Molecular dynamics simulation for screening phytochemicals as α-amylase inhibitors from medicinal plants

Abstract

Diabetes mellitus (DM) is a complicated metabolic disorder with several enzymes, including α-amylase and α-glycosidase. The α-amylase is responsible for postprandial glucose levels; therefore, inhibiting its activity is helpful in diabetes management. Hence, to find natural inhibitors of α-amylase, we have prepared a 257 phytochemical library from selected medicinal plants with antidiabetic activity and conducted a virtual screening and molecular dynamics study. Seventy-nine phytochemicals were screened out of 257 phytochemicals based on binding energy, ranged from −10.1 kcal mol⁻¹ to −7.6 kcal mol⁻¹. The binding energies of screened compounds were lower or equal to the reference molecule (−7.6 kcal mol⁻¹). The binding affinity of six screened phytochemicals was re-scored by X-SCORE. These phytochemicals were subjected to ADMET and Drug-likeness analysis. After screening docking and drug-likeness analysis, six phytochemicals viz., Shahidine, Epicatechin, Quercetin, Isocolumbin, Ellagic acid, Luteolin and a reference molecule (Acarbose) were subjected to Molecular dynamics (MD) simulation to analyze the stability of the docked protein-ligand complex. The values of root mean square deviation, RMSF, RG, SASA, H-Bond, the interaction energy of all protein-ligand complexes were calculated after 30 ns of MD simulation. The results of screened complexes revealed good stability as compared to reference Acarbose. Pharmacophore features of the screened phytochemicals and α-amylase inhibitors showed many common pharmacophore features. Based on finding the screened phytochemicals, e.g. Shahidine, Epicatechin, Quercetin, Isocolumbin, Ellagic acid, and Luteolin, may be used as a potential inhibitors against α-amylase. These phytochemicals could be optimized and synthesized to develop potential drugs to manage and treat diabetes, targeting α-amylase.

Communicated by Ramaswamy H. Sarma.

Keywords:

• Type 2 diabetes
• α-amylase inhibitor
• phytochemicals
• medicinal plant
• molecular dynamics simulations

Acknowledgements

The authors acknowledge the Department of Botany, Kumaun University, SSJ Campus, Almora for providing basic facilities to conduct this research work. The authors also acknowledge Kumaun University, Nainital, for providing high-speed internet facilities. 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, SSJ Campus, Almora.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

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

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. Tanuja Joshi contributed to the construction and analysis of Ligplot. 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.