@article{Dowling2024,

title = {Hierarchical design of pseudosymmetric protein nanocages},

author = {Quinton M. Dowling and Young-Jun Park and Chelsea N. Fries and Neil C. Gerstenmaier and Sebastian Ols and Erin C. Yang and Adam J. Wargacki and Annie Dosey and Yang Hsia and Rashmi Ravichandran and Carl D. Walkey and Anika L. Burrell and David Veesler and David Baker and Neil P. King},

url = {https://www.nature.com/articles/s41586-024-08360-6, Nature [Open Access]},

doi = {10.1038/s41586-024-08360-6},

year  = {2024},

date = {2024-12-18},

urldate = {2024-12-18},

journal = {Nature},

publisher = {Springer Science and Business Media LLC},

abstract = {Discrete protein assemblies ranging from hundreds of kilodaltons to hundreds of megadaltons in size are a ubiquitous feature of biological systems and perform highly specialized functions. Despite remarkable recent progress in accurately designing new self-assembling proteins, the size and complexity of these assemblies has been limited by a reliance on strict symmetry. Here, inspired by the pseudosymmetry observed in bacterial microcompartments and viral capsids, we developed a hierarchical computational method for designing large pseudosymmetric self-assembling protein nanomaterials. We computationally designed pseudosymmetric heterooligomeric components and used them to create discrete, cage-like protein assemblies with icosahedral symmetry containing 240, 540 and 960 subunits. At 49, 71 and 96 nm diameter, these nanocages are the largest bounded computationally designed protein assemblies generated to date. More broadly, by moving beyond strict symmetry, our work substantially broadens the variety of self-assembling protein architectures that are accessible through design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2024b,

title = {Four-component protein nanocages designed by programmed symmetry breaking},

author = {Sangmin Lee and Ryan D. Kibler and Green Ahn and Yang Hsia and Andrew J. Borst and Annika Philomin and Madison A. Kennedy and Buwei Huang and Barry Stoddard and David Baker},

url = {https://www.nature.com/articles/s41586-024-07814-1, Nature [Open Access]},

doi = {10.1038/s41586-024-07814-1},

year  = {2024},

date = {2024-12-18},

urldate = {2024-12-18},

journal = {Nature},

publisher = {Springer Science and Business Media LLC},

abstract = {Four, eight or twenty C3 symmetric protein trimers can be arranged with tetrahedral, octahedral or icosahedral point group symmetry to generate closed cage-like structures1,2. Viruses access more complex higher triangulation number icosahedral architectures by breaking perfect point group symmetry3,4,5,6,7,8,9, but nature appears not to have explored similar symmetry breaking for tetrahedral or octahedral symmetries. Here we describe a general design strategy for building higher triangulation number architectures starting from regular polyhedra through pseudosymmetrization of trimeric building blocks. Electron microscopy confirms the structures of T = 4 cages with 48 (tetrahedral), 96 (octahedral) and 240 (icosahedral) subunits, each with 4 distinct chains and 6 different protein–protein interfaces, and diameters of 33 nm, 43 nm and 75 nm, respectively. Higher triangulation number viruses possess very sophisticated functionalities; our general route to higher triangulation number nanocages should similarly enable a next generation of multiple antigen-displaying vaccine candidates10,11 and targeted delivery vehicles12,13.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kibler2024,

title = {Design of pseudosymmetric protein hetero-oligomers},

author = {Ryan D. Kibler and Sangmin Lee and Madison A. Kennedy and Basile I. M. Wicky and Stella M. Lai and Marius M. Kostelic and Ann Carr and Xinting Li and Cameron M. Chow and Tina K. Nguyen and Lauren Carter and Vicki H. Wysocki and Barry L. Stoddard and David Baker},

url = {https://www.nature.com/articles/s41467-024-54913-8, Nature Communications [Open Access]},

doi = {10.1038/s41467-024-54913-8},

year  = {2024},

date = {2024-12-18},

urldate = {2024-12-00},

journal = {Nature Communications},

publisher = {Springer Science and Business Media LLC},

abstract = {Pseudosymmetric hetero-oligomers with three or more unique subunits with overall structural (but not sequence) symmetry play key roles in biology, and systematic approaches for generating such proteins de novo would provide new routes to controlling cell signaling and designing complex protein materials. However, the de novo design of protein hetero-oligomers with three or more distinct chains with nearly identical structures is a challenging unsolved problem because it requires the accurate design of multiple protein-protein interfaces simultaneously. Here, we describe a divide-and-conquer approach that breaks the multiple-interface design challenge into a set of more tractable symmetric single-interface redesign tasks, followed by structural recombination of the validated homo-oligomers into pseudosymmetric hetero-oligomers. Starting from de novo designed circular homo-oligomers composed of 9 or 24 tandemly repeated units, we redesigned the inter-subunit interfaces to generate 19 new homo-oligomers and structurally recombined them to make 24 new hetero-oligomers, including ABC heterotrimers, A2B2 heterotetramers, and A3B3 and A2B2C2 heterohexamers which assemble with high structural specificity. The symmetric homo-oligomers and pseudosymmetric hetero-oligomers generated for each system have identical or nearly identical backbones, and hence are ideal building blocks for generating and functionalizing larger symmetric and pseudosymmetric assemblies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Glögl2024,

title = {Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists},

author = {Matthias Glögl and Aditya Krishnakumar and Robert J. Ragotte and Inna Goreshnik and Brian Coventry and Asim K. Bera and Alex Kang and Emily Joyce and Green Ahn and Buwei Huang and Wei Yang and Wei Chen and Mariana Garcia Sanchez and Brian Koepnick and David Baker},

url = {https://www.science.org/doi/abs/10.1126/science.adp1779, Science

https://www.bakerlab.org/wp-content/uploads/2024/12/Glogl-et-al-Science-Diffusion-of-TNF-binders.pdf, PDF},

doi = {10.1126/science.adp1779},

year  = {2024},

date = {2024-12-06},

urldate = {2024-12-06},

journal = {Science},

publisher = {American Association for the Advancement of Science (AAAS)},

abstract = {Despite progress in designing protein-binding proteins, the shape matching of designs to targets is lower than in many native protein complexes, and design efforts have failed for the tumor necrosis factor receptor 1 (TNFR1) and other protein targets with relatively flat and polar surfaces. We hypothesized that free diffusion from random noise could generate shape-matched binders for challenging targets and tested this approach on TNFR1. We obtain designs with low picomolar affinity whose specificity can be completely switched to other family members using partial diffusion. Designs function as antagonists or as superagonists when presented at higher valency for OX40 and 4-1BB. The ability to design high-affinity and high-specificity antagonists and agonists for pharmacologically important targets in silico presages a coming era in protein design in which binders are made by computation rather than immunization or random screening approaches.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Piraner2024,

