Protein 3D Structure Computed from Evolutionary Sequence Variation
Open accessAbstract
The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing.In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues, including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7-4.8 Å C(α)-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.
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The premise of methods to infer structure based on evolutionary couplings is that amino acids co-evolve in patterns reflective of the underlying structure because they exist and evolve in the context of 3D structure.
“The premise of methods to infer structure based on evolutionary couplings is that, because amino acids exist and evolve in the context of 3D structure, they are not free to evolve independently, but instead co-evolve in patterns reflective of the underlying structure.”
From Predicting multiple conformations via sequence clustering and AlphaFold2 (2024) · cites this paper as [9]
One of the major insights leveraged by AlphaFold comes from the use of evolutionary covariations that can be extracted from large-scale multiple sequence alignments.
“One of the major insights leveraged by AlphaFold 1,2 comes from the use of evolutionary covariations that can be extracted from large-scale multiple sequence alignments.[49][50][51][52][53][54]”
From AlphaFold and Implications for Intrinsically Disordered Proteins (2021) · cites this paper as [50]
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- Authors
- Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C.
- Affiliations
- Harvard University (US)
- MRC Laboratory of Molecular Biology (GB)
- Memorial Sloan Kettering Cancer Center (US)
- Italian institute for Genomic Medicine (IT)
- Politecnico di Torino (IT)
- Corresponding author
- Marks, Debora S.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-12-07
- Volume
- 6
- Issue
- 12
- Pages
- e28766
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3233603
- OpenAlex
- W2061042699
- Cited by
- 1,144
- References
- 84
- Citations by year
- 2026: 61 · 2025: 60 · 2024: 47 · 2023: 72 · 2022: 96 · 2021: 104 · 2020: 90 · 2019: 110 · 2018: 91 · 2017: 107 · 2016: 71 · 2015: 74 · 2014: 72 · 2013: 56 · 2012: 29
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PORTO@Iris (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion, cc-by)
- Funders
- Memorial Sloan-Kettering Cancer Center
- Studienstiftung des Deutschen Volkes
- National Institutes of Health (NIH U54, CA143798)
- Engineering and Physical Sciences Research Council (EP/H028064/1)
- National Cancer Institute (U54CA143798)
- National Institute of General Medical Sciences (R01GM106303)
- 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 › RNA and protein synthesis mechanisms
- Keywords
- protein structure prediction, coevolution, maximum entropy model, multiple sequence alignment, de novo structure prediction, protein evolution, G protein-coupled receptors, protein engineering
- MeSH terms
- Animals, Humans, Protein Conformation, Proteins, Rhodopsin, Trypsin, Genetic Variation, Drug Design, Reproducibility of Results, Models, Statistical, Genome, Protein Structure, Tertiary, Evolution, Molecular, Entropy, Computational Biology, Catalytic Domain, Imaging, Three-Dimensional
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