In silico identification of antidiabetic target for phytochemicals of A. marmelos and mechanistic insights by molecular dynamics simulations

Abstract

The leaves and fruits of Aegle marmelos (L.) have antidiabetic activity. However, the mode of action and molecules having antidiabetic activity are not known. Hence, we conducted molecular docking of phytochemicals with various molecular antidiabetic targets to find the same. Docking prioritized Dipeptidyl peptidase-4 (DPP-4) as the main target for phytochemicals of Aegle marmelos. DPP-4 inactivates intestinal peptides, glucagon-like peptide-1 (GLP-1), and Gastric inhibitory polypeptide (GIP). GLP-1 and GIP stimulate a decline in blood glucose levels, but DPP-4 inhibits their functions resulting high level of glucose. Hence inhibiting the activity of DPP-4 is a well-known strategy to treat Type 2 diabetes. Therefore, to find a mechanism that may be involved to act as a natural inhibitor of DPP-4, we screened five phytochemicals out of seventy-three based on Virtual Screening, ADMET Drug-likeness analysis, and PAINS filtering. Further, all five phytochemicals, i.e. Aegeline, Citral, Marmesinin, Auraptene, β-Bisabolene, and reference compound subjected MDS for analyzing the stability of docked complexes to assess the fluctuation and conformational changes during protein-ligand interaction. The values of RMSD, RG, RMSF, SASA, and Gibbs energy revealed the good stability of these phytochemicals in the active site pocket of DPP-4 in comparison to reference. Additionally, we have done the pharmacophore analysis, which revealed many common pharmacophore features between screened phytochemicals of A. marmelos and reference molecule. Our results show that these phytochemicals are potential antidiabetic candidates and can be further modified and evaluated to develop more effective antidiabetic drugs against DPP-4 to treat Type 2 Diabetes.

Communicated by Ramaswamy H. Sarma

Keywords:

• Aegle marmelos
• Type 2 diabetes
• iGemDock
• DPP-4
• molecular dynamics simulations

Acknowledgements

The authors are thankful to the Head Department of Botany, Kumaun University, Nainital for providing the facility, space, and resources for this work. The authors also acknowledge Kumaun University, Nainital for providing a high-speed internet facility. We also extend our acknowledge to Rashtriya Uchchattar Shiksha Abhiyan (RUSA), Ministry of Human Resource Development, Government of India to provide Computational infrastructure for the establishment of Bioinformatics Centre in Kumaun University, S.S.J. Campus, Almora.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Priyanka Sharma, Sushma Tamta, and Subhash Chandra planned the outline of this research work. Priyanka Sharma conducted docking and simulation and has written this manuscript. Tushar Joshi helps to analyze MD and post-MD simulation. Shalini Mathpal contributed to the construction and analysis of Lig plots. Dr. Subhash Chandra is a Co-supervisor of Priyanka Sharma. He has guided in the methodology troubleshooting of computational techniques. Dr. Sushma Tamta is a supervisor and she has provided her critical analysis in this manuscript.

Additional information

Funding

There was no funding source to carry out this research work.