title = {Engineered receptors for soluble cellular communication and disease sensing},

author = {Dan I. Piraner and Mohamad H. Abedi and Maria J. Duran Gonzalez and Adam Chazin-Gray and Annie Lin and Iowis Zhu and Pavithran T. Ravindran and Thomas Schlichthaerle and Buwei Huang and Tyler H. Bearchild and David Lee and Sarah Wyman and Young-wook Jun and David Baker and Kole T. Roybal},

url = {https://www.nature.com/articles/s41586-024-08366-0, Nature},

doi = {10.1038/s41586-024-08366-0},

year  = {2024},

date = {2024-11-14},

urldate = {2024-11-14},

journal = {Nature},

publisher = {Springer Science and Business Media LLC},

abstract = {Despite recent advances in mammalian synthetic biology, there remains a lack of modular synthetic receptors that can robustly respond to soluble ligands and in turn activate bespoke cellular functions. Such receptors would have extensive clinical potential to regulate the activity of engineered therapeutic cells, but to date only receptors against cell surface targets have approached clinical translation1. To address this gap, we developed a receptor architecture called synthetic intramembrane proteolysis receptor (SNIPR), that has the added ability to be activated by soluble ligands, both natural and synthetic, with remarkably low baseline activity and high fold activation, through an endocytic, pH-dependent cleavage mechanism. We demonstrate the therapeutic capabilities of the receptor platform by localizing the activity of CAR T cells to solid tumors where soluble disease-associated factors are expressed, bypassing the major hurdle of on-target off-tumor toxicity in bystander organs. We further applied the SNIPR platform to engineer fully synthetic signaling networks between cells orthogonal to natural signaling pathways, expanding the scope of synthetic biology. Our design framework enables cellular communication and environmental interactions, extending the capabilities of synthetic cellular networking in clinical and research contexts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huang2024b,

title = {Designed endocytosis-inducing proteins degrade targets and amplify signals},

author = {Buwei Huang and Mohamad Abedi and Green Ahn and Brian Coventry and Isaac Sappington and Cong Tang and Rong Wang and Thomas Schlichthaerle and Jason Z. Zhang and Yujia Wang and Inna Goreshnik and Ching Wen Chiu and Adam Chazin-Gray and Sidney Chan and Stacey Gerben and Analisa Murray and Shunzhi Wang and Jason O’Neill and Li Yi and Ronald Yeh and Ayesha Misquith and Anitra Wolf and Luke M. Tomasovic and Dan I. Piraner and Maria J. Duran Gonzalez and Nathaniel R. Bennett and Preetham Venkatesh and Maggie Ahlrichs and Craig Dobbins and Wei Yang and Xinru Wang and Danny D. Sahtoe and Dionne Vafeados and Rubul Mout and Shirin Shivaei and Longxing Cao and Lauren Carter and Lance Stewart and Jamie B. Spangler and Kole T. Roybal and Per Jr Greisen and Xiaochun Li and Gonçalo J. L. Bernardes and Carolyn R. Bertozzi and David Baker},

url = {https://www.nature.com/articles/s41586-024-07948-2, Nature [Open Access] },

doi = {10.1038/s41586-024-07948-2},

year  = {2024},

date = {2024-09-25},

urldate = {2024-09-25},

journal = {Nature},

publisher = {Springer Science and Business Media LLC},

abstract = {Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by endogenous ligands. Therapeutic approaches such as lysosome-targeting chimaeras1,2 (LYTACs) and cytokine receptor-targeting chimeras3 (KineTACs) have used this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. Although powerful, these approaches can be limited by competition with native ligands and requirements for chemical modification that limit genetic encodability and can complicate manufacturing, and, more generally, there may be no native ligands that stimulate endocytosis through a given receptor. Here we describe computational design approaches for endocytosis-triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for insulin-like growth factor 2 receptor (IGF2R) and asialoglycoprotein receptor (ASGPR), sortilin and transferrin receptors, and show that fusing these tags to soluble or transmembrane target protein binders leads to lysosomal trafficking and target degradation. As these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. EndoTag fusion to a PD-L1 antibody considerably increases efficacy in a mouse tumour model compared to antibody alone. The modularity and genetic encodability of EndoTags enables AND gate control for higher-specificity targeted degradation, and the localized secretion of degraders from engineered cells. By promoting endocytosis, EndoTag fusion increases signalling through an engineered ligand–receptor system by nearly 100-fold. EndoTags have considerable therapeutic potential as targeted degradation inducers, signalling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody–drug and antibody–RNA conjugates.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lisanza2024,

title = {Multistate and functional protein design using RoseTTAFold sequence space diffusion},

author = {Sidney Lyayuga Lisanza and Jacob Merle Gershon and Samuel W. K. Tipps and Jeremiah Nelson Sims and Lucas Arnoldt and Samuel J. Hendel and Miriam K. Simma and Ge Liu and Muna Yase and Hongwei Wu and Claire D. Tharp and Xinting Li and Alex Kang and Evans Brackenbrough and Asim K. Bera and Stacey Gerben and Bruce J. Wittmann and Andrew C. McShan and David Baker},

url = {https://www.nature.com/articles/s41587-024-02395-w, Nature Biotechnology [Open Access]},

doi = {10.1038/s41587-024-02395-w},

year  = {2024},

date = {2024-09-25},

urldate = {2024-09-25},

journal = {Nature Biotechnology},

publisher = {Springer Science and Business Media LLC},

abstract = {Protein denoising diffusion probabilistic models are used for the de novo generation of protein backbones but are limited in their ability to guide generation of proteins with sequence-specific attributes and functional properties. To overcome this limitation, we developed ProteinGenerator (PG), a sequence space diffusion model based on RoseTTAFold that simultaneously generates protein sequences and structures. Beginning from a noised sequence representation, PG generates sequence and structure pairs by iterative denoising, guided by desired sequence and structural protein attributes. We designed thermostable proteins with varying amino acid compositions and internal sequence repeats and cage bioactive peptides, such as melittin. By averaging sequence logits between diffusion trajectories with distinct structural constraints, we designed multistate parent–child protein triples in which the same sequence folds to different supersecondary structures when intact in the parent versus split into two child domains. PG design trajectories can be guided by experimental sequence–activity data, providing a general approach for integrated computational and experimental optimization of protein function.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Humphreys2024,

