ArticlePLoS computational biology2021
On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models.Physical review research · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery.RSC chemical biology · 2025Review
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Identification of coevolving positions by ancestral reconstruction.Communications biology · 2025Article
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- Article
- Impact of phylogeny on structural contact inference from protein sequence data.Journal of the Royal Society, Interface · 2023Article
- Protein language models trained on multiple sequence alignments learn phylogenetic relationships.Nature communications · 2022Article
- Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes.Nature communications · 2022Article
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.PLoS computational biology · 2022Article
- Extracting phylogenetic dimensions of coevolution reveals hidden functional signals.Scientific reports · 2022Article
- Global analysis of more than 50,000 SARS-CoV-2 genomes reveals epistasis between eight viral genes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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2 authors.
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Abstract
Coevolution-based contact prediction, either directly by coevolutionary couplings resulting from global statistical sequence models or using structural supervision and deep learning, has found widespread application in protein-structure prediction from sequence. However, one of the basic assumptions in global statistical modeling is that sequences form an at least approximately independent sample of an unknown probability distribution, which is to be learned from data. In the case of protein families, this assumption is obviously violated by phylogenetic relations between protein sequences. It has turned out to be notoriously difficult to take phylogenetic correlations into account in coevolutionary model learning. Here, we propose a complementary approach: we develop strategies to randomize or resample sequence data, such that conservation patterns and phylogenetic relations are preserved, while intrinsic (i.e. structure- or function-based) coevolutionary couplings are removed. A comparison between the results of Direct Coupling Analysis applied to real and to resampled data shows that the largest coevolutionary couplings, i.e. those used for contact prediction, are only weakly influenced by phylogeny. However, the phylogeny-induced spurious couplings in the resampled data are compatible in size with the first false-positive contact predictions from real data. Dissecting functional from phylogeny-induced couplings might therefore extend accurate contact predictions to the range of intermediate-size couplings.
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