ArticlePLoS computational biology2013
From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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54 citing papers in PubMed.
- What does it take to learn the rules of RNA base pairing? A lot less than you may think.Communications biology · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- 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
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks.Scientific reports · 2023Article
- Undersampling and the inference of coevolution in proteins.Cell systems · 2023Article
- Unveiling the Inhibitory Potentials of Peptidomimetic Azanitriles and Pyridyl Esters towards SARS-CoV-2 Main Protease: A Molecular Modelling Investigation.Molecules (Basel, Switzerland) · 2023Article
- Coevolution-based prediction of key allosteric residues for protein function regulation.eLife · 2023Article
- Bézier interpolation improves the inference of dynamical models from data.Physical review. E · 2023Article
- Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes.Nature communications · 2022Article
- Structural Investigations and Binding Mechanisms of Oseltamivir Drug Resistance Conferred by the E119V Mutation in Influenza H7N9 Virus.Molecules (Basel, Switzerland) · 2022Article
- Extracting phylogenetic dimensions of coevolution reveals hidden functional signals.Scientific reports · 2022Article
- On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.PLoS computational biology · 2021Article
- Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models.Statistica Sinica · 2021Article
- Mi3-GPU: MCMC-based Inverse Ising Inference on GPUs for protein covariation analysis.Computer physics communications · 2021Article
- Mechanical couplings of protein backbone and side chains exhibit scale-free network properties and specific hotspots for function.Computational and structural biotechnology journal · 2021Article
- Epistatic contributions promote the unification of incompatible models of neutral molecular evolution.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- A statistical inference approach to reconstruct intercellular interactions in cell migration experiments.Science advances · 2020Article
- Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity.Scientific reports · 2020Article
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3 authors.
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Abstract
Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold.
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