/uploads/2019/01/Park2018_refinement.pdf},

doi = {10.1073/pnas.1719115115},

issn = {0027-8424},

year  = {2018},

date = {2018-03-20},

journal = {Proceedings of the National Academy of Sciences},

volume = {115},

number = {12},

pages = {3054–3059},

abstract = {Protein structure refinement by direct global energy optimization has been a longstanding challenge in computational structural biology due to limitations in both energy function accuracy and conformational sampling. This manuscript demonstrates that with recent advances in both areas, refinement can significantly improve protein comparative models based on structures of distant homologues.Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lu1042,

title = {Accurate computational design of multipass transmembrane proteins},

author = {Lu, Peilong and Min, Duyoung and DiMaio, Frank and Wei, Kathy Y. and Vahey, Michael D. and Boyken, Scott E. and Chen, Zibo and Fallas, Jorge A. and Ueda, George and Sheffler, William and Mulligan, Vikram Khipple and Xu, Wenqing and Bowie, James U. and Baker, David},

url = {http://science.sciencemag.org/content/359/6379/1042

https://www.bakerlab.org/wp-content/uploads/2018/03/Lu_Science_2018.pdf},

doi = {10.1126/science.aaq1739},

issn = {0036-8075},

year  = {2018},

date = {2018-03-02},

journal = {Science},

volume = {359},

number = {6379},

pages = {1042--1046},

abstract = {In recent years, soluble protein design has achieved successes such as artificial enzymes and large protein cages. Membrane proteins present a considerable design challenge, but here too there have been advances, including the design of a zinc-transporting tetramer. Lu et al. report the design of stable transmembrane monomers, homodimers, trimers, and tetramers with up to eight membrane-spanning regions in an oligomer. The designed proteins adopted the target oligomerization state and localized to the predicted cellular membranes, and crystal structures of the designed dimer and tetramer reflected the design models.Science, this issue p. 1042The computational design of transmembrane proteins with more than one membrane-spanning region remains a major challenge. We report the design of transmembrane monomers, homodimers, trimers, and tetramers with 76 to 215 residue subunits containing two to four membrane-spanning regions and up to 860 total residues that adopt the target oligomerization state in detergent solution. The designed proteins localize to the plasma membrane in bacteria and in mammalian cells, and magnetic tweezer unfolding experiments in the membrane indicate that they are very stable. Crystal structures of the designed dimer and tetramer{textemdash}a rocket-shaped structure with a wide cytoplasmic base that funnels into eight transmembrane helices{textemdash}are very close to the design models. Our results pave the way for the design of multispan membrane proteins with new functions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Silva2018,

title = {Structures and disulfide cross‐linking of de novo designed therapeutic mini‐proteins},

author = {Silva, Daniel-Adriano and Stewart, Lance and Lam, Kwok-Ho and Jin, Rongsheng and Baker, David},

url = {https://febs.onlinelibrary.wiley.com/doi/abs/10.1111/febs.14394

},

doi = {10.1111/febs.14394},

year  = {2018},

date = {2018-02-01},

journal = {FEBS Journal},

volume = {285},

number = {10},

pages = {1783-1785},

abstract = {Recent advances in computational protein design now enable the massively parallel de novo design and experimental characterization of small hyperstable binding proteins with potential therapeutic activity. By providing experimental feedback on tens of thousands of designed proteins, the design-build-test-learn pipeline provides a unique opportunity to systematically improve our understanding of protein folding and binding. Here, we review the structures of mini-protein binders in complex with Influenza hemagglutinin and Bot toxin, and illustrate in the case of disulfide bond placement how analysis of the large datasets of computational models and experimental data can be used to identify determinants of folding and binding.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Day2018,

title = {Unintended specificity of an engineered ligand-binding protein facilitated by unpredicted plasticity of the protein fold},

author = {Day, Austin L and Greisen, Per and Doyle, Lindsey and Schena, Alberto and Stella, Nephi and Johnsson, Kai and Baker, David and Stoddard, Barry

