@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{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{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{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{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{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{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{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{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{Ovchinnikov294,

title = {Protein structure determination using metagenome sequence data},

author = { Sergey Ovchinnikov and Hahnbeom Park and Neha Varghese and Po-Ssu Huang and Georgios A. Pavlopoulos and David E. Kim and Hetunandan Kamisetty and Nikos C. Kyrpides and David Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2017/01/ovchinnikov_science_2017.pdf

http://science.sciencemag.org/content/355/6322/294},

doi = {10.1126/science.aah4043},

issn = {0036-8075},

year  = {2017},

date = {2017-01-01},

journal = {Science},

volume = {355},

number = {6322},

pages = {294--298},

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

abstract = {Fewer than a third of the 14,849 known protein families have at least one member with an experimentally determined structure. This leaves more than 5000 protein families with no structural information. Protein modeling using residue-residue contacts inferred from evolutionary data has been successful in modeling unknown structures, but it requires large numbers of aligned sequences. Ovchinnikov et al. augmented such sequence alignments with metagenome sequence data (see the Perspective by S"oding). They determined the number of sequences required to allow modeling, developed criteria for model quality, and, where possible, improved modeling by matching predicted contacts to known structures. Their method predicted quality structural models for 614 protein families, of which about 140 represent newly discovered protein folds.Science, this issue p. 294; see also p. 248Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PROT:PROT25006,

title = {Structure prediction using sparse simulated NOE restraints with Rosetta in CASP11},

author = {Ovchinnikov, Sergey and Park, Hahnbeom and Kim, David E. and Liu, Yuan and Wang, Ray Yu-Ruei and Baker, David},

url = {http://dx.doi.org/10.1002/prot.25006

https://www.bakerlab.org/wp-content/uploads/2016/05/Ovchinnikov_et_al-2016-Proteins__Structure_Function_and_Bioinformatics.pdf},

doi = {10.1002/prot.25006},

issn = {1097-0134},

year  = {2016},

date = {2016-01-01},

journal = {Proteins: Structure, Function, and Bioinformatics},

pages = {n/a--n/a},

abstract = {In CASP11 we generated protein structure models using simulated ambiguous and unambiguous nuclear Overhauser effect (NOE) restraints with a two stage protocol. Low resolution models were generated guided by the unambiguous restraints using continuous chain folding for alpha and alpha-beta proteins, and iterative annealing for all beta proteins to take advantage of the strand pairing information implicit in the restraints. The Rosetta fragment/model hybridization protocol was then used to recombine and regularize these models, and refine them in the Rosetta full atom energy function guided by both the unambiguous and the ambiguous restraints. Fifteen out of 19 targets were modeled with GDT-TS quality scores greater than 60 for Model 1, significantly improving upon the non-assisted predictions. Our results suggest that atomic level accuracy is achievable using sparse NOE data when there is at least one correctly assigned NOE for every residue. Proteins 2016. © 2016 Wiley Periodicals, Inc.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{S2015,

title = {Improved de novo structure prediction in CASP11 by incorporating Co-evolution information into rosetta},

author = {S Ovchinnikov and DE Kim and RY Wang and Y Liu and F DiMaio and D Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2015/12/Ovchinnikov_Proteins_2015.pdf},

doi = {10.1002/prot.24974},

year  = {2015},

date = {2015-12-17},

journal = {Proteins},

abstract = {We describe CASP11 de novo blind structure predictions made using the Rosetta structure prediction methodology with both automatic and human assisted protocols. Model accuracy was generally improved using co-evolution derived residue-residue contact information as restraints during Rosetta conformational sampling and refinement, particularly when the number of sequences in the family was more than three times the length of the protein. The highlight was the human assisted prediction of T0806, a large and topologically complex target with no homologs of known structure, which had unprecedented accuracy - <3.0 Å root-mean-square deviation (RMSD) from the crystal structure over 223 residues. For this target, we increased the amount of conformational sampling over our fully automated method by employing an iterative hybridization protocol. Our results clearly demonstrate, in a blind prediction scenario, that co-evolution derived contacts can considerably increase the accuracy of template-free structure modeling. This article is protected by copyright. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{S2015b,

title = {Large-scale determination of previously unsolved protein structures using evolutionary information},

author = {S Ovchinnikov, L Kinch, H Park, Y Liao, J Pei, DE Kim, H Kamisetty, NV Grishin, D Baker},

