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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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Sequence Covariation Analysis in Biological Polymers
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The evolution of human SIRT1 for multi-specificity is enabled by active-site adaptationPreprint
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“Techniques such as Direct Coupling Analysis (DCA) extract from the MSAs amino acid contacts and 3D structures (Burger and Van Nimwegen, 2010; Marks et al., 2011; Ovchinnikov et al., 2015), protein–protein interaction sites (Morcos et al.”
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Deep Homology-Based Protein Contact-Map PredictionPreprint
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MethodIdentification of a novel fold type in CPA/AT transporters by ab-initio structure predictionPreprint
Bassot, C., Sudha, G., Lamb, J., & Elofsson, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.05.368662
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Faculty Opinions recommendation of Inferring protein 3D structure from deep mutation scans.
Koehl, P. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735998329.793566129
Skolnick, J. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.727881376.793545019
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Dill, K. A., & Pérez, A. (2017) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.727224329.793538419
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Correlated Coevolving Mutations at Protein–Protein Interfaces
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A Fast and Reliable Approach to Protein Structure Prediction Using K-Mer Matching and ModellerPreprint
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Generative genomics accurately predicts future experimental resultsPreprint
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Seif-Eddine, M. (2018) · theses.fr (ABES) · doi:10.70675/26c00bdfzec8dz48e5za05az270fef99935c
Évolution, structure et inhibition des systèmes de sécrétion bactériens
Filella-Mercè, I. (2022) · theses.fr (ABES) · doi:10.70675/23fd4f39za30az4cecz94bfz9d0efc94a30f
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
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