},

url = {https://dx.doi.org/10.1093/protein/gzy031

https://www.bakerlab.org/wp-content/uploads/2019/02/Day2018.pdf},

doi = {10.1093/protein/gzy031},

year  = {2018},

date = {2018-12-19},

journal = {Protein Engineering, Design and Selection},

abstract = {Attempts to create novel ligand-binding proteins often focus on formation of a binding pocket with shape complementarity against the desired ligand (particularly for compounds that lack distinct polar moieties). Although designed proteins often exhibit binding of the desired ligand, in some cases they display unintended recognition behavior. One such designed protein, that was originally intended to bind tetrahydrocannabinol (THC), was found instead to display binding of 25-hydroxy-cholecalciferol (25-D3) and was subjected to biochemical characterization, further selections for enhanced 25-D3 binding affinity and crystallographic analyses. The deviation in specificity is due in part to unexpected altertion of its conformation, corresponding to a significant change of the orientation of an α-helix and an equally large movement of a loop, both of which flank the designed ligand-binding pocket. Those changes led to engineered protein constructs that exhibit significantly more contacts and complementarity towards the 25-D3 ligand than the initial designed protein had been predicted to form towards its intended THC ligand. Molecular dynamics simulations imply that the initial computationally designed mutations may contribute to the movement of the helix. These analyses collectively indicate that accurate prediction and control of backbone dynamics conformation, through a combination of improved conformational sampling and/or de novo structure design, represents a key area of further development for the design and optimization of engineered ligand-binding proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Romero2018,

title = {Simple yet functional phosphate-loop proteins},

author = {Romero Romero, Maria Luisa and Yang, Fan and Lin, Yu-Ru and Toth-Petroczy, Agnes and Berezovsky, Igor N. and Goncearenco, Alexander and Yang, Wen and Wellner, Alon and Kumar-Deshmukh, Fanindra and Sharon, Michal and Baker, David and Varani, Gabriele and Tawfik, Dan S.},

url = {https://www.bakerlab.org/wp-content/uploads/2019/02/Romero2018.pdfhttps://www.pnas.org/content/115/51/E11943

},

doi = {10.1073/pnas.1812400115},

issn = {0027-8424},

year  = {2018},

date = {2018-11-18},

journal = {PNAS},

volume = {115},

number = {51},

pages = {E11943--E11950},

abstract = {The complexity of modern proteins makes the understanding of how proteins evolved from simple beginnings a daunting challenge. The Walker-A motif is a phosphate-binding loop (P-loop) found in possibly the most ancient and abundant protein class, so-called P-loop NTPases. By combining phylogenetic analysis and computational protein design, we have generated simple proteins, of only 55 residues, that contain the P-loop and thereby confer binding of a range of phosphate-containing ligands{textemdash}and even more avidly, RNA and single-strand DNA. Our results show that biochemical function can be implemented in small and simple proteins; they intriguingly suggest that the P-loop emerged as a polynucleotide binder and catalysis of phosphoryl transfer evolved later upon acquisition of higher sequence and structural complexity.Abundant and essential motifs, such as phosphate-binding loops (P-loops), are presumed to be the seeds of modern enzymes. The Walker-A P-loop is absolutely essential in modern NTPase enzymes, in mediating binding, and transfer of the terminal phosphate groups of NTPs. However, NTPase function depends on many additional active-site residues placed throughout the protein{textquoteright}s scaffold. Can motifs such as P-loops confer function in a simpler context? We applied a phylogenetic analysis that yielded a sequence logo of the putative ancestral Walker-A P-loop element: a β-strand connected to an α-helix via the P-loop. Computational design incorporated this element into de novo designed β-α repeat proteins with relatively few sequence modifications. We obtained soluble, stable proteins that unlike modern P-loop NTPases bound ATP in a magnesium-independent manner. Foremost, these simple P-loop proteins avidly bound polynucleotides, RNA, and single-strand DNA, and mutations in the P-loop{textquoteright}s key residues abolished binding. Binding appears to be facilitated by the structural plasticity of these proteins, including quaternary structure polymorphism that promotes a combined action of multiple P-loops. Accordingly, oligomerization enabled a 55-aa protein carrying a single P-loop to confer avid polynucleotide binding. Overall, our results show that the P-loop Walker-A motif can be implemented in small and simple β-α repeat proteins, primarily as a polynucleotide binding motif.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Geiger-Schuller2018,