title = {Protein interactions in human pathogens revealed through deep learning},

author = {Ian R. Humphreys and Jing Zhang and Minkyung Baek and Yaxi Wang and Aditya Krishnakumar and Jimin Pei and Ivan Anishchenko and Catherine A. Tower and Blake A. Jackson and Thulasi Warrier and Deborah T. Hung and S. Brook Peterson and Joseph D. Mougous and Qian Cong and David Baker},

url = {https://www.nature.com/articles/s41564-024-01791-x, Nature Microbiology [Open Access]},

doi = {10.1038/s41564-024-01791-x},

issn = {2058-5276},

year  = {2024},

date = {2024-09-18},

urldate = {2024-09-18},

journal = {Nature Microbiology},

publisher = {Springer Science and Business Media LLC},

abstract = {Identification of bacterial protein–protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lite, a rapid deep learning model that leverages residue–residue coevolution and protein structure prediction to systematically identify and structurally characterize protein–protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1,923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer-membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Moyer2024,

title = {Enumerative Discovery of Noncanonical Polypeptide Secondary Structures},

author = {Adam P. Moyer and Theresa A. Ramelot and Mariano Curti and Margaret A. Eastman and Alex Kang and Asim K. Bera and Roberto Tejero and Patrick J. Salveson and Carles Curutchet and Elisabet Romero and Gaetano T. Montelione and David Baker},

url = {https://pubs.acs.org/doi/full/10.1021/jacs.4c04991, LACS [Open Access]},

doi = {10.1021/jacs.4c04991},

year  = {2024},

date = {2024-09-18},

urldate = {2024-09-18},

journal = {Journal of the American Chemical Society},

publisher = {American Chemical Society (ACS)},

abstract = {Energetically favorable local interactions can overcome the entropic cost of chain ordering and cause otherwise flexible polymers to adopt regularly repeating backbone conformations. A prominent example is the α helix present in many protein structures, which is stabilized by i, i + 4 hydrogen bonds between backbone peptide units. With the increased chemical diversity offered by unnatural amino acids and backbones, it has been possible to identify regularly repeating structures not present in proteins, but to date, there has been no systematic approach for identifying new polymers likely to have such structures despite their considerable potential for molecular engineering. Here we describe a systematic approach to search through dipeptide combinations of 130 chemically diverse amino acids to identify those predicted to populate unique low-energy states. We characterize ten newly identified dipeptide repeating structures using circular dichroism spectroscopy and comparison with calculated spectra. NMR and X-ray crystallographic structures of two of these dipeptide-repeat polymers are similar to the computational models. Our approach is readily generalizable to identify low-energy repeating structures for a wide variety of polymers, and our ordered dipeptide repeats provide new building blocks for molecular engineering.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huang2024,

title = {De novo design of miniprotein antagonists of cytokine storm inducers},

author = {Buwei Huang and Brian Coventry and Marta T. Borowska and Dimitrios C. Arhontoulis and Marc Exposit and Mohamad Abedi and Kevin M. Jude and Samer F. Halabiya and Aza Allen and Cami Cordray and Inna Goreshnik and Maggie Ahlrichs and Sidney Chan and Hillary Tunggal and Michelle DeWitt and Nathaniel Hyams and Lauren Carter and Lance Stewart and Deborah H. Fuller and Ying Mei and K. Christopher Garcia and David Baker},

url = {https://www.nature.com/articles/s41467-024-50919-4, Nature Communications [Open Access]},

doi = {10.1038/s41467-024-50919-4},

year  = {2024},

date = {2024-08-16},

urldate = {2024-08-16},

journal = {Nature Communications},

publisher = {Springer Science and Business Media LLC},

abstract = {Cytokine release syndrome (CRS), commonly known as cytokine storm, is an acute systemic inflammatory response that is a significant global health threat. Interleukin-6 (IL-6) and interleukin-1 (IL-1) are key pro-inflammatory cytokines involved in CRS and are hence critical therapeutic targets. Current antagonists, such as tocilizumab and anakinra, target IL-6R/IL-1R but have limitations due to their long half-life and systemic anti-inflammatory effects, making them less suitable for acute or localized treatments. Here we present the de novo design of small protein antagonists that prevent IL-1 and IL-6 from interacting with their receptors to activate signaling. The designed proteins bind to the IL-6R, GP130 (an IL-6 co-receptor), and IL-1R1 receptor subunits with binding affinities in the picomolar to low-nanomolar range. X-ray crystallography studies reveal that the structures of these antagonists closely match their computational design models. In a human cardiac organoid disease model, the IL-1R antagonists demonstrated protective effects against inflammation and cardiac damage induced by IL-1β. These minibinders show promise for administration via subcutaneous injection or intranasal/inhaled routes to mitigate acute cytokine storm effects.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pillai2024,

title = {De novo design of allosterically switchable protein assemblies},

author = {Arvind Pillai and Abbas Idris and Annika Philomin and Connor Weidle and Rebecca Skotheim and Philip J. Y. Leung and Adam Broerman and Cullen Demakis and Andrew J. Borst and Florian Praetorius and David Baker},

url = {https://www.nature.com/articles/s41586-024-07813-2, Nature [Open Access]},

doi = {10.1038/s41586-024-07813-2},

year  = {2024},

date = {2024-08-14},

urldate = {2024-08-14},

journal = {Nature},

publisher = {Springer Science and Business Media LLC},

abstract = {Allosteric modulation of protein function, wherein the binding of an effector to a protein triggers conformational changes at distant functional sites, plays a central part in the control of metabolism and cell signalling. There has been considerable interest in designing allosteric systems, both to gain insight into the mechanisms underlying such ‘action at a distance’ modulation and to create synthetic proteins whose functions can be regulated by effectors. However, emulating the subtle conformational changes distributed across many residues, characteristic of natural allosteric proteins, is a significant challenge. Here, inspired by the classic Monod–Wyman–Changeux model of cooperativity, we investigate the de novo design of allostery through rigid-body coupling of peptide-switchable hinge modules to protein interfaces that direct the formation of alternative oligomeric states. We find that this approach can be used to generate a wide variety of allosterically switchable systems, including cyclic rings that incorporate or eject subunits in response to peptide binding and dihedral cages that undergo effector-induced disassembly. Size-exclusion chromatography, mass photometry and electron microscopy reveal that these designed allosteric protein assemblies closely resemble the design models in both the presence and absence of peptide effectors and can have ligand-binding cooperativity comparable to classic natural systems such as haemoglobin. Our results indicate that allostery can arise from global coupling of the energetics of protein substructures without optimized side-chain–side-chain allosteric communication pathways and provide a roadmap for generating allosterically triggerable delivery systems, protein nanomachines and cellular feedback control circuitry.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{An2024,

