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dc.contributor.authorParente, Daniel Joseph
dc.contributor.authorRay, J. Christian J.
dc.contributor.authorLiskin, Swint-Kruse
dc.identifier.citationParente, D. J., Ray, J. C. J., & Swint-Kruse, L. (2015). Amino acid positions subject to multiple co-evolutionary constraints can be robustly identified by their eigenvector network centrality scores. Proteins, 83(12), 2293–2306.
dc.description.abstractAs 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 co-evolution between pairs of positions. Co-evolutionary 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 co-evolution 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 co-evolution 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 co-evolution 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 co-exist in evolutionary data that, together, give rise to emergent protein functions.en_US
dc.rightsThis is the peer reviewed version of the following article: Proteins, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.en_US
dc.subjectProtein evolutionen_US
dc.subjectAmino aciden_US
dc.subjectSequence alignmenten_US
dc.subjectGraph theoryen_US
dc.titleAmino acid positions subject to multiple co-evolutionary constraints can be robustly identified by their eigenvector network centrality scoresen_US
kusw.kuauthorRay, J. Christian J.
kusw.kudepartmentMolecular Biosciencesen_US
kusw.oanotesPer SHERPA/RoMEO 3/28/2017: Author's Pre-print: green tick author can archive pre-print (ie pre-refereeing) Author's Post-print: grey tick subject to Restrictions below, author can archive post-print (ie final draft post-refereeing) Restrictions:

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Some journals have separate policies, please check with each journal directly On author's personal website, institutional repositories, arXiv, AgEcon, PhilPapers, PubMed Central, RePEc or Social Science Research Network Author's pre-print may not be updated with Publisher's Version/PDF Author's pre-print must acknowledge acceptance for publication Non-Commercial Publisher's version/PDF cannot be used Publisher source must be acknowledged with citation Must link to publisher version with set statement (see policy) If OnlineOpen is available, BBSRC, EPSRC, MRC, NERC and STFC authors, may self-archive after 12 months
kusw.oaversionScholarly/refereed, author accepted manuscripten_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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