ArticlePhysical review. E2019
Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation.
Article in Physical review. E, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 63 citations in OpenAlex.
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- GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.bioRxiv : the preprint server for biology · 2026Article
- Predicting Protein Folding Dynamics Using Sequence Information.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models.Physical review research · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
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- Gauge fixing for sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A Perspective on Protein Structure Prediction Using Quantum Computers.Journal of chemical theory and computation · 2024Review
- Undersampling and the inference of coevolution in proteins.Cell systems · 2023Article
- Contingency and Entrenchment of Drug-Resistance Mutations in HIV Viral Proteins.The journal of physical chemistry. B · 2022Article
- Unique features of different classes of G-protein-coupled receptors revealed from sequence coevolutionary and structural analysis.Proteins · 2022Article
- Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.Molecular biology and evolution · 2022Article
- Article
- The generative capacity of probabilistic protein sequence models.Nature communications · 2021Article
- Mi3-GPU: MCMC-based Inverse Ising Inference on GPUs for protein covariation analysis.Computer physics communications · 2021Article
- Remote homology search with hidden Potts models.PLoS computational biology · 2020Article
- Article
- Symmetry, gauge freedoms, and the interpretability of sequence-function relationships.Physical review researchArticle
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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.
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