Work

Intermediate divergence levels maximize the strength of structure–sequence correlations in enzymes and viral proteins

Year: 2016

Type: article

Abstract: Structural properties such as solvent accessibility and contact number predict site-specific sequence variability in many proteins. However, the strength and significance of these structure-sequence relationships vary widely among different proteins, with absolute correlation strengths ranging from 0 to 0.8. In particular, two recent works have made contradictory observations. Yeh et al. (Mol. Biol. Evol. 31:135-139, 2014) found that both relative solvent accessibility (RSA) and weighted contact number (WCN) are good predictors of sitewise evolutionary rate in enzymes, with WCN clearly out-performing RSA. Shahmoradi et al. (J. Mol. Evol. 79:130-142, 2014) considered these same predictors (as well as others) in viral proteins and found much weaker correlations and no clear advantage of WCN over RSA. Because these two studies had substantial methodological differences, however, a direct comparison of their results is not possible. Here, we reanalyze the datasets of the two studies with one uniform analysis pipeline, and we find that many apparent discrepancies between the two analyses can be attributed to the extent of sequence divergence in individual alignments. Specifically, the alignments of the enzyme dataset are much more diverged than those of the virus dataset, and proteins with higher divergence exhibit, on average, stronger structure-sequence correlations. However, the highest structure-sequence correlations are observed at intermediate divergence levels, where both highly conserved and highly variable sites are present in the same alignment.

Source: Protein Science

DOI: 10.1002/pro.2920

Authors: Eleisha L. Jackson, Amir Shahmoradi, Stephanie J. Spielman, Benjamin R. Jack, Claus O. Wilke

Institution: The University of Texas at Austin

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Science Foundation, University of Texas at Austin, National Institutes of Health, National Institute of General Medical Sciences

Awards: BEACON: An NSF Center for the Study of Evolution in Action, Identifying Mutations that Promote Microbial Evolvability, Software development and application of a simulation framework for protein evolution, Graduate Research Fellowship Program, A mechanistic model of bacteriophage T7 infection, replication, and evolution