title = {Extreme stability in de novo-designed repeat arrays is determined by unusually stable short-range interactions},

author = {Geiger-Schuller, Kathryn and Sforza, Kevin and Yuhas, Max and Parmeggiani, Fabio and Baker, David and Barrick, Doug},

url = {https://www.pnas.org/content/115/29/7539

https://www.bakerlab.org/wp-content/uploads/2019/02/Geiger-Schuller2018.pdf},

doi = {10.1073/pnas.1800283115},

issn = {0027-8424},

year  = {2018},

date = {2018-07-17},

journal = {PNAS},

volume = {115},

number = {29},

pages = {7539-7544},

abstract = {We apply a statistical thermodynamic formalism to quantify the cooperativity of folding of de novo-designed helical repeat proteins (DHRs). This analysis provides a fundamental thermodynamic description of folding for de novo-designed proteins and permits comparison with naturally occurring repeat protein thermodynamics. We find that individual DHR units are intrinsically stable, unlike those of naturally occurring proteins. This observation reveals local (intrarepeat) interactions as a source of high stability in Rosetta-designed proteins and suggests that different types of DHR repeats may be combined in a single polypeptide chain, expanding the repertoire of folded DHRs for applications such as molecular recognition. Favorable intrinsic stability imparts a downhill shape to the energy landscape, suggesting that DHRs fold fast and through parallel pathways.Designed helical repeats (DHRs) are modular helix{textendash}loop{textendash}helix{textendash}loop protein structures that are tandemly repeated to form a superhelical array. Structures combining tandem DHRs demonstrate a wide range of molecular geometries, many of which are not observed in nature. Understanding cooperativity of DHR proteins provides insight into the molecular origins of Rosetta-based protein design hyperstability and facilitates comparison of energy distributions in artificial and naturally occurring protein folds. Here, we use a nearest-neighbor Ising model to quantify the intrinsic and interfacial free energies of four different DHRs. We measure the folding free energies of constructs with varying numbers of internal and terminal capping repeats for four different DHR folds, using guanidine-HCl and glycerol as destabilizing and solubilizing cosolvents. One-dimensional Ising analysis of these series reveals that, although interrepeat coupling energies are within the range seen for naturally occurring repeat proteins, the individual repeats of DHR proteins are intrinsically stable. This favorable intrinsic stability, which has not been observed for naturally occurring repeat proteins, adds to stabilizing interfaces, resulting in extraordinarily high stability. Stable repeats also impart a downhill shape to the energy landscape for DHR folding. These intrinsic stability differences suggest that part of the success of Rosetta-based design results from capturing favorable local interactions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lau2018,

title = {Discovery and engineering of enhanced SUMO protease enzymes},

author = {Yue-Ting K. Lau, and Vladimir Baytshtok, and Tessa A. Howard, and Brooke M. Fiala, and JayLee M. Johnson, and Lauren P. Carter, and David Baker, and Christopher D. Lima, and Christopher D. Bahl},

