Multitarget Potential of Phytochemicals from Traditional Medicinal Tree, Terminalia arjuna (Roxb. ex DC.) Wight & Arnot as Potential Medicaments for Cardiovascular Disease: An In-Silico Approach

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Abstract

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Terminalia arjuna (Roxb. ex DC.) Wight & Arnot of the Combretaceae family is one of the most frequently approved and utilized medicinal trees in the traditional medicinal system, which was used for the treatment of a variety of diseases, including cardiovascular disorders. The present study aims to identify phytochemicals from T. arjuna, that do not exhibit any toxicity and have significant cardioprotective activity using an in-silico technique. Four different cardiovascular proteins, namely human angiotensin receptor (PDB ID: 4YAY), P38 mitogen-activated protein kinase (MAPK, PDB ID: 4DLI), 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-Co A) reductase (PDB ID: 1HW9), and human C-reactive protein (PDB ID: 1B09), were used as target proteins to identify potential inhibitors using a virtual screening of the phytochemicals in T. arjuna revealed casuarinin as a potential inhibitor of all selected target proteins with strong binding energy. Furthermore, MD simulations for a 100 ns time scale also revealed that most of the key protein contacts of all target proteins were retained throughout the simulation trajectories. Binding free energy calculations using the MM-GBSA approach also support a strong inhibitory effect of casuarinin on target proteins. Casuarinin’s effective binding to these proteins lays the groundwork for the development of broad-spectrum drugs as well as the understanding of the underlying mechanism against cardiovascular diseases through in vivo and clinical studies.

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Kumar, V., Sharma, N., Orfali, R., Patel, C. N., Alnajjar, R., Saini, R., … Perveen, S. (2023). Multitarget Potential of Phytochemicals from Traditional Medicinal Tree, Terminalia arjuna (Roxb. ex DC.) Wight & Arnot as Potential Medicaments for Cardiovascular Disease: An In-Silico Approach. Molecules, 28(3). https://doi.org/10.3390/molecules28031046

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