Protein structure determination using metagenome sequence data
Open accessAbstract
Despite 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.
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In recent years, there has been major progress in utilizing residue co-evolution information for predicting structures of individual proteins, based on rapid growth of experimental data and new ideas, combined with deep learning.
“In recent years, there has been major progress in utilizing the residue co-evolution information for predicting structures of individual proteins, based on the rapid growth of experimental data on proteins and new ideas on how to use it, combined with spectacular advances in computer science (deep learning) [30–33].”
From Challenges in protein docking (2020) · cites this paper as [33]
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- Authors
- Ovchinnikov, S., Park, H., Varghese, N., Huang, P.-S., Pavlopoulos, G. A., Kim, D. E., Kamisetty, H., Kyrpides, N. C., & Baker, D.
- Affiliations
- University of Washington (US)
- Joint Genome Institute (US)
- Howard Hughes Medical Institute (US)
- Meta (United States) (US)
- King Abdulaziz University (SA)
- Corresponding author
- Baker, David
- ORCID
- Venue
- Science
- Published
- 2017-01-20
- Volume
- 355
- Issue
- 6322
- Pages
- 294-298
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC5493203
- OpenAlex
- W2574496196
- Cited by
- 520
- References
- 55
- Citations by year
- 2026: 19 · 2025: 20 · 2024: 27 · 2023: 39 · 2022: 44 · 2021: 60 · 2020: 61 · 2019: 104 · 2018: 89 · 2017: 56
- FWCI
- 34.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy
- National Institute of General Medical Sciences
- NIH (R01GM092802)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, Rosetta, residue-residue contacts, protein families, membrane proteins, novel protein fold, Protein Structure Initiative, structural genomics
- MeSH terms
- Algorithms, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, Software, Protein Folding, Crystallography, X-Ray, Evolution, Molecular, Computational Biology, Sequence Analysis, Protein, Databases, Protein, Metagenome
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