PHARMACOINFORMATICS ANALYSIS OF MORUS MACROURA FOR DRUG DISCOVERY AND DEVELOPMENT

Abstract

Objective: Pharmacoinformatics is an innovative approach rapidly evolving in pharmaceutical research and drug development. This study focuses on analysing Morus macroura, a plant species with untapped pharmacological potential. This investigation aims to leverage pharmacoinformatics techniques to unveil the hidden potential of Morus macroura in drug discovery and development.
 Methods: The study includes analyses of protein-protein interactions, deep learning docking, adsorption tests, distribution, metabolism, excretion, molecular dynamics simulations and free energy calculation using Molecular Mechanics Generalized Born Surface Area (MMGBSA).
 Results: Nine active compounds were identified in Morus macroura, namely Andalasin A, Guangsangon K, Guangsangon L, Guangsangon M, Guangsangon N, Macrourone C, Mulberrofuran G, Mulberrofuran K, and Mulberroside C. These compounds exhibit protein-protein interaction activities against a cytochrome P450 monooxygenase that catalyses the conversion of C19 androgens. These plant compounds influence aromatase excess syndrome, deficiency, and ovarian dysgenesis. Regarding drug-likeness, Mulberroside C and Macrourone C demonstrated good absorption potential by adhering to Lipinski's rule of five. Deep learning docking simulations yielded affinity results of-9.62 kcal/mol for Guangsangon M,-10.44 kcal/mol for Macrourone C, and-10.99 kcal/mol for Guangsangon L. Subsequent molecular dynamics simulations indicated that Guangsangon L and Macrourone C remained stable during a 100 ns simulation.
 Conclusion: Morus macroura interacts with important proteins, particularly CYP19A1, which might influence health conditions like aromatase excess syndrome and ovarian dysgenesis. These findings provide potential paths for addressing specific health issues and advancing drug development. Molecular dynamics simulations indicated that Guangsangon L and Macrourone C remained stable during simulation.