ArticleProteins2015
Amino acid positions subject to multiple coevolutionary constraints can be robustly identified by their eigenvector network centrality scores.
Article in Proteins, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Network analysis of correlated conformational dynamics across the evolution of influenza A virus NS1.bioRxiv : the preprint server for biology · 2026Article
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- Identification of positions in human aldolase a that are neutral for apparent KArchives of biochemistry and biophysics · 2024Article
- Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function.The Journal of biological chemistry · 2024Review
- Identification of a covert evolutionary pathway between two protein folds.Nature communications · 2023Article
- PYK-SubstitutionOME: an integrated database containing allosteric coupling, ligand affinity and mutational, structural, pathological, bioinformatic and computational information about pyruvate kinase isozymes.Database : the journal of biological databases and curation · 2023Article
- Odd one out? Functional tuning of Zymomonas mobilis pyruvate kinase is narrower than its allosteric, human counterpart.Protein science : a publication of the Protein Society · 2022Article
- Structural Plasticity Is a Feature of Rheostat Positions in the Human NaInternational journal of molecular sciences · 2022Article
- Substitutions at a rheostat position in human aldolase A cause a shift in the conformational population.Protein science : a publication of the Protein Society · 2022Article
- Article
- Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation.Protein science : a publication of the Protein Society · 2021Article
- Substitutions at Nonconserved Rheostat Positions Modulate Function by Rewiring Long-Range, Dynamic Interactions.Molecular biology and evolution · 2021Article
- Article
- Rheostat positions: A new classification of protein positions relevant to pharmacogenomics.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2020Article
- Disease-relevant mutations alter amino acid co-evolution networks in the second nucleotide binding domain of CFTR.PloS one · 2020Article
- Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites.Nucleic acids research · 2019Article
- Applying graph theory to protein structures: an Atlas of coiled coils.Bioinformatics (Oxford, England) · 2018Article
- ConKit: a python interface to contact predictions.Bioinformatics (Oxford, England) · 2017Article
- Review
- Data on publications, structural analyses, and queries used to build and utilize the AlloRep database.Data in brief · 2016Article
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Authors and funding
3 authors.
Funding
Abstract
As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for coevolution between pairs of positions. Coevolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of coevolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded coevolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; "central" positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise coevolution scores: instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints-detectable by divergent algorithms--that occur at key protein locations. Finally, we discuss the fact that multiple patterns coexist in evolutionary data that, together, give rise to emergent protein functions.
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