title = {Binding and sensing diverse small molecules using shape-complementary pseudocycles},

author = {Linna An and Meerit Said and Long Tran and Sagardip Majumder and Inna Goreshnik and Gyu Rie Lee and David Juergens and Justas Dauparas and Ivan Anishchenko and Brian Coventry and Asim K. Bera and Alex Kang and Paul M. Levine and Valentina Alvarez and Arvind Pillai and Christoffer Norn and David Feldman and Dmitri Zorine and Derrick R. Hicks and Xinting Li and Mariana Garcia Sanchez and Dionne K. Vafeados and Patrick J. Salveson and Anastassia A. Vorobieva and David Baker},

url = {https://www.science.org/doi/10.1126/science.adn3780, Science},

doi = {10.1126/science.adn3780},

year  = {2024},

date = {2024-07-19},

urldate = {2024-07-19},

journal = {Science},

publisher = {American Association for the Advancement of Science (AAAS)},

abstract = {We describe an approach for designing high-affinity small molecule–binding proteins poised for downstream sensing. We use deep learning–generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Berhanu2024,

title = {Sculpting conducting nanopore size and shape through de novo protein design},

author = {Samuel Berhanu and Sagardip Majumder and Thomas Müntener and James Whitehouse and Carolin Berner and Asim K. Bera and Alex Kang and Binyong Liang and Nasir Khan and Banumathi Sankaran and Lukas K. Tamm and David J. Brockwell and Sebastian Hiller and Sheena E. Radford and David Baker and Anastassia A. Vorobieva},

url = {https://www.science.org/doi/10.1126/science.adn3796, Science},

doi = {10.1126/science.adn3796},

year  = {2024},

date = {2024-07-19},

urldate = {2024-07-19},

journal = {Science},

publisher = {American Association for the Advancement of Science (AAAS)},

abstract = {Transmembrane β-barrels have considerable potential for a broad range of sensing applications. Current engineering approaches for nanopore sensors are limited to naturally occurring channels, which provide suboptimal starting points. By contrast, de novo protein design can in principle create an unlimited number of new nanopores with any desired properties. Here we describe a general approach to designing transmembrane β-barrel pores with different diameters and pore geometries. Nuclear magnetic resonance and crystallographic characterization show that the designs are stably folded with structures resembling those of the design models. The designs have distinct conductances that correlate with their pore diameter, ranging from 110 picosiemens (~0.5 nanometer pore diameter) to 430 picosiemens (~1.1 nanometer pore diameter). Our approach opens the door to the custom design of transmembrane nanopores for sensing and sequencing applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Berger2024,

title = {Preclinical proof of principle for orally delivered Th17 antagonist miniproteins},

author = {Stephanie Berger and Franziska Seeger and Ta-Yi Yu and Merve Aydin and Huilin Yang and Daniel Rosenblum and Laure Guenin-Macé and Caleb Glassman and Lauren Arguinchona and Catherine Sniezek and Alyssa Blackstone and Lauren Carter and Rashmi Ravichandran and Maggie Ahlrichs and Michael Murphy and Ingrid Swanson Pultz and Alex Kang and Asim K. Bera and Lance Stewart and K. Christopher Garcia and Shruti Naik and Jamie B. Spangler and Florian Beigel and Matthias Siebeck and Roswitha Gropp and David Baker},

url = {https://www.cell.com/cell/fulltext/S0092-8674(24)00631-7, Cell [Open Access]},

doi = {10.1016/j.cell.2024.05.052},

year  = {2024},

date = {2024-06-26},

urldate = {2024-06-00},

journal = {Cell},

publisher = {Elsevier BV},

abstract = {Interleukin (IL)-23 and IL-17 are well-validated therapeutic targets in autoinflammatory diseases. Antibodies targeting IL-23 and IL-17 have shown clinical efficacy but are limited by high costs, safety risks, lack of sustained efficacy, and poor patient convenience as they require parenteral administration. Here, we present designed miniproteins inhibiting IL-23R and IL-17 with antibody-like, low picomolar affinities at a fraction of the molecular size. The minibinders potently block cell signaling in vitro and are extremely stable, enabling oral administration and low-cost manufacturing. The orally administered IL-23R minibinder shows efficacy better than a clinical anti-IL-23 antibody in mouse colitis and has a favorable pharmacokinetics (PK) and biodistribution profile in rats. This work demonstrates that orally administered de novo-designed minibinders can reach a therapeutic target past the gut epithelial barrier. With high potency, gut stability, and straightforward manufacturability, de novo-designed minibinders are a promising modality for oral biologics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Edman2024,

title = {Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies},

author = {Natasha I. Edman, 

Ashish Phal, 

Rachel L. Redler, 

Thomas Schlichthaerle, 

Sanjay R. Srivatsan, 

Devon Duron Ehnes, 

Ali Etemadi, 

Seong J. An, 

Andrew Favor, 

Zhe Li, 

Florian Praetorius, 

Max Gordon, 

Thomas Vincent, 

Silvia Marchiano, 

Leslie Blakely, 

Chuwei Lin, 

Wei Yang, 

Brian Coventry, 

Derrick R. Hicks, 

Longxing Cao, 

Neville Bethel, 

Piper Heine, 

Analisa Murray, 

Stacey Gerben, 

Lauren Carter, 

Marcos Miranda, 

Babak Negahdari, 

Sangwon Lee, 

Cole Trapnell, 

Ying Zheng, 

Charles E. Murry, 

Devin K. Schweppe, 

Benjamin S. Freedman, 

Lance Stewart, 

Damian C. Ekiert, 

Joseph Schlessinger, 

Jay Shendure, 

Gira Bhabha, 

Hannele Ruohola-Baker, 

David Baker, },

url = {https://authors.elsevier.com/sd/article/S0092-8674(24)00534-8, Cell (Open Access)

https://www.bakerlab.org/wp-content/uploads/2024/06/Cell_13439_Modulation_of_FGF_pathway_signalling_2024.pdf, PDF},

doi = {10.1016/j.cell.2024.05.025},

year  = {2024},

date = {2024-06-10},

journal = {Cell},

abstract = {Many growth factors and cytokines signal by binding to the extracellular domains of their receptors and driving association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affect signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo-designed fibroblast growth factor receptor (FGFR)-binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and mitogen-activated protein kinase (MAPK) pathway activation. The high specificity of the designed agonists reveals distinct roles for two FGFR splice variants in driving arterial endothelium and perivascular cell fates during early vascular development. Our designed modular assemblies should be broadly useful for unraveling the complexities of signaling in key developmental transitions and for developing future therapeutic applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jiang2024,

title = {De novo design of buttressed loops for sculpting protein functions},

author = {Jiang, Hanlun

and Jude, Kevin M.

