Protein Stability Changes upon Point Mutations Identified with a Gaussian Network Model Simulating Protein Unfolding Behavior

Abstract

Understanding the effects of missense mutations on protein stability is a widely acknowledged significant biological problem. Genomic missense mutations may alter one or more amino acids leading to increased or decreased stability of the encoded proteins. In this study, we propose a novel approach - Protein Stability Prediction with a Gaussian Network Model (PSP-GNM) to study the effect of single amino acid substitutions on protein stability. Specifically, PSP-GNM employs a coarse-grained Gaussian Network Model (GNM) that has interactions between amino acids weighted by the Miyazawa-Jernigan (MJ) statistical potential. We use PSP-GNM to simulate partial unfolding of the wildtype and mutant structures and then, use the difference in energies of the unfolded wildtype and mutant protein structures to estimate the experimentally obtained unfolding free energy change (ΔΔG). We verify the extent of correspondence between the ΔΔG calculated by PSP-GNM and the ΔΔG obtained experimentally using three datasets: 350 forward mutations from 66 proteins, 2298 forward mutations from 126 proteins and 611 forward and reverse mutations from 66 proteins and observe Pearson correlation coefficient (PCC) as high as 0.58 and root mean-squared error (RMSE) as low as 1.24 kcal/mol. The performance is comparable to the existing state of the art methods. Importantly, we do observe an increase in the correlation to 0.73 and decrease in RMSE to 1.07 when considering only those measurements made close to 25°C and neutral pH, suggesting a strong dependence on temperature and pH. PSP-GNM is written in Python and is available as a free downloadable package at https://github.com/sambitmishra0628/PSP-GNM .