Journal of Molecular Evolution

, Volume 79, Issue 3–4, pp 130–142 | Cite as

Predicting Evolutionary Site Variability from Structure in Viral Proteins: Buriedness, Packing, Flexibility, and Design

  • Amir Shahmoradi
  • Dariya K. Sydykova
  • Stephanie J. Spielman
  • Eleisha L. Jackson
  • Eric T. Dawson
  • Austin G. Meyer
  • Claus O. Wilke
Original Article

Abstract

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on nine non-homologous viral protein structures and from variation in homologous variants of those proteins, where they were available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1–0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than the more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

Supplementary material

239_2014_9644_MOESM1_ESM.pdf (185 kb)
Supplementary material 1 (pdf 184 KB)
239_2014_9644_MOESM2_ESM.pdf (488 kb)
Supplementary material 2 (pdf 488 KB)
239_2014_9644_MOESM3_ESM.pdf (248 kb)
Supplementary material 3 (pdf 248 KB)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Amir Shahmoradi
    • 1
    • 2
  • Dariya K. Sydykova
    • 2
  • Stephanie J. Spielman
    • 2
  • Eleisha L. Jackson
    • 2
  • Eric T. Dawson
    • 2
  • Austin G. Meyer
    • 2
  • Claus O. Wilke
    • 2
  1. 1.Department of PhysicsThe University of Texas at AustinAustinUSA
  2. 2.Department of Integrative Biology, Center for Computational Biology and Bioinformatics, and Institute for Cellular and Molecular BiologyThe University of Texas at AustinAustinUSA

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