url = {http://www.jbc.org/content/293/34/13224.short

https://www.bakerlab.org/wp-content/uploads/2019/02/Lau2018.pdf},

doi = {10.1074/jbc.RA118.004146},

year  = {2018},

date = {2018-07-05},

journal = {The Journal of Biological Chemistry},

volume = {293},

pages = {13224-13233},

abstract = {Small ubiquitin-like modifier (SUMO) is commonly used as a protein fusion domain to facilitate expression and purification of recombinant proteins, and a SUMO-specific protease is then used to remove SUMO from these proteins. Although this protease is highly specific, its limited solubility and stability hamper its utility as an in vitro reagent. Here, we report improved SUMO protease enzymes obtained via two approaches. First, we developed a computational method and used it to re-engineer WT Ulp1 from Saccharomyces cerevisiae to improve protein solubility. Second, we discovered an improved SUMO protease via genomic mining of the thermophilic fungus Chaetomium thermophilum, as proteins from thermophilic organisms are commonly employed as reagent enzymes. Following expression in Escherichia coli, we found that these re-engineered enzymes can be more thermostable and up to 12 times more soluble, all while retaining WT-or-better levels of SUMO protease activity. The computational method we developed to design solubility-enhancing substitutions is based on the RosettaScripts application for the macromolecular modeling suite Rosetta, and it is broadly applicable for the improvement of solution properties of other proteins. Moreover, we determined the X-ray crystal structure of a SUMO protease from C. thermophilum to 1.44 Å resolution. This structure revealed that this enzyme exhibits structural and functional conservation with the S. cerevisiae SUMO protease, despite exhibiting only 28% sequence identity. In summary, by re-engineering the Ulp1 protease and discovering a SUMO protease from C. thermophilum, we have obtained proteases that are more soluble, more thermostable, and more efficient than the current commercially available Ulp1 enzyme.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hosseinzadeh2017,

title = {Comprehensive computational design of ordered peptide macrocycles},

author = {Hosseinzadeh, Parisa* and Bhardwaj, Gaurav* and Mulligan, Vikram Khipple* and Shortridge, Matthew D. and Craven, Timothy W. and Pardo-Avila, F{'a}tima and Rettie, Stephen A. and Kim, David E. and Silva, Daniel-Adriano and Ibrahim, Yehia M. and Webb, Ian K. and Cort, John R. and Adkins, Joshua N. and Varani, Gabriele and Baker, David},

url = {http://science.sciencemag.org/content/358/6369/1461

https://www.bakerlab.org/wp-content/uploads/2017/12/Science_Hosseinzadeh_et_al_2017.pdf},

doi = {10.1126/science.aap7577},

issn = {0036-8075},

year  = {2017},

date = {2017-12-15},

journal = {Science},

volume = {358},

number = {6369},

pages = {1461-1466},

abstract = {Mixed-chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to date, but there is currently no way to systematically search the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with L-amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of L- and D-amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Butterfield2017,

title = {Evolution of a designed protein assembly encapsulating its own RNA genome},

author = {Butterfield, Gabriel L.*

and Lajoie, Marc J.*

and Gustafson, Heather H.

and Sellers, Drew L.

and Nattermann, Una

and Ellis, Daniel

and Bale, Jacob B.

and Ke, Sharon

and Lenz, Garreck H.

and Yehdego, Angelica

and Ravichandran, Rashmi

and Pun, Suzie H.

and King, Neil P.

and Baker, David},

url = {http://dx.doi.org/10.1038/nature25157

https://www.bakerlab.org/wp-content/uploads/2017/12/Nature_Butterfield_et_al_2017.pdf},

doi = {10.1038/nature25157},

issn = {1476-4687},

year  = {2017},

date = {2017-12-13},

journal = {Nature},

abstract = {The challenges of evolution in a complex biochemical environment, coupling genotype to phenotype and protecting the genetic material, are solved elegantly in biological systems by the encapsulation of nucleic acids. In the simplest examples, viruses use capsids to surround their genomes. Although these naturally occurring systems have been modified to change their tropism and to display proteins or peptides, billions of years of evolution have favoured efficiency at the expense of modularity, making viral capsids difficult to engineer. Synthetic systems composed of non-viral proteins could provide a ‘blank slate’ to evolve desired properties for drug delivery and other biomedical applications, while avoiding the safety risks and engineering challenges associated with viruses. Here we create synthetic nucleocapsids, which are computationally designed icosahedral protein assemblies with positively charged inner surfaces that can package their own full-length mRNA genomes. We explore the ability of these nucleocapsids to evolve virus-like properties by generating diversified populations using Escherichia coli as an expression host. Several generations of evolution resulted in markedly improved genome packaging (more than 133-fold), stability in blood (from less than 3.7% to 71% of packaged RNA protected after 6hours of treatment), and in vivo circulation time (from less than 5minutes to approximately 4.5hours). The resulting synthetic nucleocapsids package one full length RNA genome for every 11 icosahedral assemblies, similar to the best recombinant adeno-associated virus vectors. Our results show that there are simple evolutionary paths through which protein assemblies can acquire virus-like genome packaging and protection. Considerable effort has been directed at ‘top-down’ modification of viruses to be safe and effective for drug delivery and vaccine applications; the ability to design synthetic nanomaterials computationally and to optimize them through evolution now enables a complementary ‘bottom-up’ approach with considerable advantages in programmability and control.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{1000b,