and Wu, Kejia

and Fallas, Jorge

and Ueda, George

and Brunette, T. J.

and Hicks, Derrick R.

and Pyles, Harley

and Yang, Aerin

and Carter, Lauren

and Lamb, Mila

and Li, Xinting

and Levine, Paul M.

and Stewart, Lance

and Garcia, K. Christopher

and Baker, David},

url = {https://www.nature.com/articles/s41589-024-01632-2, Nature Chemical Biology [Open Access]

https://www.bakerlab.org/wp-content/uploads/2024/05/s41589-024-01632-2.pdf, PDF},

doi = {10.1038/s41589-024-01632-2},

year  = {2024},

date = {2024-05-30},

urldate = {2024-05-30},

journal = {Nature Chemical Biology},

abstract = {In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9–14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2024,

title = {Computational design of non-porous pH-responsive antibody nanoparticles},

author = {Erin C. Yang and Robby Divine and Marcos C. Miranda and Andrew J. Borst and Will Sheffler and Jason Z. Zhang and Justin Decarreau and Amijai Saragovi and Mohamad Abedi and Nicolas Goldbach and Maggie Ahlrichs and Craig Dobbins and Alexis Hand and Suna Cheng and Mila Lamb and Paul M. Levine and Sidney Chan and Rebecca Skotheim and Jorge Fallas and George Ueda and Joshua Lubner and Masaharu Somiya and Alena Khmelinskaia and Neil P. King and David Baker},

url = {https://www.nature.com/articles/s41594-024-01288-5, NSMB [Open Access]

https://www.bakerlab.org/wp-content/uploads/2024/05/Yang-etal-NSMB2024-s41594-024-01288-5.pdf, PDF},

doi = {10.1038/s41594-024-01288-5},

year  = {2024},

date = {2024-05-09},

urldate = {2024-05-09},

journal = {Nature Structural & Molecular Biololgy},

publisher = {Springer Science and Business Media LLC},

abstract = {Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Craven2024,

title = {Computational Design of Cyclic Peptide Inhibitors of a Bacterial Membrane Lipoprotein Peptidase},

author = {Timothy W. Craven and Mark D. Nolan and Jonathan Bailey and Samir Olatunji and Samantha J. Bann and Katherine Bowen and Nikita Ostrovitsa and Thaina M. Da Costa and Ross D. Ballantine and Dietmar Weichert and Paul M. Levine and Lance J. Stewart and Gaurav Bhardwaj and Joan A. Geoghegan and Stephen A. Cochrane and Eoin M. Scanlan and Martin Caffrey and David Baker},

url = {https://pubs.acs.org/doi/10.1021/acschembio.4c00076, ACS Chem. Bio. [Open Access]

https://www.bakerlab.org/wp-content/uploads/2024/05/craven-et-al-2024-computational-design-of-cyclic-peptide-inhibitors-of-a-bacterial-membrane-lipoprotein-peptidase.pdf, PDF},

doi = {10.1021/acschembio.4c00076},

year  = {2024},

date = {2024-05-07},

urldate = {2024-05-07},

journal = {ACS Chemical Biology},

publisher = {American Chemical Society (ACS)},

abstract = {There remains a critical need for new antibiotics against multi-drug-resistant Gram-negative bacteria, a major global threat that continues to impact mortality rates. Lipoprotein signal peptidase II is an essential enzyme in the lipoprotein biosynthetic pathway of Gram-negative bacteria, making it an attractive target for antibacterial drug discovery. Although natural inhibitors of LspA have been identified, such as the cyclic depsipeptide globomycin, poor stability and production difficulties limit their use in a clinical setting. We harness computational design to generate stable de novo cyclic peptide analogues of globomycin. Only 12 peptides needed to be synthesized and tested to yield potent inhibitors, avoiding costly preparation of large libraries and screening campaigns. The most potent analogues showed comparable or better antimicrobial activity than globomycin in microdilution assays against ESKAPE-E pathogens. This work highlights computational design as a general strategy to combat antibiotic resistance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Salveson2024,

title = {Expansive discovery of chemically diverse structured macrocyclic oligoamides},

author = {Patrick J. Salveson and Adam P. Moyer and Meerit Y. Said and Gizem Gӧkçe and Xinting Li and Alex Kang and Hannah Nguyen and Asim K. Bera and Paul M. Levine and Gaurav Bhardwaj and David Baker},

url = {https://www.science.org/doi/abs/10.1126/science.adk1687, Science

https://www.bakerlab.org/wp-content/uploads/2024/04/Salveson-et-al-Science-2024.pdf, PDF},

doi = {10.1126/science.adk1687},

year  = {2024},

date = {2024-04-25},

urldate = {2024-01-01},

journal = {Science},

abstract = {Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shen2024,

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

author = {Hao Shen and Eric M. Lynch and Susrut Akkineni and Joseph L. Watson and Justin Decarreau and Neville P. Bethel and Issa Benna and William Sheffler and Daniel Farrell and Frank DiMaio and Emmanuel Derivery and James J. De Yoreo and Justin Kollman and David Baker},

url = {https://link.springer.com/article/10.1038/s41565-024-01641-1, Nature Nanotechnology [Open Access]

https://www.bakerlab.org/wp-content/uploads/2024/04/s41565-024-01641-1.pdf, PDF},

doi = {10.1038/s41565-024-01641-1},

year  = {2024},

date = {2024-04-03},

urldate = {2024-04-03},

journal = {Nature Nanotechnology},

publisher = {Springer Science and Business Media LLC},

abstract = {Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mansoor2024,

title = {Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling},

author = {Sanaa Mansoor and Minkyung Baek and Hahnbeom Park and Gyu Rie Lee and David Baker},

url = {https://pubs.acs.org/doi/10.1021/acs.jctc.3c01057, J. Chem. Theory Comput.