url = {https://www.bakerlab.org/wp-content/uploads/2016/01/Ovchinnikov_eLife_2015.pdf},

doi = {10.7554/eLife.09248},

year  = {2015},

date = {2015-09-03},

journal = {eLife},

abstract = {The prediction of the structures of proteins without detectable sequence similarity to any protein of known structure remains an outstanding scientific challenge. Here we report significant progress in this area. We first describe de novo blind structure predictions of unprecendented accuracy we made for two proteins in large families in the recent CASP11 blind test of protein structure prediction methods by incorporating residue-residue co-evolution information in the Rosetta structure prediction program. We then describe the use of this method to generate structure models for 58 of the 121 large protein families in prokaryotes for which three-dimensional structures are not available. These models, which are posted online for public access, provide structural information for the over 400,000 proteins belonging to the 58 families and suggest hypotheses about mechanism for the subset for which the function is known, and hypotheses about function for the remainder. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{625,

title = {WeFold: a coopetition for protein structure prediction.},

author = { George A Khoury and Adam Liwo and Firas Khatib and Hongyi Zhou and Gaurav Chopra and Jaume Bacardit and Leandro O Bortot and Rodrigo A Faccioli and Xin Deng and Yi He and Pawel Krupa and Jilong Li and Magdalena A Mozolewska and Adam K Sieradzan and James Smadbeck and Tomasz Wirecki and Seth Cooper and Jeff Flatten and Kefan Xu and David Baker and Jianlin Cheng and Alexandre C B Delbem and Christodoulos A Floudas and Chen Keasar and Michael Levitt and Zoran Popovi'c and Harold A Scheraga and Jeffrey Skolnick and Silvia N Crivelli},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Khoury_Proteins_2014.pdf},

doi = {10.1002/prot.24538},

issn = {1097-0134},

year  = {2014},

date = {2014-09-01},

journal = {Proteins},

volume = {82},

pages = {1850-68},

abstract = {The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{622,

title = {High-resolution modeling of transmembrane helical protein structures from distant homologues.},

author = { Kuang-Yui M Chen and Jiaming Sun and Jason S Salvo and David Baker and Patrick Barth},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Chen_PLOS_2014.pdf},

doi = {10.1371/journal.pcbi.1003636},

issn = {1553-7358},

year  = {2014},

date = {2014-05-01},

journal = {PLoS computational biology},

volume = {10},

pages = {e1003636},

abstract = {Eukaryotic transmembrane helical (TMH) proteins perform a wide diversity of critical cellular functions, but remain structurally largely uncharacterized and their high-resolution structure prediction is currently hindered by the lack of close structural homologues. To address this problem, we present a novel and generic method for accurately modeling large TMH protein structures from distant homologues exhibiting distinct loop and TMH conformations. Models of the adenosine A2AR and chemokine CXCR4 receptors were first ranked in GPCR-DOCK blind prediction contests in the receptor structure accuracy category. In a benchmark of 50 TMH protein homolog pairs of diverse topology (from 5 to 12 TMHs), size (from 183 to 420 residues) and sequence identity (from 15% to 70%), the method improves most starting templates, and achieves near-atomic accuracy prediction of membrane-embedded regions. Unlike starting templates, the models are of suitable quality for computer-based protein engineering: redesigned models and redesigned X-ray structures exhibit very similar native interactions. The method should prove useful for the atom-level modeling and design of a large fraction of structurally uncharacterized TMH proteins from a wide range of structural homologues.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{540,

title = {Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information.},

author = { Sergey Ovchinnikov and Hetunandan Kamisetty and David Baker},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Ovchinnikov_2014A.pdf},

doi = {10.7554/eLife.02030},

issn = {2050-084X},

year  = {2014},

date = {2014-05-01},

journal = {eLife},

volume = {3},

pages = {e02030},

abstract = {Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to predict contacts across interfaces and assemble models of biological complexes. We find that residue pairs identified using a pseudo-likelihood-based method to covary across protein-protein interfaces in the 50S ribosomal unit and 28 additional bacterial protein complexes with known structure are almost always in contact in the complex, provided that the number of aligned sequences is greater than the average length of the two proteins. We use this method to make subunit contact predictions for an additional 36 protein complexes with unknown structures, and present models based on these predictions for the tripartite ATP-independent periplasmic (TRAP) transporter, the tripartite efflux system, the pyruvate formate lyase-activating enzyme complex, and the methionine ABC transporter.DOI: http://dx.doi.org/10.7554/eLife.02030.001.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{529,

