Unravelling Hot Spots: a comprehensive computational mutagenesis study

Abstract

As protein–protein interactions are critical for all biological functions, representing a large and important class of targets for human therapeutics, identification of protein–protein interaction sites and detection of specific amino acid residues that contribute to the specificity and strength of protein interactions is very important in the biochemistry field. Alanine scanning mutagenesis has allowed the discovery of energetically crucial determinants for protein association that have been defined as hot spots. Systematic experimental mutagenesis is very laborious and time-consuming to perform, and thus it is important to achieve an accurate, predictive computational methodology for alanine scanning mutagenesis, capable of reproducing the experimental mutagenesis values. Having as a basis the MM–PBSA approach first developed by Massova et al., we performed a complete study of the influence of the variation of different parameters, such as the internal dielectric constant, the solvent representation, and the number of trajectories, in the accuracy of the free energy binding differences. As a result, we present here a very simple and fast methodological approach that achieved an overall success rate of 82% in reproducing the experimental mutagenesis data.