In Silico Analysis of the Ability of <i>Tecomella undulata</i> to Treat Parkinson’s Disease: Unveiling the Potential of Natural Drug Design, Development, and Therapy

Monoamine oxidase (MAO) is an enzyme that plays a crucial role in breaking down monoamine neurotransmitters, including serotonin, dopamine, and norepinephrine, thereby regulating their levels in the brain and other tissues. A decrease in MAO enzymes leads to a nonfatal neurological condition that can lead to Parkinson’s disease. In this study, compounds from Tecomella undulata that mimic the structure of MAO-A can be used as substitutes for the blood–brain barrier, which was confirmed via in silico approaches. To study protein–ligand interactions, the target protein, MAO-A (Protein Data Bank ID: 2Z5Y), was subjected to molecular docking and dynamics studies with high-affinity compounds extracted from T. undulata . Among the 30 phytochemicals that were subjected to molecular docking simulation to examine the behavior of the dynamic protein complex, the compounds with the highest binding affinities were squalene, benzoic acid, 4-methyl-[4-(methoxycarbonyl)phenyl] methyl ester, and stigmasterol. In terms of the root mean square deviation (RMSD), root mean square fluctuation, ligand RMSD, radius of gyration, solvent accessible surface area, and H bond, ligand binding demonstrated sustained stability throughout the simulation period. The results suggest that substances derived from T. undulata show important potential for treating Parkinson’s disease, justifying additional research through both in vitro and in vivo experiments.

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Journal
Journal of Bio-X Research
Published
2025-01-01
DOI
https://doi.org/10.34133/jbioxresearch.0054
Primary Topic
Computational Drug Discovery Methods
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article
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article

In Silico Analysis of the Ability of Tecomella undulata to Treat Parkinson’s Disease: Unveiling the Potential of Natural Drug Design, Development, and Therapy

Anguraj Aswini, Helan Soundra Rani Michael, Ranjithkumar Rajamani, Rathish Kumar Sivaraman et al.
Journal of Bio-X Research
Computational Drug Discovery Methods
article

In Silico Analysis of the Ability of Tecomella undulata to Treat Parkinson’s Disease: Unveiling the Potential of Natural Drug Design, Development, and Therapy

Anguraj Aswini, Helan Soundra Rani Michael, Ranjithkumar Rajamani, Rathish Kumar Sivaraman, Ramesh Kumar Nandana, S. Geetha, Djearamane Sinouvassane, Sumathi Ramamoorthy Niraimathi, Samuel Shini Keren Happuch, Sanilkumar Sruthika
article en

Abstract

Monoamine oxidase (MAO) is an enzyme that plays a crucial role in breaking down monoamine neurotransmitters, including serotonin, dopamine, and norepinephrine, thereby regulating their levels in the brain and other tissues. A decrease in MAO enzymes leads to a nonfatal neurological condition that can lead to Parkinson’s disease. In this study, compounds from Tecomella undulata that mimic the structure of MAO-A can be used as substitutes for the blood–brain barrier, which was confirmed via in silico approaches. To study protein–ligand interactions, the target protein, MAO-A (Protein Data Bank ID: 2Z5Y), was subjected to molecular docking and dynamics studies with high-affinity compounds extracted from T. undulata . Among the 30 phytochemicals that were subjected to molecular docking simulation to examine the behavior of the dynamic protein complex, the compounds with the highest binding affinities were squalene, benzoic acid, 4-methyl-[4-(methoxycarbonyl)phenyl] methyl ester, and stigmasterol. In terms of the root mean square deviation (RMSD), root mean square fluctuation, ligand RMSD, radius of gyration, solvent accessible surface area, and H bond, ligand binding demonstrated sustained stability throughout the simulation period. The results suggest that substances derived from T. undulata show important potential for treating Parkinson’s disease, justifying additional research through both in vitro and in vivo experiments.

Journal of Bio-X ResearchVol. 8
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Good health and well-being
Openalex Percentile: Top 79%
Computational Drug Discovery Methods
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