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Objective: One substance found in the leaves of Garcinia cowa Roxb that has anticancer properties is garcinisidone-A. The study aims to simulate the docking of garcinisidone-A (Gar-A), molecular dynamics, and predict the ADME by predicting the binding of the HER2 protein in breast cancer cells and developing new drug candidate options for cancer treatment, often starting with computational analysis. Methods: The research method involves computational utilization of pkCSM applications, Gar-A docking simulation with the HER2 protein using Gnina software version 1.0.2, and molecular dynamics conducted with GROMACS 2022.2 and CHARMMGUI applications. Results: Gar-A has a molecular weight of less than 500, a Log P value of greater than 5, a limited amount of water solubility, a low level of skin permeability, good intestinal permeability, and a Convolutional Neural Network (CNN) pose score on the HER2 protein of 0.6178. It also does not readily cross the blood-brain barrier, and total clearance values indicate rapid elimination via other excretory routes or enzyme metabolism. Gar-A is thought to have interactions with HER2. There are hydrogen bond interactions with amino acids Lys753 and Asp863, carbon-hydrogen bonds with amino acids Leu785, Ser783, Thr862, and alkyl bonds with amino acids Leu726, Leu852, and Ile767. The stability of the Gar-A-substrate interaction could have been more evident during 100 ns molecular dynamics simulation. Conclusion: The physicochemical properties of Gar-A align with Lipinski's rule for drug candidates. ADME predictions indicate good intestinal permeability for Gar-A; however, it suggests it cannot penetrate the blood-brain barrier. The docking results reveal that Gar-A has a value close to one which indicates similar action to its natural ligand and molecular dynamics simulations that Gar-A is less stable. The results illustrate that Gar-A has the potential as a breast anticancer.
Objective: One substance found in the leaves of Garcinia cowa Roxb that has anticancer properties is garcinisidone-A. The study aims to simulate the docking of garcinisidone-A (Gar-A), molecular dynamics, and predict the ADME by predicting the binding of the HER2 protein in breast cancer cells and developing new drug candidate options for cancer treatment, often starting with computational analysis. Methods: The research method involves computational utilization of pkCSM applications, Gar-A docking simulation with the HER2 protein using Gnina software version 1.0.2, and molecular dynamics conducted with GROMACS 2022.2 and CHARMMGUI applications. Results: Gar-A has a molecular weight of less than 500, a Log P value of greater than 5, a limited amount of water solubility, a low level of skin permeability, good intestinal permeability, and a Convolutional Neural Network (CNN) pose score on the HER2 protein of 0.6178. It also does not readily cross the blood-brain barrier, and total clearance values indicate rapid elimination via other excretory routes or enzyme metabolism. Gar-A is thought to have interactions with HER2. There are hydrogen bond interactions with amino acids Lys753 and Asp863, carbon-hydrogen bonds with amino acids Leu785, Ser783, Thr862, and alkyl bonds with amino acids Leu726, Leu852, and Ile767. The stability of the Gar-A-substrate interaction could have been more evident during 100 ns molecular dynamics simulation. Conclusion: The physicochemical properties of Gar-A align with Lipinski's rule for drug candidates. ADME predictions indicate good intestinal permeability for Gar-A; however, it suggests it cannot penetrate the blood-brain barrier. The docking results reveal that Gar-A has a value close to one which indicates similar action to its natural ligand and molecular dynamics simulations that Gar-A is less stable. The results illustrate that Gar-A has the potential as a breast anticancer.
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.
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