Mutation effects predicted from sequence co-variation
Open accessAbstract
Many high-throughput experimental technologies have been developed to assess the effects of large numbers of mutations (variation) on phenotypes. However, designing functional assays for these methods is challenging, and systematic testing of all combinations is impossible, so robust methods to predict the effects of genetic variation are needed. Most prediction methods exploit evolutionary sequence conservation but do not consider the interdependencies of residues or bases. We present EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions. We validate EVmutation by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations and show that it outperforms methods that do not account for epistasis. EVmutation can be used to assess the quantitative effects of mutations in genes of any organism. We provide pre-computed predictions for ∼7,000 human proteins at http://evmutation.org/.
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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 [53]
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Continuous evolution of user-defined genes at 1 million times the genomic mutation rate
Rix, G., Williams, R. L., Hu, V. J., Spinner, A., Pisera, A. (., Marks, D. S., & Liu, C. C. (2024) · Science · doi:10.1126/science.adm9073 · Cited by 71 · see citing works
Self-play reinforcement learning guides protein engineering
Wang, Y., Tang, H., Huang, L., Pan, L., Yang, L., Yang, H., Mu, F., & Yang, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00691-9 · Cited by 69 · see citing works
Biophysics-based protein language models for protein engineering
Gelman, S., Johnson, B., Freschlin, C. R., Sharma, A., D'Costa, S., Peters, J., Gitter, A., & Romero, P. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02776-2 · Cited by 70 · see citing works
Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering
Xie, W. J., & Warshel, A. (2023) · National Science Review · doi:10.1093/nsr/nwad331 · Cited by 69 · see citing works
Machine learning prediction of enzyme optimum pH
Gado, J. E., Knotts, M., Shaw, A. Y., Marks, D. S., Gauthier, N. P., Sander, C., & Beckham, G. T. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01026-6 · Cited by 70 · see citing works
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.
Bisardi, M., Rodriguez-Rivas, J., Zamponi, F., & Weigt, M. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msab321
Cluster learning-assisted directed evolution
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Deep generative design of RNA family sequences
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Persistent spectral theory-guided protein engineering
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Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation
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- Authors
- Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S.
- Affiliations
- Harvard University (US)
- Dana-Farber Cancer Institute (US)
- Corresponding author
- Marks, Debora S
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2017-01-16
- Volume
- 35
- Issue
- 2
- Pages
- 128-135
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC5383098
- OpenAlex
- W2245592118
- Cited by
- 775
- References
- 113
- Citations by year
- 2026: 85 · 2025: 115 · 2024: 106 · 2023: 83 · 2022: 86 · 2021: 100 · 2020: 54 · 2019: 60 · 2018: 50 · 2017: 34 · 2012: 1
- FWCI
- 35.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM106303)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- mutation effect prediction, epistasis, high-throughput mutagenesis, protein evolution
- MeSH terms
- Amino Acid Sequence, DNA Mutational Analysis, Epistasis, Genetic, Humans, Molecular Sequence Data, Mutation, Genetic Variation, Conserved Sequence, Evolution, Molecular, Proteome, High-Throughput Nucleotide Sequencing
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