Predicting Evolutionary Site Variability from Structure in Viral Proteins: Buriedness, Packing, Flexibility, and Design
- 549 Downloads
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.
This work was supported in part by NIH Grant R01 GM088344, DTRA Grant HDTRA1-12-C-0007, ARO Grant W911NF-12-1-0390, and the BEACON Center for the Study of Evolution in Action (NSF Cooperative Agreement DBI-0939454). The Texas Advanced Computing Center at UT Austin provided high-performance computing resources.
- Burger L, van Nimwegen E (2010) Disentangling direct from indirect co-evolution of residues in protein alignments. PLoS Comput Biol 6(e1000):633Google Scholar
- Franzosa EA, Xia Y (2012) Independent effects of protein core size and expression on residue-level structure-evolution relationships. PLoS ONE 7(e46):602Google Scholar
- Jackson EL, Ollikainen N, Covert III AW, Kortemme T, Wilke CO (2013) Amino-acid site variability among natural and designed proteins. PeerJ 1:e211Google Scholar
- Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935, doi:10.1063/1.445869, http://scitation.aip.org/content/aip/journal/jcp/79/2/10.1063/1.445869
- Kryazhimskiy S, Plotkin JB (2008) The population genetics of dN/dS. PLoS Genet 4(e1000):304Google Scholar
- Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, Jacak R, Kaufman K, Renfrew DP, Smith CA, Sheffler W, Davis IW, Cooper S, Treuille A, Mandell DJ, Richter F, Ban YEA, Fleishman SJ, Corn JE, Kim DE, Lyskov S, Berrondo M, Mentzer S, Popović Z, Havranek JJ, Karanicolas J, Das R, Meiler J, Kortemme T, Gray JJ, Kuhlman B, Baker D, Bradley P (2011) ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 487:545–574PubMedCentralCrossRefPubMedGoogle Scholar
- Liberles DA, Teichmann SA, Bahar I, Bastolla U, Bloom J, BornbergBauer E, Colwell LJ, de Koning APJ, Dokholyan NV, Echave J, Elofsson A, Gerloff DL, Goldstein RA, Grahnen JA, Holder MT, Lakner C, Lartillot N, Lovell SC, Naylor G, Perica T, Pollock DD, Pupko T, Regan L, Roger A, Rubinstein N, Shakhnovich E, Sjölander K, Sunyaev S, Teufel AI, Thorne JL, Thornton JW, Weinreich DM, Whelan S (2012) The interface of protein structure, protein biophysics, and molecular evolution. Protein Sci 21:769–785PubMedCentralCrossRefPubMedGoogle Scholar
- Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, Zecchina R, Sander C (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6(e28):766Google Scholar
- Meyer AG, Dawson ET, Wilke CO (2013) Cross-species comparison of site-specific evolutionary-rate variation in influenza haemagglutinin. Phil Trans R Soc B 368(20120):334Google Scholar
- Ollikainen N, Kortemme T (2013) Computational protein design quantifies structural constraints on amino acid covariation. PLoS Comput Biol 9(e1003):313Google Scholar
- Tien MZ, Meyer AG, Sydykova DK, Spielman SJ, Wilke CO (2013) Maximum allowed solvent accessibilites of residues in proteins. PLOS ONE 8(e80):635Google Scholar
- Yeh SW, Huang TT, Liu JW, Yu SH, Shih CH, Hwang JK (2014) Echave J (2014a) Local packing density is the main structural determinant of the rate of protein sequence evolution at site level. BioMed Res Int 572:409Google Scholar