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Gene set enrichment analysis, network pharmacology and in silico docking approach to understand the molecular mechanism of traditional medicines for the treatment of diabetes mellitus

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Abstract

Herbal constituents, present in most traditional medicines, are known to act on multiple targets and exert therapeutic activities but often lack scientific evidence at a molecular level. Identification of potential drugs and targets is not only key for validation but also plays an important role in drug discovery. Advances in technologies and computing power, coupled with an enormous increase in available data, have made it possible to decipher the possible molecular mechanism of action of such herbal medicines by focusing on “multi-compounds, multi-targets, and multi-pathways” model through various in silico methods. The current study aims to understand the molecular mechanism of action of 11 common herbs used for the management of diabetes mellitus (DM) and associated complications by utilization of gene set enrichment analysis, network pharmacology, and docking studies. STRING, KEGG pathway, modeller9.10, AutoDock4.0, and DSV2019 were used to perform the study. Compound-gene set enrichment analysis predicted 63 compounds from the 11 plants to modulate 26 protein targets and 11 pathways that are involved in DM. Network analysis identified PI3K-AKT signaling pathway as a key pathway of DM and its associated complications that is modulated by the phytoconstituents. Among the selected phytoconstituents, Apigenin, Quercetin, and Kaempferol showed the highest binding affinity with α-glucosidase (− 6.29 kcal/mol), α-amylase (− 6.95 kcal/mol), and PPAR-γ (− 7.13 kcal/mol), respectively, and interacted with active site residues. The study suggests flavonoids and triterpenes present as major phytoconstituents that regulate the antidiabetic activity and interact with key protein molecules. The study also identified the “PI3K-AKT signaling pathway” as a major pathway involved in the pathogenesis of DM, which is most likely to be strongly modulated by the antidiabetic herbs.

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Acknowledgements

The authors are thankful to the Biomedical Informatics Centre, ICMR-National Institute of Traditional Medicine, Belagavi, India, for supporting this study.

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Authors and Affiliations

  1. ICMR-National Institute of Traditional Medicine, Belagavi, Karnataka, 590010, India

    Vishal S. Patil, Sanjay H. Deshpande, Darasaguppe R. Harish, Rajashri Virge & Sinjini Nandy

  2. Gadag Institute of Medical Science, Gadag, Karnataka, 582103, India

    Anuradha S. Patil

  3. ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, 600077, India

    Subarna Roy

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  1. Vishal S. Patil
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Contributions

VSP, main worker, designed and performed the study, wrote and drafted the manuscript. SD performed network pharmacology, docking study and revision of manuscript. ASP, RV and SN reviewed literature, data analysis, and were responsible for revision of manuscript. HDR and SR supervised the study, provided inputs, reviewed drafts, and finalized the manuscript.

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Correspondence to Subarna Roy.

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Patil, V.S., Deshpande, S.H., Harish, D.R. et al. Gene set enrichment analysis, network pharmacology and in silico docking approach to understand the molecular mechanism of traditional medicines for the treatment of diabetes mellitus. J Proteins Proteom 11, 297–310 (2020). https://doi.org/10.1007/s42485-020-00049-4

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