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Large-scale determination of previously unsolved protein structures using evolutionary information
Open accessAbstract
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.
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“Traditional computational prediction pipelines reliant on inter-residue distance restraints calculated from deep multiple sequence alignments (MSAs) have historically struggled to accurately predict the structures of these proteins and their movements.”
From Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022) · cites this paper as Ovchinnikov et al., 2015
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About the need to make computational models of biological macromolecules available and discoverable
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“This underlies the statistical deduction of spatially closed acids, with high correlation values to be used as constraints for folding, for example, TM proteins [90,91].”
BackgroundNew insights into substrate folding preference of plant OSCs
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“We applied MELD to study the binding of a set of peptides to P53 regulators, MDM2 and MDMX. 96,97 The P53 inhibition of MDM2 and MDMX is a target for anticancer therapies.”
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NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents
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Computational approaches to macromolecular interactions in the cell
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Protein-Protein Docking Using Evolutionary Information
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Modeling membrane proteins: The importance of cysteine amino-acids
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Contingency and Entrenchment of Drug-Resistance Mutations in HIV Viral Proteins
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“The YhhN family is highly conserved between species (59, 61), and accordingly TMEM86a has a high degree of sequence conservation with the only other known mammalian lysoplasmalogenase protein, TMEM86b (59).”
BackgroundConKit: a python interface to contact predictions
Šimkovic, F., Thomas, J. M. H., & Rigden, D. J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx148 · Cited by 14
Sequential search leads to faster, more efficient fragment-basedde novoprotein structure prediction
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“Three recent studies using the software ROSETTA describe the use of 10,000 (Ovchinnikov, S. et al , 2015), 20,000 (Ovchinnikov, S. et al , 2017), and 20,000-900,000(Kim,D.E.etal,2014)decoyspertarget,meaningthatthe consensus is not clear even for the same structure predictor.”
BackgroundMethod2.8 angstrom crystal structure of Succinate-Acetate Permease from Citrobacter koseri
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Evolution of the Immunoglobulin Isotypes—Variations of Biophysical Properties among Animal Classes
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“Such restraints have been used to improve structure prediction accuracy for IMPs by restricting the sampling space in Rosetta (Ovchinnikov et al., 2015).”
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Combining Evolutionary Covariance and NMR Data for Protein Structure Determination
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“Analysis of deep learning methods for blind protein contact prediction in CASP12.”
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“The AF2 model of FtsW is also in good agreement both with crystal structures of the homologous RodA (alone and in complex with the FtsI homolog PBP2) (23,45) as well as with an earlier Rosetta model of FtsW based on co-evolutionary information (50).”
BackgroundResultRandom Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection
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FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution
Feng, J., & Shukla, D. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b11869 · Cited by 9
3D protein structure from genetic epistasis experimentsPreprint
Rollins, N. J., Brock, K. P., Poelwijk, F. J., Stiffler, M. A., Gauthier, N. P., Sander, C., & Marks, D. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/320721 · Cited by 10
“In addition to experimental determination (e.g. NMR, X-Ray crystallography and cryo-EM), recent computational approaches have also been successful in folding both small and large proteins ab initio using residue-residue contacts inferred from natural, evolutionary sequence information ( 32-36 ).”
BackgroundLearning sequence, structure, and function representations of proteins with language modelsPreprint
Hamamsy, T., Barot, M., Morton, J. T., Steinegger, M., Bonneau, R., & Cho, K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.26.568742 · Cited by 12
“Studies of neutral rates of evolution as well as studies of protein-protein structure similarity (and structure similarity correspondence with function concordance), suggest that a large fraction of novel or unannotated proteins are cases of failed homology detection [8, 9, 10].”
BackgroundDevelopment of anti-PD-L1 antibody based on structure prediction of AlphaFold2
Du, K., & Huang, H. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1275999 · Cited by 8
“Previous large-scale structure prediction studies have addressed protein families (5 – 8), speci fi c functional classes (9, 10), domains within whole proteomes (11), and in some cases, full chains or complexes (12, 13).”
BackgroundLi, B., Mendenhall, J., & Meiler, J. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.05.005 · Cited by 8
“In fact, the effectiveness of using predicted coevolving residue pairs to identify native-like docking solutions has been demonstrated previously for globular proteins [91,96 – 100] and some isolated cases of IMPs [97,98].”