title = {Sampling and energy evaluation challenges in ligand binding protein design},

author = {Jiayi Dou and Lindsey Doyle and Per Greisen and Alberto Schena and Hahnbeom Park and Kai Johnsson and Barry Stoddard and David Baker},

url = {http://onlinelibrary.wiley.com/doi/10.1002/pro.3317/abstract



https://www.bakerlab.org/wp-content/uploads/2017/12/Dou_et_al-2017-Protein_Science.pdf},

doi = {10.1002/pro.3317},

issn = {1469-896},

year  = {2017},

date = {2017-10-30},

journal = {Protein Science},

volume = {26},

pages = {2426-2437},

abstract = {The steroid hormone 17α-hydroxylprogesterone (17-OHP) is a biomarker for congenital adrenal hyperplasia and hence there is considerable interest in development of sensors for this compound. We used computational protein design to generate protein models with binding sites for 17-OHP containing an extended, nonpolar, shape-complementary binding pocket for the four-ring core of the compound, and hydrogen bonding residues at the base of the pocket to interact with carbonyl and hydroxyl groups at the more polar end of the ligand. Eight of 16 designed proteins experimentally tested bind 17-OHP with micromolar affinity. A co-crystal structure of one of the designs revealed that 17-OHP is rotated 180° around a pseudo-two-fold axis in the compound and displays multiple binding modes within the pocket, while still interacting with all of the designed residues in the engineered site. Subsequent rounds of mutagenesis and binding selection improved the ligand affinity to nanomolar range, while appearing to constrain the ligand to a single bound conformation that maintains the same “flipped” orientation relative to the original design. We trace the discrepancy in the design calculations to two sources: first, a failure to model subtle backbone changes which alter the distribution of sidechain rotameric states and second, an underestimation of the energetic cost of desolvating the carbonyl and hydroxyl groups of the ligand. The difference between design model and crystal structure thus arises from both sampling limitations and energy function inaccuracies that are exacerbated by the near two-fold symmetry of the molecule.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chevalier2017,

title = {Massively parallel de novo protein design for targeted therapeutics},

author = {Aaron Chevalier* and Daniel-Adriano Silva* and Gabriel J. Rocklin* and Derrick R. Hicks and Renan Vergara and Patience Murapa and Steffen M. Bernard and Lu Zhang and Kwok-Ho Lam and Guorui Yao and Christopher D. Bahl and Shin-Ichiro Miyashita and Inna Goreshnik and 	James T. Fuller and	Merika T. Koday and Cody M. Jenkins and Tom Colvin and Lauren Carter and Alan Bohn and Cassie M. Bryan and D. Alejandro Fernández-Velasco and Lance Stewart and Min Dong and Xuhui Huang and Rongsheng Jin and Ian A. Wilson and Deborah H. Fuller and David Baker	},

url = {https://www.nature.com/nature/journal/v550/n7674/full/nature23912.html

https://www.bakerlab.org/wp-content/uploads/2017/12/Nature_Chevalier_etal_2017.pdf},

doi = {10.1038/nature23912},

issn = {0028-0836},

year  = {2017},

date = {2017-10-05},

journal = {Nature},

volume = {550},

number = {7674},

pages = {74-79},

abstract = {De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37–43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ovchinnikov2017,

title = {Protein structure prediction using Rosetta in CASP12},

author = {Sergey Ovchinnikov, Hahnbeom Park, David E. Kim, Frank DiMaio, David Baker},

url = {https://onlinelibrary.wiley.com/doi/epdf/10.1002/prot.25390

https://www.bakerlab.org/wp-content/uploads/2019/10/Ovchinnikov_et_al-2018-Proteins__Structure_Function_and_Bioinformatics.pdf},

