COMPUTATIONAL STUDY OF COMPOUNDS IN MANGOSTEEN (GARCINIA MANGOSTANA L.) AS A CANDIDATE OF LUNG CANCER THERAPY
DOI:
https://doi.org/10.22159/ijap.2025.v17s1.08Keywords:
Lung cancer, Garcinia mangostana L., Network pharmacology, Protein-protein interaction, Deep learning docking, Molecular dynamics simulationsAbstract
Objective: Cancer involves uncontrolled cell growth and spreading to other body parts. Lung cancer is the most common and deadliest cancer worldwide, with treatments often causing significant side effects. This research aims to predict the potential of compounds in mangosteen (Garcinia mangostana L.) as a candidate for lung cancer therapy.
Methods: The methods used in this research are network pharmacology analysis using string and cytoscape, molecular docking using deep learning, and molecular dynamics simulations.
Results: Eleven compounds have been identified in Garcinia mangostana L., including catechin, gartanin, alpha-mangostin, norathyriol, maclurin, 8-deoxygartanin, beta-mangostin, gamma-mangostin, garcinone A, garcinone B, and garcinone D. Based on ADMET analysis, these compounds exhibit varying degrees of absorption, distribution, metabolism, excretion, and toxicity profiles, which can provide valuable insights into their potential therapeutic applications and safety profiles. It has significant protein targets identified are AURKA, PLK1, CCNA2, and KIF11, with AURKA chosen for molecular docking and molecular dynamics simulations. Molecular docking revealed garcinone D has a binding energy -10.30 kcal/mol and gamma-Mangostin -10.28 kcal/mol had better affinity than the native ligand adenosine-5'-diphosphate -9.00 kcal/mol. Molecular dynamics simulations indicated that garcinone D and gamma-Mangostin were less stable than the native ligand over a 100 ns simulation.
Conclusion: The compounds, including gamma-Mangostin and garcinone D, target the lung cancer-related protein AURKA and are demonstrate to affect key biological pathways such as the cell cycle and motor proteins. Deep learning docking shows that garcinone D and gamma-mangostin exhibit high affinity, while molecular dynamics simulations confirm their stability over 100 ns.
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