https://www.bakerlab.org/wp-content/uploads/2024/05/mansoor-et-al-2024-protein-ensemble-generation-through-variational-autoencoder-latent-space-sampling.pdf, PDF},

doi = {10.1021/acs.jctc.3c01057},

year  = {2024},

date = {2024-03-28},

urldate = {2024-04-09},

journal = {J. Chem. Theory Comput.},

publisher = {American Chemical Society (ACS)},

abstract = {Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 Å of held-out crystal structures, with a consistency higher than that of MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sahtoe2024,

title = {Design of amyloidogenic peptide traps},

author = {Danny D. Sahtoe and Ewa A. Andrzejewska and Hannah L. Han and Enrico Rennella and Matthias M. Schneider and Georg Meisl and Maggie Ahlrichs and Justin Decarreau and Hannah Nguyen and Alex Kang and Paul Levine and Mila Lamb and Xinting Li and Asim K. Bera and Lewis E. Kay and Tuomas P. J. Knowles and David Baker},

url = {https://www.nature.com/articles/s41589-024-01578-5, Nature Chemical Biology [Open Access]},

doi = {10.1038/s41589-024-01578-5},

year  = {2024},

date = {2024-03-19},

urldate = {2024-03-19},

journal = {Nature Chemical Biology},

publisher = {Springer Science and Business Media LLC},

abstract = {Segments of proteins with high β-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein−peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid β1−42 (Aβ42). The Aβ binders block the assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aβ42 species.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huddy2024,

title = {Blueprinting extendable nanomaterials with standardized protein blocks},

author = {Timothy F. Huddy and Yang Hsia and Ryan D. Kibler and Jinwei Xu and Neville Bethel and Deepesh Nagarajan and Rachel Redler and Philip J. Y. Leung and Connor Weidle and Alexis Courbet and Erin C. Yang and Asim K. Bera and Nicolas Coudray and S. John Calise and Fatima A. Davila-Hernandez and Hannah L. Han and Kenneth D. Carr and Zhe Li and Ryan McHugh and Gabriella Reggiano and Alex Kang and Banumathi Sankaran and Miles S. Dickinson and Brian Coventry and T. J. Brunette and Yulai Liu and Justas Dauparas and Andrew J. Borst and Damian Ekiert and Justin M. Kollman and Gira Bhabha and David Baker},

url = {https://www.nature.com/articles/s41586-024-07188-4, Nature [Open Access]},

doi = {10.1038/s41586-024-07188-4},

year  = {2024},

date = {2024-03-13},

urldate = {2024-03-13},

journal = {Nature},

publisher = {Springer Science and Business Media LLC},

abstract = {A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies, in comparison, has been much more complex, largely owing to the irregular shapes of protein structures. Here we describe extendable linear, curved and angled protein building blocks, as well as inter-block interactions, that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight ‘train track’ assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence–structure relationships, it has not previously been possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank three-dimensional canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to ‘back of an envelope’ architectural blueprints.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Krishna2024,

title = {Generalized biomolecular modeling and design with RoseTTAFold All-Atom},

author = {Rohith Krishna and Jue Wang and Woody Ahern and Pascal Sturmfels and Preetham Venkatesh and Indrek Kalvet and Gyu Rie Lee and Felix S. Morey-Burrows and Ivan Anishchenko and Ian R. Humphreys and Ryan McHugh and Dionne Vafeados and Xinting Li and George A. Sutherland and Andrew Hitchcock and C. Neil Hunter and Alex Kang and Evans Brackenbrough and Asim K. Bera and Minkyung Baek and Frank DiMaio and David Baker},

url = {https://www.science.org/stoken/author-tokens/ST-1739/full, Science [Full Access Link]

https://www.bakerlab.org/wp-content/uploads/2024/03/science.adl2528.pdf, PDF},

doi = {10.1126/science.adl2528},

year  = {2024},

date = {2024-03-07},

urldate = {2024-03-07},

journal = {Science},

publisher = {American Association for the Advancement of Science (AAAS)},

abstract = {Deep learning methods have revolutionized protein structure prediction and design but are currently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA) which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications given their sequences and chemical structures. By fine tuning on denoising tasks we obtain RFdiffusionAA, which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we design and experimentally validate, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light harvesting molecule bilin.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mout2024,

title = {De novo design of modular protein hydrogels with programmable intra- and extracellular viscoelasticity},

author = {Rubul Mout, Ross C. Bretherton, Justin Decarreau, Sangmin Lee, Nicole Gregorio, Natasha I. Edman, Maggie Ahlrichs, Yang Hsia, Danny D. Sahtoe, George Ueda, Alee Sharma, Rebecca Schulman, Cole A. DeForest, David Baker},

url = {https://www.pnas.org/doi/full/10.1073/pnas.2309457121, PNAS [Open Access]},

doi = {10.1073/pnas.2309457121},

year  = {2024},

date = {2024-01-30},

urldate = {2024-01-30},

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

abstract = {Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in viscoelastic biomaterials exhibiting fluid-like properties under rest and low shear, but solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly in a manner similar to formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baker2024,

title = {Protein design meets biosecurity},

author = {David Baker and George Church},

url = {https://www.science.org/doi/10.1126/science.ado1671, Science [Open Access]

https://www.bakerlab.org/wp-content/uploads/2024/04/Baker-Church-Protein-design-meets-biosecurity-Science-25-Jan-2024.pdf, PDF},

doi = {10.1126/science.ado1671},

year  = {2024},

date = {2024-01-26},

urldate = {2024-01-26},

journal = {Science},

publisher = {American Association for the Advancement of Science (AAAS)},

abstract = {The power and accuracy of computational protein design have been increasing rapidly with the incorporation of artificial intelligence (AI) approaches. This promises to transform biotechnology, enabling advances across sustainability and medicine. DNA synthesis plays a critical role in materializing designed proteins. However, as with all major revolutionary changes, this technology is vulnerable to misuse and the production of dangerous biological agents. To enable the full benefits of this revolution while mitigating risks that may emerge, all synthetic gene sequence and synthesis data should be collected and stored in repositories that are only queried in emergencies to ensure that protein design proceeds in a safe, secure, and trustworthy manner.},

howpublished = {Science},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2024,

title = {Computationally designed sensors detect endogenous Ras activity and signaling effectors at subcellular resolution},

author = {Jason Zhang, William Nguyen, Nathan Greenwood, John Rose, Shao-En Ong, Dustin Maly, David Baker},

url = {https://www.nature.com/articles/s41587-023-02107-w, Nature Biotechnology [Open Access]},

