Virtual screening, molecular dynamics, density functional theory and quantitative structure activity relationship studies to design peroxisome proliferator-activated receptor-γ agonists as anti-diabetic drugs. Issue 2 (22nd January 2021)

• Record Type: Journal Article
• Authors: Ahmed, Sinthyia; Islam, Nazrul; Shahinozzaman, Md; Fakayode, Sayo O.; Afrin, Nadia; Halim, Mohammad A.
• Is Part Of: Journal of biomolecular structure & dynamics. Volume 39:Issue 2(2021)
• Page Start: 728
• Page End: 742
• Publication Date: 2021-01-22
• Subjects: Virtual screening -- Molecular dynamics -- Type 2 diabetes -- PPAR-γ agonists -- Insulin sensitizer -- Quantitative-structural-activity-relationship -- Partial-Least-Square Regression -- Principal-Component-Analysis; Biomolecules -- Periodicals; Molecular structure -- Periodicals; Molecular Biology -- Periodicals; Biomechanics -- Periodicals; 572
• DOI: 10.1080/07391102.2020.1714482
• Languages: English
• ISSNs: 0739-1102

Abstract

Abstract: Type 2 diabetes (T2D) is generally characterized by elevated blood glucose levels, insulin resistance, and relative lack of insulin; however, insulin resistance is the predominant risk factor. Hence, the use of insulin sensitizer drugs to increase insulin sensitivity has gained immense interest as an attractive treatment option for T2D and their major target is a nuclear receptor PPAR-γ (peroxisome proliferator-activated receptor-γ). A wide range of synthetic insulin sensitizers such as thiazolidinedione act as PPAR-γ agonists thereby enhancing insulin action and improving hyperglycemia in patients. Nonetheless, they pose severe adverse effects for human, necessitating an emergent need to develop effective insulin sensitizer drugs. Herein, virtual screening of 10, 000 ligands is performed and the best five ligands are identified. MET364, ILE341, CYS285, ALA292, PHE282, and LEU330 residues are found to play an important role in ligand binding. It is shown from the molecular dynamics simulations results of the top-ranked ligands that increased numbers of hydrogen bonds are formed with PPAR-γ catalytic residues. Quantum chemical calculations reveal that all the best ligands can demonstrate good thermodynamic stability and pharmacokinetic properties. Partial-least-square (PLS) regression of quantitative structural activity relationship (QSAR) is utilized to model and predict the binding energy for ligands. Principal component analysis is further explored for the best ligands' QSAR pattern recognition. Importantly, the predicted values of the binding energy of the potential ligands by the PLS regression is favourably compared with the values of binding energy obtained from molecular docking with incredible high accuracy of 98%.