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Increasing the accuracy of protein loop structure prediction with evolutionary constraints
Marks, C., & Deane, C. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty996 · Cited by 8
Insights into DNA cleavage by MutL homologs from analysis of conserved motifs in eukaryotic Mlh1
Putnam, C. D., & Kolodner, R. D. (2023) · BioEssays · doi:10.1002/bies.202300031 · Cited by 10
“(D) Evolutionary coupling of residues inMlh1with GREMLIN.[51] Blue and dark blue points are evolutionary coupled sites; red points are side-chain contacts calculated from an Alphafold2model of S.”
InfluentialKilian, M., & Bischofs, I. B. (2023) · Molecular Microbiology · doi:10.1111/mmi.15169 · Cited by 8
“…on a protein sequence provide predictors of residue proximity (Hopf et al., 2012; Kamisetty et al., 2013; Marks et al., 2011; Morcos et al., 2011; Ovchinnikov et al., 2015) even at protein– protein interfaces, and therefore contain information about the structure of conserved protein complexes…”
BackgroundHuang, Y. J., Brock, K. P., Sander, C., Marks, D. S., & Montelione, G. T. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-2200-6_10 · Cited by 7
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“…number of targets, however.23 Recently, residue contacts derived from co-evolution were used in conjunction with the Rosetta de novo protocol.24 ,25 Despite the folding constraints imposed by the membrane, generating a structure that is close to the native structure is usually a rare event…”
MethodStatistical investigations of protein residue direct couplings
Neuwald, A. F., & Altschul, S. F. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006237 · Cited by 7
Protein Structures, Interactions and Function from Evolutionary Couplings
Hopf, T. A., & Marks, D. S. (2017) · Springer eBooks · doi:10.1007/978-94-024-1069-3_2 · Cited by 6
“Many other groups have since used this or similar approaches to predict accurate long-range contacts from sequences, benchmarking against known contacts in observed 3D structures; such accurate predictions are typically available for thousands of families (Hopf et al. 2012; Michel et al. 2014; Kosciolek and Jones 2014…”
BackgroundMethodInfluentialThe evolutionary history of topological variations in the CPA/AT transporters
Sudha, G., Bassot, C., Lamb, J., Shu, N., Huang, Y. H., & Elofsson, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009278 · Cited by 5
“coli member of PSE (YieH) has been presented earlier with an identical fold [32], (3) the recently added protein model from Pfam predicts a similar fold (see Pfam website).”
BackgroundAbriata, L. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/123349 · Cited by 6
Green, A. G., Elhabashy, H., Brock, K. P., Maddamsetti, R., Kohlbacher, O., & Marks, D. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/791293 · Cited by 6
Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR DataPreprint
Li, E. H., Spaman, L. E., Tejero, R., Huang, Y. J., Ramelot, T. A., Fraga, K. J., Prestegard, J. H., Kennedy, M. A., & Montelione, G. T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.22.525096 · Cited by 8
“Advances in attention-based machine learning [10, 11] and contact prediction based on sequence covariance [12-17], along with the continuing rapid expansion of genomic sequence databases [18] and the growth of the open-access Protein Data Bank (PDB) [19], are enabling novel structural biology research.”
Unifying Constraints Linking Protein Folding and Native Dynamics Decoded from AlphaFold
Zhang, Z., Ren, W., Xie, L., Zheng, Y., Guan, X., Wang, J., Li, W., & Tang, Q.-Y. (2026) · Physical Review Letters · doi:10.1103/7j2j-f8f7 · Cited by 5
Widespread evolutionary crosstalk among protein domains in the context of multi-domain proteins
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Mutagenesis-based protein structure determination
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Approaches toab initiomolecular replacement of α-helical transmembrane proteins
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Tuned Fitness Landscapes for Benchmarking Model-Guided Protein DesignPreprint
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Unifying constraints linking protein folding and native dynamics decoded from AlphaFold
Zhang, Z., Ren, W., Xie, L., Zheng, Y., Guan, X., Wang, J., Li, W., & Tang, Q.-Y. (2026) · doi:10.64898/2026.01.08.698426 · Cited by 5
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A coevolution-guided model for the rotor of the bacterial flagellar motor
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dos Santos, R. N., Bottino, G. F., Gozzo, F. C., Morcos, F., & Martínez, L. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25843 · Cited by 4
Pair Potentials as Machine Learning Features
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Protein Structure Prediction and Homology Modeling
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Virus-inspired designs of antimicrobial nanocapsules
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Kassem, M. M., Christoffersen, L., Cavalli, A., & Lindorff‐Larsen, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-29357-y · Cited by 3
Greß, A., & Kalinina, O. V. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa159 · Cited by 4
Accurate contact predictions for thousands of protein families using PconsC3Preprint
Skwark, M. J., Michel, M., Hurtado, D. M., Ekeberg, M., & Elofsson, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/079673 · Cited by 3
“Using a more elaborate folding protocol [25] or generating more than 2000 Rosetta decoys might improve this situation.”