doi = {10.1038/s41587-023-02107-w},

year  = {2024},

date = {2024-01-25},

journal = {Nature Biotechnology},

abstract = {The utility of genetically encoded biosensors for sensing the activity of signaling proteins has been hampered by a lack of strategies for matching sensor sensitivity to the physiological concentration range of the target. Here we used computational protein design to generate intracellular sensors of Ras activity (LOCKR-based Sensor for Ras activity (Ras-LOCKR-S)) and proximity labelers of the Ras signaling environment (LOCKR-based, Ras activity-dependent Proximity Labeler (Ras-LOCKR-PL)). These tools allow the detection of endogenous Ras activity and labeling of the surrounding environment at subcellular resolution. Using these sensors in human cancer cell lines, we identified Ras-interacting proteins in oncogenic EML4-Alk granules and found that Src-Associated in Mitosis 68-kDa (SAM68) protein specifically enhances Ras activity in the granules. The ability to subcellularly localize endogenous Ras activity should deepen our understanding of Ras function in health and disease and may suggest potential therapeutic strategies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sumida2024,

title = {Improving Protein Expression, Stability, and Function with ProteinMPNN},

author = {Kiera H Sumida and Reyes Núñez-Franco and Indrek Kalvet and Samuel J Pellock and Basile I M Wicky and Lukas F Milles and Justas Dauparas and Jue Wang and Yakov Kipnis and Noel Jameson and Alex Kang and Joshmyn De La Cruz and Banumathi Sankaran and Asim K Bera and Gonzalo Jiménez-Osés and David Baker},

url = {https://pubs.acs.org/doi/10.1021/jacs.3c10941, JACS [Open Access]},

doi = {10.1021/jacs.3c10941},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {JACS},

abstract = {Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{King2024,

title = {Computational Stabilization of a Non‐Heme Iron Enzyme Enables Efficient Evolution of New Function},

author = {Brianne R. King and Kiera H. Sumida and Jessica L. Caruso and David Baker and Jesse G. Zalatan},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202414705, Angew Chem Int Ed

https://www.bakerlab.org/wp-content/uploads/2025/02/Angew-Chem-Int-Ed-2024-King-Computational-Stabilization-of-a-Non‐Heme-Iron-Enzyme-Enables-Efficient-Evolution-of-New.pdf, PDF},

doi = {10.1002/anie.202414705},

issn = {1521-3773},

year  = {2024},

date = {2024-10-12},

urldate = {2025-01-10},

journal = {Angew Chem Int Ed},

publisher = {Wiley},

abstract = {Deep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non-native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non-native, remote C(sp3)−H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild-type enzyme. After three rounds of directed evolution, we obtained a 6-fold activity increase from the wild-type parent and an 80-fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user-friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning-based protein stabilization tools in directed evolution workflows for novel biocatalysts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Baryshev2024,

title = {Massively parallel measurement of protein–protein interactions by sequencing using MP3-seq},

author = {Alexandr Baryshev and Alyssa La Fleur and Benjamin Groves and Cirstyn Michel and David Baker and Ajasja Ljubetič and Georg Seelig},

url = {https://www.nature.com/articles/s41589-024-01718-x, Nature Chemical Biology [Open Access]},

doi = {10.1038/s41589-024-01718-x},

year  = {2024},

date = {2024-08-27},

urldate = {2024-08-27},

journal = {Nature Chemical Biology},

publisher = {Springer Science and Business Media LLC},

abstract = {Protein–protein interactions (PPIs) regulate many cellular processes and engineered PPIs have cell and gene therapy applications. Here, we introduce massively parallel PPI measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Lastly, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics-based energy terms to predict MP3-seq values. We find that AF-M-based models could be valuable for prescreening interactions but experimentally measuring interactions remains necessary to rank their strengths quantitatively.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2024,

title = {Accurate de novo design of heterochiral protein–protein interactions},

author = {Ke Sun and Sicong Li and Bowen Zheng and Yanlei Zhu and Tongyue Wang and Mingfu Liang and Yue Yao and Kairan Zhang and Jizhong Zhang and Hongyong Li and Dongyang Han and Jishen Zheng and Brian Coventry and Longxing Cao and David Baker and Lei Liu and Peilong Lu},

url = {https://www.nature.com/articles/s41422-024-01014-2, Cell Research [Open Access]},

doi = {10.1038/s41422-024-01014-2},

year  = {2024},

date = {2024-08-14},

urldate = {2024-08-14},

journal = {Cell Research},

publisher = {Springer Science and Business Media LLC},

abstract = {Abiotic d-proteins that selectively bind to natural l-proteins have gained significant biotechnological interest. However, the underlying structural principles governing such heterochiral protein–protein interactions remain largely unknown. In this study, we present the de novo design of d-proteins consisting of 50–65 residues, aiming to target specific surface regions of l-proteins or l-peptides. Our designer d-protein binders exhibit nanomolar affinity toward an artificial l-peptide, as well as two naturally occurring proteins of therapeutic significance: the D5 domain of human tropomyosin receptor kinase A (TrkA) and human interleukin-6 (IL-6). Notably, these d-protein binders demonstrate high enantiomeric specificity and target specificity. In cell-based experiments, designer d-protein binders effectively inhibited the downstream signaling of TrkA and IL-6 with high potency. Moreover, these binders exhibited remarkable thermal stability and resistance to protease degradation. Crystal structure of the designed heterochiral d-protein–l-peptide complex, obtained at a resolution of 2.0 Å, closely resembled the design model, indicating that the computational method employed is highly accurate. Furthermore, the crystal structure provides valuable information regarding the interactions between helical l-peptides and d-proteins, particularly elucidating a novel mode of heterochiral helix–helix interactions. Leveraging the design of d-proteins specifically targeting l-peptides or l-proteins opens up avenues for systematic exploration of the mirror-image protein universe, paving the way for a diverse range of applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2024b,

title = {Single-cell sensor analyses reveal signaling programs enabling Ras-G12C drug resistance},

author = {Jason Z. Zhang and Shao-En Ong and David Baker and Dustin J. Maly},

url = {https://www.nature.com/articles/s41589-024-01684-4, Nat Chem Biol [Open Access]},

