Comparative study of covalent & non-covalent drug inhibitory mechanism investigation: targeting HSP72 protein in cancer therapy using molecular modelling techniques.

Abstract

Cancer is the most complicated and diverse disease that has been menacing human beings worldwide. Up to date, important advancement has been done to improve the existing therapeutic interventions in the treatment and management of cancer. However, the side effect of these drugs that are mostly associated with the “off-target” effects is a perpetual failure in cancer drug development. Therefore, efficient regimen with minimal toxicities and high drug target selectivity should be achieved. Covalent inhibition is an emerging field in drug discovery and a very distinct category of therapeutics that reduces adverse side effects and possible interactions that lead to drug resistance due to its attainable reactivity and high selectivity. The Heat shock proteins (HSPs) play a crucial role in the clearance of damaged proteins by encouraging proteotoxicity and proteins acclamation. This process occurs by avoiding unsuitable stress-induced protein aggregation, ensure suitable refolding of denatured proteins, and promoting their degradation; thus, the involvement of this enzyme in many human diseases, including cancer. In this study, we delve into the structural features of one of the most crucial enzymatic targets of the stress proteins, the Heat shock proteins72. In drug development, the integration of computational techniques including molecular dynamic simulations, docking and molecular modelling has allowed drug developers to screen and syntheses millions of compounds and thus screen out possible lead drugs. Computer-Aided Drug Design has been validated as a cost-effective strategy to fast trace the drug discovery process due to these in silico methods. One of the characteristics of the HSP72 is its ability to be targeted either covalently or non-covalently through small drug molecules. Therefore, the above-mentioned methods, amongst several other computational tools were employed out in this study to provide insights into conformational changes that explain potential covalent and non-covalent inhibitory mechanisms, binding sites assessment features leading to promising small molecule inhibitor candidates. These combinatorial computational studies offer an inclusive in silico perspective to fill the gap in drug design studies about targeting protein degradation, thus providing insights toward the structural characteristics of the pivotal target and describing promising drug developments.