ArticleeLife2015
Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis.
Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Selection for Folding Stability Predicts Observed Covariation Between Protein Positions in the PDB.Journal of molecular evolution · 2026Article
- Adaptive evolution and co-evolution of chloroplast genomes in Pteridaceae species occupying different habitats: overlapping residues are always highly mutated.BMC plant biology · 2023Article
- Chasing long-range evolutionary couplings in the AlphaFold era.Biopolymers · 2023Article
- Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes.Computational and structural biotechnology journal · 2021Article
- Sixty-five years of the long march in protein secondary structure prediction: the final stretch?Briefings in bioinformatics · 2018Article
- Origins of coevolution between residues distant in protein 3D structures.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Editorial: Evolution acting on the same target, but at multiple levels: Proteins as the test case.Journal of biosciences · 2017Article
- Structure of FIV capsid C-terminal domain demonstrates lentiviral evasion of genetic fragility by coevolved substitutions.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Methods for analysing correlated mutations in proteins are becoming an increasingly powerful tool for predicting contacts within and between proteins. Nevertheless, limitations remain due to the requirement for large multiple sequence alignments (MSA) and the fact that, in general, only the relatively small number of top-ranking predictions are reliable. To date, methods for analysing correlated mutations have relied exclusively on amino acid MSAs as inputs. Here, we describe a new approach for analysing correlated mutations that is based on combined analysis of amino acid and codon MSAs. We show that a direct contact is more likely to be present when the correlation between the positions is strong at the amino acid level but weak at the codon level. The performance of different methods for analysing correlated mutations in predicting contacts is shown to be enhanced significantly when amino acid and codon data are combined.
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Registered trials
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