Identification of a novel human dihydrofolate reductase inhibitor by virtual screening, docking, ADMET prediction and molecular dynamics simulations

Dihydrofolate reductase (DHFR) is a critical enzyme that catalyzes the reduction of dihydrofolate totetrahydrofolate (THF), responsible of the synthesis of raw material for cell growth and proliferation.Inhibition of DHFR resulted in a deficiency of THF and cell death. This feature makes human DHFR(hDHFR) as an attractive target for cancer therapy. Accordingly, we aimed to identify a novel effectivehDHFR inhibitor using series of in silico approaches. In this study, we performed structure-based virtualscreening of the PubChem database to identify potent hDHFR inhibitors. First, we retrieved all compoundsfrom the PubChem database having at least ۹۰% structural similarity with the known natural hDHFRinhibitors. Afterwards, the compounds were screened through molecular docking methods against hDHFRto investigate protein–ligand interaction and estimate their binding affinities. The sixteen compounds withhigher binding affinity compared to methotrexate (MTX), as a reference compound, were selected. Thesecompounds displayed essential molecular orientation and interactions with key residues of the hDHFR activesite. They further investigated by Lipinski and ADMET prediction. At this stage, nondrug-like compoundswere excluded based on computational approaches. Then, potential inhibitor (PubChem CID: ۴۶۸۸۶۸۱۲)were identified and docked into the actives sites of hDHFR to investigate the binding modes. Finally,molecular dynamics (MD) simulation carried out to evaluate the stability of ligand-target complexes. Wefound that binding of CID: ۴۶۸۸۶۸۱۲ stabilized the structure of hDHFR with the lowest fluctuations. Inconclusion, this compound (CID: ۴۶۸۸۶۸۱۲) can be suggested as a potential anti-cancer drug due to its nontoxicity,high binding affinity, and specificity towards the inhibition of hDHFR.