Modulation of folding energy landscape by charge–charge interactions: Linking experiments with computational modeling

Abstract

The kinetics of folding–unfolding of a structurally diverse set of four proteins optimized for thermodynamic stability by rational redesign of surface charge–charge interactions is characterized experimentally. The folding rates are faster for designed variants compared with their wild-type proteins, whereas the unfolding rates are largely unaffected. A simple structure-based computational model, which incorporates the Debye–Hückel formalism for the electrostatics, was used and found to qualitatively recapitulate the experimental results. Analysis of the energy landscapes of the designed versus wild-type proteins indicates the differences in refolding rates may be correlated with the degree of frustration of their respective energy landscapes. Our simulations indicate that naturally occurring wild-type proteins have frustrated folding landscapes due to the surface electrostatics. Optimization of the surface electrostatics seems to remove some of that frustration, leading to enhanced formation of native-like contacts in the transition-state ensembles (TSE) and providing a less frustrated energy landscape between the unfolded and TS ensembles. Macroscopically, this results in faster folding rates. Furthermore, analyses of pairwise distances and radii of gyration suggest that the less frustrated energy landscapes for optimized variants are a result of more compact unfolded and TS ensembles. These findings from our modeling demonstrates that this simple model may be used to: (i) gain a detailed understanding of charge–charge interactions and their effects on modulating the energy landscape of protein folding and (ii) qualitatively predict the kinetic behavior of protein surface electrostatic interactions.