Decoding the Knacks of Ellagitannin Lead Compounds to Treat Nonalcoholic Fatty Liver Disease using Computer-aided Drug Designing.

Background: The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing globally, impacting individuals in Western nations and rapid growing in Asian countries due to sedentary lifestyles; thus, NAFLD has emerged as a significant worldwide health concern. Presently, lifestyle changes represent the primary approach to managing NAFLD.

Methods: This research aimed to identify the potential drug targets for treating NAFLD through comprehensive in silico computational analysis. These include the prediction of the three-dimensional structure of the protein, the prediction of inhibitors by PubChem and ZINC, molecular docking by Autodcok, pharmacophore modeling, molecular dynamics simulation by the OPLS_2005 force field, and the orthorhombic box solvent model Intermolecular Interaction Potential 3 Points Transferable to the selected compound. The toxicity of the lead compounds was analyzed through AdmetSAR software.

Results: The protein associated with the PNPLA3 gene, whose overall three-dimensional structure was 95% accurate, were retrieved following inhibitor selection via PubChem and ZINC. Among the selected inhibitors and docked compounds with ID 10033935 (ellagitannin) showed a minimum E-Score of -17.266. In docking and pharmacophore modeling the compound ellagitannin shows promise as a potential drug candidate. Moreover, the molecular dynamics and structural stability of the protein-ligand complex were evaluated with several metrics such as as root mean square fluctuation and root mean square deviation and resulted in the stability not only of PNPLA3-10033935 (ellagitannin) but also of compound PNPLA3-71448940 and PNPLA3-5748394 complexed proteins at 400 ns with very slight variation.

Conclusion: Overall, ellagitannin was identified as the best druggable target with the best therapeutics profile. The findings of our study can pave the way for the development of a new drug against NALFD.