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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 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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Metadata
- 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
- 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
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.
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