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Abstract
Peroxisome proliferator-activated receptors (PPARs) are considered important targets for the treatment of Type 2 diabetes (T2DM). To accelerate the discovery of PPAR α/γ dual agonists, the comparative molecular field analysis (CoMFA) were performed for PPARα and PPARγ, respectively. Based on the molecular alignment, highly predictive CoMFA model for PPARα was obtained with a cross-validated q2 value of 0.741 and a conventional r2 of 0.975 in the non-cross-validated partial least-squares (PLS) analysis, while the CoMFA model for PPARγ with a better predictive ability was shown with q2 and r2 values of 0.557 and 0.996, respectively. Contour maps derived from the 3D-QSAR models provided information on main factors towards the activity. Then, we carried out structural optimization and designed several new compounds to improve the predicted biological activity. To investigate the binding modes of the predicted compounds in the active site of PPARα/γ, a molecular docking simulation was carried out. Molecular dynamic (MD) simulations indicated that the predicted ligands were stable in the active site of PPARα/γ. Therefore, combination of the CoMFA and structure-based drug design results could be used for further structural alteration and synthesis and development of novel and potent dual agonists.
| Abbreviations | ||
| DM | = | diabetes mellitus |
| T2DM | = | type 2 diabetes |
| PPARs | = | peroxisome proliferator-activated receptors |
| LBDD | = | ligand based drug design |
| 3D-QSAR | = | three-dimensional quantitative structure activity relationship |
| CoMFA | = | comparative molecular field analysis |
| PLS | = | partial least square |
| LOO | = | leave-one-out |
| q2 | = | cross-validated correlation coefficient |
| ONC | = | optimal number of principal components |
| r2 | = | non-cross-validated correlation coefficient |
| SEE | = | standard error of estimate |
| F | = | the Fischer ratio |
| r2pred | = | predictive correlation coefficient |
| DBD | = | DNA binding domain |
| MD | = | molecular dynamics |
| RMSD | = | root-mean-square deviation |
| RMSF | = | root mean square fluctuations |
Communicated by Ramaswamy H. Sarma
Graphical Abstract
In this study, we explored the SARs of zwitterionic derivatives dually targeting PPARα/γ and designed novel PPARα/γ dual agonists, using 3D-QSAR studies. Molecular docking and molecular dynamics simulation served as validation and complement to the SAR results derived from the 3D-QSAR model.
Disclosure statement
No potential conflict of interest was reported by the authors.
