Identification of novel natural inhibitors ofOpisthorchis felineuscytochrome P450 using structure-based screening and molecular dynamic simulation

Abstract

Opisthorchis felineus is the etiological agent of opisthorchiasis in humans. O. felineus cytochrome P450 (OfCYP450) is an important enzyme in the parasite xenobiotic metabolism. To identify the potential anti-opisthorchid compound, we conducted a structure-based virtual screening of natural compounds from the ZINC database (n = 1,65,869) against the OfCYP450. The ligands were screened against OfCYP450 in four sequential docking modes that resulted in 361 ligands having better docking score. These compounds were evaluated for Lipinski and ADMET prediction, and 10 compounds were found to fit well with re-docking studies. After refinement by docking and drug-likeness analyses, four potential inhibitors (ZINC2358298, ZINC8790946, ZINC70707116, and ZINC85878789) were identified. These ligands with reference compounds (itraconazole and fluconazole) were further subjected to molecular dynamics simulation (MDS) and binding energy analyses to compare the dynamic structure of protein after ligand binding and the stability of the OfCYP450 and bound complexes. The binding energy analyses were also calculated. The results suggested that the compounds had a negative binding energy with -259.41, -110.09, -188.25, -163.30, -202.10, and -158.79 kJ mol for itraconazole, fluconazole, and compounds with IDs ZINC2358298, ZINC8790946, ZINC70707116, and ZINC85878789, respectively. These lead compounds displayed significant pharmacological and structural properties to be drug candidates. On the basis of MDS results and binding energy analyses, we concluded that ZINC8790946, ZINC70707116, and ZINC85878789 have excellent potential to inhibit OfCYP450.

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Identification of lead molecules against potential drug target protein MAPK4 from L. donovani: An in-silico approach using docking, molecular dynamics and binding free energy calculation

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High-throughput screening of natural compounds and inhibition of a major therapeutic target HsGSK-3β for Alzheimer’s disease using computational approaches

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