title = {Protein folding, structure prediction and design.},

author = { David Baker},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Baker_BiochemSocTrans_2014.pdf},

doi = {10.1042/BST20130055},

issn = {1470-8752},

year  = {2014},

date = {2014-04-01},

journal = {Biochemical Society transactions},

volume = {42},

pages = {225-9},

abstract = {I describe how experimental studies of protein folding have led to advances in protein structure prediction and protein design. I describe the finding that protein sequences are not optimized for rapid folding, the contact order-protein folding rate correlation, the incorporation of experimental insights into protein folding into the Rosetta protein structure production methodology and the use of this methodology to determine structures from sparse experimental data. I then describe the inverse problem (protein design) and give an overview of recent work on designing proteins with new structures and functions. I also describe the contributions of the general public to these efforts through the Rosetta@home distributed computing project and the FoldIt interactive protein folding and design game.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{505,

title = {Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.},

author = { Rocco Moretti and Sarel J Fleishman and Rudi Agius and Mieczyslaw Torchala and Paul A Bates and Panagiotis L Kastritis and Jo~ao P G L M Rodrigues and Mika"el Trellet and Alexandre M J J Bonvin and Meng Cui and Marianne Rooman and Dimitri Gillis and Yves Dehouck and Iain Moal and Miguel Romero-Durana and Laura Perez-Cano and Chiara Pallara and Brian Jimenez and Juan Fernandez-Recio and Samuel Flores and Michael Pacella and Krishna Praneeth Kilambi and Jeffrey J Gray and Petr Popov and Sergei Grudinin and Juan Esquivel-Rodr'iguez and Daisuke Kihara and Nan Zhao and Dmitry Korkin and Xiaolei Zhu and Omar N A Demerdash and Julie C Mitchell and Eiji Kanamori and Yuko Tsuchiya and Haruki Nakamura and Hasup Lee and Hahnbeom Park and Chaok Seok and Jamica Sarmiento and Shide Liang and Shusuke Teraguchi and Daron M Standley and Hiromitsu Shimoyama and Genki Terashi and Mayuko Takeda-Shitaka and Mitsuo Iwadate and Hideaki Umeyama and Dmitri Beglov and David R Hall and Dima Kozakov and Sandor Vajda and Brian G Pierce and Howook Hwang and Thom Vreven and Zhiping Weng and Yangyu Huang and Haotian Li and Xiufeng Yang and Xiaofeng Ji and Shiyong Liu and Yi Xiao and Martin Zacharias and Sanbo Qin and Huan-Xiang Zhou and Sheng-You Huang and Xiaoqin Zou and Sameer Velankar and Jo"el Janin and Shoshana J Wodak and David Baker},

url = {http://www.bakerlab.org/wp-content/uploads/2015/12/Moretti_Proteins_2013.pdf},

doi = {10.1002/prot.24356},

issn = {1097-0134},

year  = {2013},

date = {2013-11-01},

journal = {Proteins},

volume = {81},

pages = {1980-7},

abstract = {Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{508,

title = {Improved chemical shift based fragment selection for CS-Rosetta using Rosetta3 fragment picker.},

author = { Robert Vernon and Yang Shen and David Baker and Oliver F Lange},

doi = {10.1007/s10858-013-9772-4},

issn = {1573-5001},

year  = {2013},

date = {2013-10-01},

journal = {Journal of biomolecular NMR},

volume = {57},

pages = {117-27},

abstract = {A new fragment picker has been developed for CS-Rosetta that combines beneficial features of the original fragment picker, MFR, used with CS-Rosetta, and the fragment picker, NNMake, that was used for purely sequence based fragment selection in the context of ROSETTA de-novo structure prediction. Additionally, the new fragment picker has reduced sensitivity to outliers and other difficult to match data points rendering the protocol more robust and less likely to introduce bias towards wrong conformations in cases where data is bad, missing or inconclusive. The fragment picker protocol gives significant improvements on 6 of 23 CS-Rosetta targets. An independent benchmark on 39 protein targets, whose NMR data sets were published only after protocol optimization had been finished, also show significantly improved performance for the new fragment picker (van der Schot et al. in J Biomol NMR, 2013).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}