ArticleNature biotechnology2017
Mutation effects predicted from sequence co-variation.
Article in Nature biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 398 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
398 citing papers in PubMed.
- Quantitative analysis of trafficking defects induced by heterozygous expression of hERG voltage sensor domain variants.Channels (Austin, Tex.) · 2026Article
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- The GPCRDiabetologia · 2026Article
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Enhancing prediction accuracy for enzyme activity engineering through sequence co-evolution and epistatic relationship modeling.Nucleic acids research · 2026Article
- The fitness landscape of a form II rubisco in a photosynthetic bacterium guides engineering of oxygen tolerance.Science advances · 2026Article
- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- Review
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
- Identification of novel functional sites in the CaProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Evolutionary profiles for protein fitness prediction.Bioinformatics (Oxford, England) · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Protein stability is determined by single-site bias rather than pairwise covariance.Nature chemical biology · 2026Article
- Property guidance for protein sequence generative models with ProteinGuide.Nature biotechnology · 2026Article
- Scanning the horizon: deep mutational scanning approaches in virology.Journal of virology · 2026Review
- Structure and evolution-guided design of minimal RNA-guided nucleases.Science (New York, N.Y.) · 2026Article
- FireProtASR 2.0: evolution-guided Design of Protein Ancestors and Successors with phylogenetics and machine learning.Briefings in bioinformatics · 2026Article
- Machine-learning prediction of affinity and epistasis in the bovine pancreatic trypsin inhibitor-chymotrypsin complex.Protein science : a publication of the Protein Society · 2026Article
- Engineering and Application of a Thermostable MHETase for PET Depolymerization.ACS sustainable chemistry & engineering · 2026Article
338 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.