Regulatory DNA in A . thaliana can tolerate high levels of sequence divergencePreprint
Alexandre, C. M., Urton, J. R., Jean-Baptiste, K., Dorrity, M. W., Cuperus, J. T., Sullivan, A. M., Bemm, F., Jolić, D., Arsovski, A. A., Thompson, A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/104323 · Cited by 3
Harrar, T. E., & Gohlke, H. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01123 · Cited by 2
Contreras, S. C., Bertolani, S. J., & Siegel, J. B. (2019) · ACS Omega · doi:10.1021/acsomega.9b02636 · Cited by 2
(2023) · doi:10.1002/pro.4779 · Cited by 2
“…the structure predicted by Baker and colleagues before any Class-I FH structure was experimentally determined, shows overall similarity in folds indicating that the two groups of enzymes, being similar in sequence, have the same three-dimensional structure as well (Ovchinnikov et al., 2015).”
Improving protein structure prediction using amino acid contact & distance prediction
Ji, S. (2019) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 2
Tandem WW/PPxY motif interactions in WWOX: the multifaceted role of the second WW domainPreprint
Rotem‐Bamberger, S., Fahoum, J., Keinan‐Adamsky, K., Tsaban, T., Avraham, O., Shalev, D. E., Chill, J. H., & Schueler‐Furman, O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.01.470705 · Cited by 2
“While the models generated by AlphaFold2 converge, models that we generated using additional protocols (including Rosetta ab initio folding with GREMLIN constraints (37), TrRosetta (38) and RoseTTAFold (39), data not shown) exhibit a range of additional possible relative orientations between WW1 and WW2, with predomin…”
MethodSulfite exporter SsTauE enhances bacterial feather degradation by maintaining sulfur homeostasis
Wu, L., Zhang, M.-S., Yang, Z.-D., Li, Z., Deng, J., & Luo, X. (2025) · International Biodeterioration & Biodegradation · doi:10.1016/j.ibiod.2025.106222 · Cited by 1
Westerman, T. L., Scheuerell, E., Nguyen, V. H., Johnson, K., McClelland, M., & Elfenbein, J. R. (2026) · Journal of Bacteriology · doi:10.1128/jb.00431-25 · Cited by 1
“YeiH is predicted to be a membrane protein with nine transmembrane helices, a pair of broken helices that enter and exit on the same side of the membrane, and a periplasmic C-terminus (32–34).”
Dheur, S., & Saupe, S. J. (2016) · Revue des sciences sociales · doi:10.4000/revss.411 · Cited by 1
Pierce, M. R., Ji, J.-J., Novak, S. X., Sieburg, M. A., Nangia, S., Nangia, S., & Hougland, J. L. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00917 · Cited by 1
Structural discrimination analysis for constraint selection in protein modeling
Bottino, G. F., Ferrari, A., Gozzo, F. C., & Martínez, L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab425 · Cited by 1
Si, Y., Zhang, Y., & Yan, C. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab576 · Cited by 1
Viral Complexity: Amino acid co-evolution in viral genomes as a possible metricPreprint
Sruthi, C. K., & Prakash, M. K. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/159541 · Cited by 1
Campaña, M. B., Irudayanathan, F. J., Davis, T. R., McGovern-Gooch, K. R., Loftus, R., Ashkar, M., Escoffery, N., Navarro, M., Sieburg, M. A., Nangia, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/556233 · Cited by 1
Computational Tools for Structural Analysis of Proteins
Abriata, L. A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.12366-0
Chamberland, S. R. (2019) · OpenCommons at University of Connecticut (University of Connecticut)
Metadata
- Authors
- Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- The University of Texas Southwestern Medical Center (US)
- Meta (United States) (US)
- Corresponding author
- Baker, David
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2015-09-03
- Volume
- 4
- Pages
- e09248-e09248
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC4602095
- OpenAlex
- W1895528264
- Cited by
- 222
- References
- 80
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2 · 2023: 12 · 2022: 17 · 2021: 20 · 2020: 25 · 2019: 42 · 2018: 30 · 2017: 39 · 2016: 24 · 2015: 4
- FWCI
- 14.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (NIH) (R01GM092802, GM094575)
- Welch Foundation (Robert A. Welch Foundation) (I-1505)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein structure prediction, de novo structure prediction, Rosetta, prokaryotic proteins, structural model, protein function prediction, evolutionary information
- MeSH terms
- Bacterial Proteins, Models, Molecular, Protein Conformation, Evolution, Molecular, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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