Simulations of mutant p53 DNA binding domains reveal a novel druggable pocket.

The DNA binding domain (DBD) of the tumor suppressor p53 is the site of several oncogenic mutations. A subset of these mutations lowers the unfolding temperature of the DBD. Unfolding leads to the exposure of a hydrophobic ß-strand and nucleates aggregation which results in pathologies through loss of function and dominant negative/gain of function effects. Inspired by the hypothesis that structural changes that are associated with events initiating unfolding in DBD are likely to present opportunities for inhibition, we investigate the dynamics of the wild type (WT) and some aggregating mutants through extensive all atom explicit solvent MD simulations. Simulations reveal differential conformational sampling between the WT and the mutants of a turn region (S6-S7) that is contiguous to a known aggregation-prone region (APR). The conformational properties of the S6-S7 turn appear to be modulated by a network of interacting residues. We speculate that changes that take place in this network as a result of the mutational stress result in the events that destabilize the DBD and initiate unfolding. These perturbations also result in the emergence of a novel pocket that appears to have druggable characteristics. FDA approved drugs are computationally screened against this pocket.

The Multifaceted Roles of Molecular Dynamics Simulations in Drug Discovery.

Discovery of new therapeutics is a very challenging, expensive and time-consuming process. With the number of approved drugs declining steadily, combined with increasing costs, a rational approach is needed to facilitate, expedite and streamline the drug discovery process. In silico methods are playing key roles in the discovery of a growing number of marketed drugs. The use of computational approaches, particularly molecular dynamics, in drug design is rapidly gaining momentum and acceptance as an essential part of the toolkit for modern drug discovery. From analysing atomistic details for explaining experimentally observed phenomena, to designing drugs with increased efficacy and specificity, the insight that such simulations can provide is generating new ideas and applications that have previously been unexplored. Here we discuss physics-based simulation methodologies and applications in drug design: from locating pockets to designing novel lead compounds, from small molecules to peptides. With developments in hardware, software and theory, the improved predictive abilities of in silico efforts are becoming an essential part of efficient, economic and accurate drug development strategies.