doi = {10.1002/prot.25390},

year  = {2017},

date = {2017-09-22},

journal = {Proteins},

abstract = {We describe several notable aspects of our structure predictions using Rosetta in CASP12 in the free modeling (FM) and refinement (TR) categories. First, we had previously generated (and published) models for most large protein families lacking experimentally determined structures usingRosetta guided by co-evolution based contact predictions, and for several targets these models proved better starting points for comparative modeling than any known crystal structure—our model database thus starts to fulfill one of the goals of the original protein structure initiative. Second, while our“human”group simply submitted ROBETTA models for most targets, for six targets expert intervention improved predictions considerably; the largest improvement was for T0886where we correctly parsed two discontinuous domains guided by predicted contact maps to accurately identify a structural homolog of the same fold. Third, Rosetta all atom refinement followed by MD simulations led to consistent but small improvements when starting models were close to the native structure, and larger but less consistent improvements when starting models were further away.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bick2017,

title = {Computational design of environmental sensors for the potent opioid fentanyl},

author = {Bick, Matthew J* and Greisen, Per J* and Morey, Kevin J and Antunes, Mauricio S and La, David and Sankaran, Banumathi and Reymond, Luc and Johnsson, Kai and Medford, June I and Baker, David},

editor = {Cravatt, Benjamin F},

url = {https://elifesciences.org/articles/28909

https://www.bakerlab.org/wp-content/uploads/2018/06/elife-28909-v2-1.pdf},

doi = {10.7554/eLife.28909},

isbn = {2050-084X},

year  = {2017},

date = {2017-09-19},

journal = {eLife Sciences Publications},

volume = {6},

pages = {e28909},

abstract = {We describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{1000,

title = {Origins of coevolution between residues distant in protein 3D structures},

author = {I Anishchenko and S Ovchinnikov and H Kamisetty and D Baker},

editor = {August 22, 2017},

url = {http://www.pnas.org/content/114/34/9122

https://www.bakerlab.org/wp-content/uploads/2018/08/9122.full1_.pdf},

doi = {10.1073/pnas.1702664114},

year  = {2017},

date = {2017-08-22},

journal = {Proceedings of the National Academy of Sciences},

volume = {114},

number = {34},

pages = {9122-9127},

abstract = {Residue pairs that directly coevolve in protein families are generally close in protein 3D structures. Here we study the exceptions to this general trend—directly coevolving residue pairs that are distant in protein structures—to determine the origins of evolutionary pressure on spatially distant residues and to understand the sources of error in contact-based structure prediction. Over a set of 4,000 protein families, we find that 25% of directly coevolving residue pairs are separated by more than 5 Å in protein structures and 3% by more than 15 Å. The majority (91%) of directly coevolving residue pairs in the 5–15 Å range are found to be in contact in at least one homologous structure—these exceptions arise from structural variation in the family in the region containing the residues. Thirty-five percent of the exceptions greater than 15 Å are at homo-oligomeric interfaces, 19% arise from family structural variation, and 27% are in repeat proteins likely reflecting alignment errors. Of the remaining long-range exceptions (<1% of the total number of coupled pairs), many can be attributed to close interactions in an oligomeric state. Overall, the results suggest that directly coevolving residue pairs not in repeat proteins are spatially proximal in at least one biologically relevant protein conformation within the family; we find little evidence for direct coupling between residues at spatially separated allosteric and functional sites or for increased direct coupling between residue pairs on putative allosteric pathways connecting them.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lin2017,

title = {Cyclic oligomer design with de novo αβ-proteins},

author = {Yu-Ru Lin and Nobuyasu Koga and Sergey M. Vorobiev and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2018/06/Lin_et_al-2017-Protein_Science.pdf

http://onlinelibrary.wiley.com/doi/10.1002/pro.3270/full},

doi = {10.1002/pro.3270},

year  = {2017},

date = {2017-08-12},

journal = {Protein Science},

abstract = {We have previously shown that monomeric globular αβ- proteins can be designed de novo with considerable control over topology, size and shape. In this paper, we investigate the design of cyclic homo-oligomers from these starting points. We experimented with both keeping the original monomer backbones fixed during the cyclic docking and design process, and allowing the backbone of the monomer to conform to that of adjacent subunits in the homo-oligomer. The latter flexible backbone protocol generated designs with shape complementarity approaching that of native homo-oligomers, but experimental characterization showed that the fixed backbone designs were more stable and less aggregation prone. C2 homo-oligomers with β- strand backbone interactions were designed using both fixed and flexible backbone protocols. Designed C2 oligomers were structurally confirmed through x-ray crystallography and small-angle X-ray scattering (SAXS). In contrast, C3-C5 designed homo-oligomers with primarily nonpolar residues at interfaces all formed a range of oligomeric states. Taken together, our results suggest that for homo-oligomers formed from globular building blocks, improved structural specificity will be better achieved using monomers with increased shape complementarity and with more polar interfaces. This article is protected by copyright. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{433b,

