ArticleCell systems2023
Undersampling and the inference of coevolution in proteins.
Article in Cell systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- Evolutionary pathways in epistatic mechanical networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Exploring Evolution to Uncover Insights Into Protein Mutational Stability.Molecular biology and evolution · 2025Article
- Deep-learning-based design of synthetic orthologs of SH3 signaling domains.Cell systems · 2024Article
- Physical modeling of embryonic transcriptomes identifies collective modes of gene expression.bioRxiv : the preprint server for biology · 2024Article
- Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- Inferring couplings in networks across order-disorder phase transitions.Physical review researchArticle
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
Protein structure, function, and evolution depend on local and collective epistatic interactions between amino acids. A powerful approach to defining these interactions is to construct models of couplings between amino acids that reproduce the empirical statistics (frequencies and correlations) observed in sequences comprising a protein family. The top couplings are then interpreted. Here, we show that as currently implemented, this inference unequally represents epistatic interactions, a problem that fundamentally arises from limited sampling of sequences in the context of distinct scales at which epistasis occurs in proteins. We show that these issues explain the ability of current approaches to predict tertiary contacts between amino acids and the inability to obviously expose larger networks of functionally relevant, collectively evolving residues called sectors. This work provides a necessary foundation for more deeply understanding and improving evolution-based models of proteins.
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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.