In-Silico Molecular Docking and ADMET Prediction of Natural Compounds In Oncom

Abstract

Oncom is a traditional fermented cuisine from West Java, Indonesia, produced through the process of mold fermentation. Oncom is categorized into black oncom (BO) and red oncom (RO) based on the colour of the spore of the molds used in their fermentation. Both types of oncom are commonly found in traditional markets in West Java, Indonesia. This study aimed to explore and predict the diverse activities of bioactive compounds from both oncom by analyzing their interactions with multiple enzymes using the in silico method. The ethanol extracts of oncom were analyzed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). The absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions of the substances were performed using pkCSM and ProTox-II network servers, along with Lipinski’s rule of five screening. Molecular docking was performed on β-lactamase (PDB ID: 1LLB), cytochrome P450 (PDB ID: 1JIP), human COX-2 (PDB ID: 3LN1), SARS-COV-2 protease (PDB ID: 6LU7), and protein kinase (PDB ID: 1MV5) using AutoDockTools. The LC-HRMS analysis revealed the presence of 64 compounds in oncom extracts. ADMET together with Lipinski’s guidelines screening, predicted that nine of the 64 compounds showed promising biological activities. Daidzein exhibited a higher Gibbs free energy (∆G) of -6.44 kcal/mol for 1LLB compared to clavulanic acid (-5.07 kcal/mol) and -5.91 kcal/mol for 1MV5 in contrast to verapamil (-4.04 kcal/mol). The findings indicate that daidzein exhibits potential inhibitory activity of bacterial infections and cancer cells.