ArticlePloS one2014
Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- MODEL-FREE INFERENCE FOR CHARACTERIZING PROTEIN MUTATIONS THROUGH A COEVOLUTIONARY LENS.The annals of applied statistics · 2026Article
- AT-TSVM: Improving Transmembrane Protein Inter-Helical Residue Contact Prediction Using Active Transfer Transductive Support Vector Machines.International journal of molecular sciences · 2025Article
- Mapping the energy landscape of a fold-switching protein MAD2.Protein science : a publication of the Protein Society · 2025Article
- PHACE: Phylogeny-Aware Detection of Molecular Coevolution.Molecular biology and evolution · 2025Article
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
- Protein language models learn evolutionary statistics of interacting sequence motifs.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features.International journal of molecular sciences · 2024Article
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- pycofitness-Evaluating the fitness landscape of RNA and protein sequences.Bioinformatics (Oxford, England) · 2024Article
- Improving AlphaFold predicted contacts in alpha-helical transmembrane proteins structures using structural features.Research square · 2023Article
- The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes Based on Phylogenetic Profiles.Systematic biology · 2023Article
- Undersampling and the inference of coevolution in proteins.Cell systems · 2023Article
- Article
- From systems to structure - using genetic data to model protein structures.Nature reviews. Genetics · 2022Review
- Fast and accurate inference of gene regulatory networks through robust precision matrix estimation.Bioinformatics (Oxford, England) · 2022Article
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.PLoS computational biology · 2022Article
- Improved prediction of protein-protein interactions using AlphaFold2.Nature communications · 2022Article
- Deep graph learning of inter-protein contacts.Bioinformatics (Oxford, England) · 2022Article
- Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.Molecular biology and evolution · 2022Article
- Inter-Residue Distance Prediction From Duet Deep Learning Models.Frontiers in genetics · 2022Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
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Abstract
In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code.
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