Computational Identification of Potential Dipeptidyl Peptidase (DPP)-IV Inhibitors: Structure Based Virtual Screening, Molecular Dynamics Simulation and Knowledge Based SAR Studies

Type 2 Diabetes mellitus (T2DM) is a globally leading metabolic problem with increased morbidity and mortality. Current medication therapies in the market to control diabetes are not sufficient and therefore, there is further need to develop more selective and effective treatment approaches. Inhibition of Dipeptidyl-peptidase-IV (DPP-IV) enzyme may serve as an interesting target for developing novel anti-diabetic drug candidate. In the present study, hierarchical virtual screening of drug like compounds was done followed by molecular dynamics simulation and knowledge-based structure-activity relation (SAR) study in order to retrieve hit compound as prospective inhibitors of DPP-IV enzyme. Important amino acid residues present in the active target site were acknowledged as vital and were also found to have similar interactions with the potential hits. Further, in silico technique was undertaken to identify ubiquitous promising hits against DPP-IV enzyme and this was followed by calculation of binding energy and absorption, distribution, metabolism, excretion (ADME) prediction that could possibly support their pharmacokinetic prospective. Furthermore, stability study using molecular dynamics simulation of protein complex was accomplished with the most capable targeted hit established in the present study. In the end, comparative analysis of 3-dimensional binding pose, orientation and planar structure of the potential retrieved hit was done with marketed drugs (alogliptin and sitagliptin) in order to develop knowledge-based structure-activity relationship, which proved the successful designing of DPP-IV enzyme inhibitors.