title = {Global analysis of protein folding using massively parallel design, synthesis, and testing},

author = {GJ Rocklin and TM Chidyausiku and I Goreshnik and A Ford and S Houliston and A Lemak and L Carter and R Ravichandran and VK Mulligan and A Chevalier and CH Arrowsmith and D Baker},

url = {http://science.sciencemag.org/content/357/6347/168.full?ijkey=/u00BDqfiTTGY&keytype=ref&siteid=sci

https://www.bakerlab.org/wp-content/uploads/2017/12/Science_Rocklin_etal_2017.pdf},

doi = {10.1126/science.aan0693},

year  = {2017},

date = {2017-07-14},

journal = {Science},

volume = {357},

pages = {168-175},

abstract = {Proteins fold into unique native structures stabilized by thousands of weak interactions that collectively overcome the entropic cost of folding. Although these forces are “encoded” in the thousands of known protein structures, “decoding” them is challenging because of the complexity of natural proteins that have evolved for function, not stability. We combined computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for more than 15,000 de novo designed miniproteins, 1000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences. This analysis identified more than 2500 stable designed proteins in four basic folds—a number sufficient to enable us to systematically examine how sequence determines folding and stability in uncharted protein space. Iteration between design and experiment increased the design success rate from 6% to 47%, produced stable proteins unlike those found in nature for topologies where design was initially unsuccessful, and revealed subtle contributions to stability as designs became increasingly optimized. Our approach achieves the long-standing goal of a tight feedback cycle between computation and experiment and has the potential to transform computational protein design into a data-driven science.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Strauch2017,

title = {Computational design of trimeric influenza-neutralizing proteins targeting the hemagglutinin receptor binding site},

author = {Strauch, Eva-Maria and Bernard, Steffen M and La, David and Bohn, Alan J and Lee, Peter S and Anderson, Caitlin E and Nieusma, Travis and Holstein, Carly A and Garcia, Natalie K and Hooper, Kathryn A and Ravichandran, Rashmi and Nelson, Jorgen W and Sheffler, William and Bloom, Jesse D and Lee, Kelly K and Ward, Andrew B and Yager, Paul and Fuller, Deborah H and Wilson, Ian A and Baker, David},

url = {https://www.bakerlab.org/wp-content/uploads/2017/06/Strauch_NatureBiotech_2017.pdf

https://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3907.html},

doi = {10.1038/nbt.3907},

issn = {1546-1696},

year  = {2017},

date = {2017-06-12},

journal = {Nature Biotechnology},

volume = {[Epub ahead of print]},

abstract = {Many viral surface glycoproteins and cell surface receptors are homo-oligomers, and thus can potentially be targeted by geometrically matched homo-oligomers that engage all subunits simultaneously to attain high avidity and/or lock subunits together. The adaptive immune system cannot generally employ this strategy since the individual antibody binding sites are not arranged with appropriate geometry to simultaneously engage multiple sites in a single target homo-oligomer. We describe a general strategy for the computational design of homo-oligomeric protein assemblies with binding functionality precisely matched to homo-oligomeric target sites. In the first step, a small protein is designed that binds a single site on the target. In the second step, the designed protein is assembled into a homo-oligomer such that the designed binding sites are aligned with the target sites. We use this approach to design high-avidity trimeric proteins that bind influenza A hemagglutinin (HA) at its conserved receptor binding site. The designed trimers can both capture and detect HA in a paper-based diagnostic format, neutralizes influenza in cell culture, and completely protects mice when given as a single dose 24 h before or after challenge with influenza.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}