ArticleProceedings of the National Academy of Sciences of the United States of America2013
Coevolutionary signals across protein lineages help capture multiple protein conformations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
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Who cites it
97 citing papers in PubMed.
- A structure-aware generative AI framework for revealing functional relationships in protein families.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Disentangling coevolutionary constraints for modeling protein conformational heterogeneity.Communications chemistry · 2026Article
- Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models.Nature communications · 2026Article
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Memorization bias impacts modeling of alternative conformational states of solute carrier membrane proteins with methods from deep learning.PLoS computational biology · 2025Article
- Evolutionary Constraints Guide AlphaFold2 in Predicting Alternative Conformations and Inform Rational Mutation Design.Journal of chemical information and modeling · 2025Article
- Memorization Bias Impacts Modeling of Alternative Conformational States of Symmetric Solute Carrier Membrane Proteins with Methods from Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Generating the polymorph landscapes of amyloid fibrils using AI: RibbonFold.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evolving concepts of the protein universe.iScience · 2025Review
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Residue coevolution and mutational landscape for OmpR and NarL response regulator subfamilies.Biophysical journal · 2024Article
- pycofitness-Evaluating the fitness landscape of RNA and protein sequences.Bioinformatics (Oxford, England) · 2024Article
- Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction.Journal of chemical information and modeling · 2024Article
- Article
- Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis.Nature communications · 2023Article
- Evolutionary selection of proteins with two folds.Nature communications · 2023Article
- Coevolution-based prediction of key allosteric residues for protein function regulation.eLife · 2023Article
- Article
37 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ~1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.
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