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    Amino acid positions subject to multiple co-evolutionary constraints can be robustly identified by their eigenvector network centrality scores

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    Issue Date
    2015-12
    Author
    Parente, Daniel Joseph
    Ray, J. Christian J.
    Liskin, Swint-Kruse
    Publisher
    Wiley
    Type
    Article
    Article Version
    Scholarly/refereed, author accepted manuscript
    Rights
    This is the peer reviewed version of the following article: Proteins, which has been published in final form at http://dx.doi.org/10.1002/prot.24948. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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    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 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.
    URI
    http://hdl.handle.net/1808/23504
    DOI
    https://doi.org/10.1002/prot.24948
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    • Molecular Biosciences Scholarly Works [581]
    Citation
    Parente, 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. http://doi.org/10.1002/prot.24948

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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