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

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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

DOI 10.1038/nbt.3769PMC PMC5383098

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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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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: Hopf, Thomas A
Ingraham, John B
Poelwijk, Frank J
Schärfe, Charlotta P I
Springer, Michael
Sander, Chris
Marks, Debora S
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2017-01-16
Volume: 35
Issue: 2
Pages: 128-135
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.3769
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

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Mutation effects predicted from sequence co-variation

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