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

View/ Open
Issue Date
2015-12Author
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.
Metadata
Show full item recordAbstract
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.
Collections
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
Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.