Predictive Modeling of Protein Folding Thermodynamics, Mutational Effects and Free-Energy Landscapes

Abstract

Deciphering the folding mechanism of small single-domain proteins has a long and well-chartered history that has been and still is aided by numerous experimental and computational approaches. The computational tools at the disposal of the folding community range from all-atom molecular simulations to structure-based models. In this review, we highlight one such structure-based statistical mechanical model termed the Wako-Saito-Munoz-Eaton (WSME) model. We have, over the past few years, made the model physically more realistic by systematically introducing mean-field terms for solvation and electrostatics apart from conventional packing interactions. The WSME model can simply be calibrated with equilibrium unfolding curves and various features such as heat capacity thermograms, free-energy surfaces or profiles and hence the folding mechanism, changes in stability upon point mutations or certain post-translational modifications, thermodynamic vs. dynamic effects and possible connections with function fallout of the model without additional calibration. The model requires only a small set of tunable thermodynamic parameters (~3-4) allowing for a tremendous scope in further improvement of its energy function. Most importantly, it can be employed as a rapid, physical and ensemble-based tool to directly characterize experimental equilibrium and kinetic rate and amplitude data (in real world units), that is not conventionally possible in other native-centric treatments. We believe that the WSME model is now poised to address numerous questions in the field of protein folding including pathway heterogeneity, structural-energetic relations, quantifying disorder and the effect of point mutations in disease.