doi = {10.1038/s41589-024-01684-4},

year  = {2024},

date = {2024-08-05},

urldate = {2024-08-05},

journal = {Nature Chemical Biology},

publisher = {Springer Science and Business Media LLC},

abstract = {Clinical resistance to rat sarcoma virus (Ras)-G12C inhibitors is a challenge. A subpopulation of cancer cells has been shown to undergo genomic and transcriptional alterations to facilitate drug resistance but the immediate adaptive effects on Ras signaling in response to these drugs at the single-cell level is not well understood. Here, we used Ras biosensors to profile the activity and signaling environment of endogenous Ras at the single-cell level. We found that a subpopulation of KRas-G12C cells treated with Ras-G12C-guanosine-diphosphate inhibitors underwent adaptive signaling and metabolic changes driven by wild-type Ras at the Golgi and mutant KRas at the mitochondria, respectively. Our Ras biosensors identified major vault protein as a mediator of Ras activation through its scaffolding of Ras signaling pathway components and metabolite channels. Overall, methods including ours that facilitate direct analysis on the single-cell level can report the adaptations that subpopulations of cells adopt in response to cancer therapies, thus providing insight into drug resistance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Peruzzi2024b,

title = {Enhancing extracellular vesicle cargo loading and functional delivery by engineering protein-lipid interactions},

author = {Justin A. Peruzzi and Taylor F. Gunnels and Hailey I. Edelstein and Peilong Lu and David Baker and Joshua N. Leonard and Neha P. Kamat},

url = {https://www.nature.com/articles/s41467-024-49678-z [Nature Communications, Open Access]},

doi = {10.1038/s41467-024-49678-z},

year  = {2024},

date = {2024-07-04},

urldate = {2024-12-00},

journal = {Nature Communications},

publisher = {Springer Science and Business Media LLC},

abstract = {Naturally generated lipid nanoparticles termed extracellular vesicles (EVs) hold significant promise as engineerable therapeutic delivery vehicles. However, active loading of protein cargo into EVs in a manner that is useful for delivery remains a challenge. Here, we demonstrate that by rationally designing proteins to traffic to the plasma membrane and associate with lipid rafts, we can enhance loading of protein cargo into EVs for a set of structurally diverse transmembrane and peripheral membrane proteins. We then demonstrate the capacity of select lipid tags to mediate increased EV loading and functional delivery of an engineered transcription factor to modulate gene expression in target cells. We envision that this technology could be leveraged to develop new EV-based therapeutics that deliver a wide array of macromolecular cargo.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Goverde2024,

title = {Computational design of soluble and functional membrane protein analogues},

author = {Casper A. Goverde and Martin Pacesa and Nicolas Goldbach and Lars J. Dornfeld and Petra E. M. Balbi and Sandrine Georgeon and Stéphane Rosset and Srajan Kapoor and Jagrity Choudhury and Justas Dauparas and Christian Schellhaas and Simon Kozlov and David Baker and Sergey Ovchinnikov and Alex J. Vecchio and Bruno E. Correia},

url = {https://www.nature.com/articles/s41586-024-07601-y, Nature [Open Access]

},

doi = {10.1038/s41586-024-07601-y},

issn = {1476-4687},

year  = {2024},

date = {2024-06-19},

urldate = {2024-06-19},

journal = {Nature},

publisher = {Springer Science and Business Media LLC},

abstract = {De novo design of complex protein folds using solely computational means remains a substantial challenge. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wait2024,

title = {Machine learning-guided engineering of genetically encoded fluorescent calcium indicators},

author = {Sarah J. Wait and Marc Expòsit and Sophia Lin and Michael Rappleye and Justin Daho Lee and Samuel A. Colby and Lily Torp and Anthony Asencio and Annette Smith and Michael Regnier and Farid Moussavi-Harami and David Baker and Christina K. Kim and Andre Berndt},

url = {https://www.nature.com/articles/s43588-024-00611-w, Nat Comp Sci

https://www.bakerlab.org/wp-content/uploads/2024/03/s43588-024-00611-w.pdf, PDF},

doi = {10.1038/s43588-024-00611-w},

year  = {2024},

date = {2024-03-21},

urldate = {2024-03-00},

journal = {Nature Computational Science},

publisher = {Springer Science and Business Media LLC},

abstract = {Here we used machine learning to engineer genetically encoded fluorescent indicators, protein-based sensors critical for real-time monitoring of biological activity. We used machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries. The trained ensemble performed an in silico functional screen on 1,423 novel, uncharacterized GCaMP variants. As a result, we identified the ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP+, which achieve both faster kinetics and larger ∆F/F0 responses upon stimulation than previously published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP2+, which outperforms the tested sixth-, seventh- and eighth-generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient engineering of proteins for desired biophysical characteristics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mateos2024,

title = {Computational design of binders targeting the VSDIV from NaV1.7 sodium channel},

author = {Diego Lopez Mateos and Adam M. Murray and Hai M. Nguyen and Preetham Venkatesh and Brian Koepnick and David Baker and Heike Wulff and Vladimir Yarov-Yarovoy},

url = {https://www.cell.com/biophysj/abstract/S0006-3495(23)01470-4, Biophysical Journal},

doi = {10.1016/j.bpj.2023.11.770},

year  = {2024},

date = {2024-02-08},

urldate = {2024-02-00},

journal = {Biophysical Journal},

publisher = {Elsevier BV},

abstract = {Chronic pain affects about 20% of the US population, but safe treatments are limited. There is an urgent need for effective and non-addictive therapies for chronic pan conditions. Voltage-gated sodium (NaV) channel, NaV1.7, is a key player in pain signaling pathway, making it a promising target for novel pain therapeutics. Achieving high subtype selectivity when targeting NaV channels is of primary importance to avoid impairing vital physiological functions mediated by off-target channels. Efforts to selectively target NaV1.7 have been hindered by the difficulties in targeting NaV1.7 over other NaV channel subtypes. Peptidic gating modifier toxins (GMTs), such as Protoxin-II (ProTx2), are promising scaffolds for novel peptide design targeting ion channels with high potency and subtype selectivity. ProTx2 binds to the second and fourth voltage-sensing domains (VSDII and VSDIV) from NaV1.7 with moderate subtype selectivity and can modulate channel activation and inactivation. In this project, we modeled ProTx2 bound to human NaV1.7 VSDIV in an activated state. We used RoseTTAFold Diffusion and Protein MPNN protein design methods to generate protein binders inspired by ProTx2 binding motif with increased predicted binding affinity for human NaV1.7 VSDIV in an activated state. Additionally, we applied these protein design methods to create de novo binders targeting human NaV1.7 VSDIV in an activated state. We anticipate that trapping the VSDIV in an activated conformation will stabilize an inactivated state of the channel, as activation of VSDIV is coupled with channel fast inactivation. Initial electrophysiological screening of our top in silico binders identified promising candidates that inhibited NaV1.7 in the micromolar range. These binders will undergo further testing and optimization against NaV1.7 to create novel molecular tools to study NaV channel activity and effective and safe therapies for chronic pain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}