Mutation effects predicted from sequence co-variation

By Thomas A. Hopf, John Ingraham, F. J. Poelwijk, Charlotta P I Schärfe, Michael Springer, Christopher Sander, Debora Marks. 2015

766 citations recorded by the source corpus.

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/.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “…properties that affect ﬁtness directly, such as activity, stability, structure, binding, and other properties under selection (Hormoz, 2013; Hopf et al., 2017) as well as by contributions from phylogenetic bias (Gabaldon, 2007), experimental and selection biases (Wang et al., 2019;…”
Clustering huge protein sequence sets in linear time (2017, background): “Metaclust should also allow us to predict more accurately the effects of mutations on proteins 23 .”
Low-N protein engineering with data-efficient deep learning (2020, methodology): “While unsupervised methods trained on natural sequence data perform well at predicting or avoiding loss-of-function variants during modeling and design, they have also been unable to reliably model or design better-than-natural variants 38,39,41,47,52 .”
Language models enable zero-shot prediction of the effects of mutations on protein function (2021, methodology): “The MSAs used for training DeepSequence and EVMutation are generated from the 2017-10 version of Uniref100, whereas the models we study are trained on sequences from Uniref90 2020-03.”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “These methods, which we will call direct methods, have greatly increased the accuracy of sequence coevolution analysis in predicting true contacts, opening up this method to predicting protein structures [11,22–24], protein-protein interactions [25,26], and the effects of mutations [27].”