In silico Identification and Optimization of Natural Inhibitors for Drug Target Sites in Cryptosporidium parvum: A Review

Abstract

Cryptosporidium parvum is the most common enteric protozoan pathogens affecting humans worldwide. Currently approved drugs to treat cryptosporidiosis are ineffective and no vaccines exist against C. parvum. Here, We docked benzoxazole derivatives collected from literature with Cryptosporidium parvum inosine 5′-monophosphate dehydrogenase using AutoDock4.2 tool, which resulted in energy-based descriptors such as Binding Energy, Intermolecular Energy, Internal Energy, Torsional Energy, vdW + Hbond + desolv Energy and electrostatic energy. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. After that, we have built quantitative structure activity relationship (QSAR) model using energy-based descriptors yielding correlation coefficient r2 of 0.7948. To assess the predictive performance of QSAR model, different cross-validation procedures were adopted. Our results suggests that ligand-receptor binding interactions for inosine 5′-monophosphate dehydrogenase employing QSAR modeling seems to be a promising approach to design more potent inosine 5′-monophosphate dehydrogenase inhibitors prior to their synthesis.