Quantitative Phytochemical Investigation, Antibacterial Potency, and Drug-ability Assessment of Three Indian Medicinal Plants Leaf Extracts Using Bioinformatics Tools

Natural regimens have long-held ethnomedicinal values, serving as primary sources for mainstream medicine. Therefore, scientists are paying more attention to studying the biological activity of existing plant species in organized ways to select potent bioactive metabolites to use for specific therapeutic purposes. This study used the same approach to find potent antibacterial phytoconstituents in three well-known Indian medicinal plants: Psidium guajava L., Syzygium cumini L. and Punica granatum L. In the earlier study, methanolic leaf extracts of the above plant extracts were more effective than n-hexane extracts against biofilm and drug-resistant pathogenic bacteria. Accordingly, we selected methanolic crude extracts for gas chromatography-mass spectrometry (GC-MS) to identify the phytoconstituents presented. In addition, we added a few reported candidates from the above plant extracts for molecular docking studies against four bacterial targets. For molecular docking studies, we retrieved all phytoconstituents or ligands from the PubChem database and bacterial target proteins from the protein data bank using PyRx 0.8-AutoDock 4.2 software. Furthermore, we used various bioinformatics and chemoinformatics tools to examine the investigated phytoconstituents physicochemical properties, toxicity, and drug-ability profiles. Out of the 30 GC-MS report-derived candidates from three plants, P5 from P. guajava, P18 from S. cumini, and P21 from P. granatum had the potent binding ability with bacterial targets. In the same way, out of the 30 reported candidates, P39, P43, and P56 from three plants, along with amikacin, showed strong binding against the same bacterial target. Both sets of candidates showed favorable physicochemical and toxicity profiles; however, all GC-MS-derived and few reported candidates exhibited negative drug-likeness. The study reveals that these crude extracts have antibacterial properties because they contain both GC-MS and existing phytoconstituents. The study starts with a crude extraction and then uses bioinformatics to choose two possible antibacterial candidates, ursolic acid and punicacortein A. This platform could be useful for finding an antibacterial agent that works specifically on a specific target. To sum up, the study encourages the isolation of more bioactive candidates from different Indian medicinal plants and uses bioinformatics tools to speed up the selection of strong leads that can speed up the process of making antibacterial drugs within limited resources.