Cited by 787Genomics and Rare DiseasesEvolution and Genetic DynamicsProtein Structure and Dynamics

Mutation effects predicted from sequence co-variation

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Hopf, Thomas A, Ingraham, John B, Poelwijk, Frank J, Schärfe, Charlotta P I, Springer, Michael, Sander, Chris, Marks, Debora S·2017·Nature Biotechnology

Abstract

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]”

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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
Venue
Nature Biotechnology
Published
2017-01-16
Volume
35
Issue
2
Pages
128-135
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
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
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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