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Theoretical Insights into the Biophysics of Protein Bi-stability and Evolutionary Switches.

Authors :
Sikosek, Tobias
Krobath, Heinrich
Chan, Hue Sun
Source :
PLoS Computational Biology; 6/2/2016, Vol. 12 Issue 6, p1-27, 27p
Publication Year :
2016

Abstract

Deciphering the effects of nonsynonymous mutations on protein structure is central to many areas of biomedical research and is of fundamental importance to the study of molecular evolution. Much of the investigation of protein evolution has focused on mutations that leave a protein’s folded structure essentially unchanged. However, to evolve novel folds of proteins, mutations that lead to large conformational modifications have to be involved. Unraveling the basic biophysics of such mutations is a challenge to theory, especially when only one or two amino acid substitutions cause a large-scale conformational switch. Among the few such mutational switches identified experimentally, the one between the G<subscript>A</subscript> all-α and G<subscript>B</subscript> α+β folds is extensively characterized; but all-atom simulations using fully transferrable potentials have not been able to account for this striking switching behavior. Here we introduce an explicit-chain model that combines structure-based native biases for multiple alternative structures with a general physical atomic force field, and apply this construct to twelve mutants spanning the sequence variation between G<subscript>A</subscript> and G<subscript>B</subscript>. In agreement with experiment, we observe conformational switching from G<subscript>A</subscript> to G<subscript>B</subscript> upon a single L45Y substitution in the GA98 mutant. In line with the latent evolutionary potential concept, our model shows a gradual sequence-dependent change in fold preference in the mutants before this switch. Our analysis also indicates that a sharp G<subscript>A</subscript>/G<subscript>B</subscript> switch may arise from the orientation dependence of aromatic π-interactions. These findings provide physical insights toward rationalizing, predicting and designing evolutionary conformational switches. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
12
Issue :
6
Database :
Complementary Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
115855533
Full Text :
https://doi.org/10.1371/journal.pcbi.1004960