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• W2904361469 abstract "Potts statistical models have become a popular and promising way to analyze mutational covariation in protein multiple sequence alignments (MSAs) in order to understand protein structure, function, and fitness. But the statistical limitations of these models, which can have millions of parameters and are fit to MSAs of only thousands or hundreds of effective sequences using a procedure known as inverse Ising inference, are incompletely understood. In this work we predict how model quality degrades as a function of the number of sequences N, sequence length L, amino-acid alphabet size q, and the degree of conservation of the MSA, in different applications of the Potts models: in fitness predictions of individual protein sequences, in predictions of the effects of single-point mutations, in double mutant cycle predictions of epistasis, and in 3D contact prediction in protein structure. We show how as MSA depth N decreases an overfitting effect occurs such that sequences in the training MSA have overestimated fitness, and we predict the magnitude of this effect and discuss how regularization can help correct for it, using a regularization procedure motivated by statistical analysis of the effects of finite sampling. We find that as N decreases the quality of point-mutation effect predictions degrade least, fitness and epistasis predictions degrade more rapidly, and contact predictions are most affected. However, overfitting becomes negligible for MSA depths of more than a few thousand effective sequences, as often used in practice, and regularization becomes less necessary. We discuss the implications of these results for users of Potts covariation analysis." @default.
• W2904361469 created "2018-12-22" @default.
• W2904361469 creator A5033874771 @default.
• W2904361469 creator A5061246105 @default.
• W2904361469 date "2019-03-05" @default.
• W2904361469 modified "2023-10-14" @default.
• W2904361469 title "Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation" @default.
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• W2904361469 doi "https://doi.org/10.1103/physreve.99.032405" @default.
• W2904361469 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6508952" @default.
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• W2904361469 hasPublicationYear "2019" @default.
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