Cryptosporidiosis, a public health challenge: A combined 3D shape-based virtual screening, docking study, and molecular dynamics simulation approach to identify inhibitors with novel scaffolds for the treatment of cryptosporidiosis

Abstract

Cryptosporidiosis is a neglected tropical disease caused by the protozoan parasite Cryptosporidium parvum. Limited therapeutic options, limitation in in vitro parasite culture, and lack of a reliable animal model of parasite for replication of in vivo life cycle and drug testing demand alternative methods for drug development. The in silico methods of drug discovery prove a crucial process in such conditions.Recent research reported a limited number of small molecules for drug development. Purine nucleotide biosynthesis in Cryptosporidium species is dependent on the IMPDH (CpIMPDH) enzyme, so distortion of parasite IMPDH has been pursued as a compelling strategy for curbing Cryptosporidium infection due to its different kinetics from the host enzyme. Our study's primary aim was to discover novel ligand molecules with noticeable activity against Cryptosporidium parvum IMPDH. For this purpose, we selected 18 previously discovered ligands to understand the interaction feature between ligand and receptor, and their shape and electronic features are employed as a template for shape-based virtual screening of the ZINC database (drug-like subset) search approach via Schrodinger-2019 (Maestro 11.9). The obtained hits were subsequently subjected to structure-based screening, quantum polarized ligand docking (QPLD), and molecular dynamics simulations to fetch potential small molecules with the highest binding affinity for CpIMPDH protein. Further ligand binding energy and pharmacokinetic analysis were also taken into consideration as filtering criteria for selecting the most promising drug-like compounds. On this experimentation analysis, three top-ranked (ZINC24855054, ZINC58171263, and ZINC08000072) molecules were found to have appropriate pharmacokinetic properties along with surpassing in silico inhibitory potential towards the CpIMPDH compared to known inhibitors. The molecular docking and molecular dynamics simulation analysis results satisfactorily confirmed the inhibitory action. Therefore, these new scaffolds deduced by the presented computational methodology could recommend lead molecules for designing promising anti-cryptosporidial drugs targeting CpIMPDH protein.