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Abstract
The dipeptidyl peptidase-IV (DPP-IV) family of receptors possesses a large binding cavity that imparts promiscuity for number of ligand binding which is not common to other receptors. This feature increases the challenge of using computational methods to identify DPP-IV inhibitors, therefore using both pharmacophore and structure-based screening seems to be a reliable approach. Mining of novel DPP-IV inhibitors by integrating both of these in silico techniques was reported. Pharmacophore model (Model_008) obtained from structurally diverse reported compounds was used as a template for screening of MolMall database followed by structure-based screening against PDB ID: 5T4E. After absorption, distribution, metabolism and excretion (ADME) analysis of shortlisted compounds, consensus docking and molecular mechanics/generalized born surface area studies were carried out. The results of the docking studies obtained were comparable to that of the reference ligand. Out of nine hits identified, only one hit (ID MolMall-20062) was available which was procured through exchange program. Molecular dynamic simulation studies of the procured hit revealed its good selectivity and stability in DPP-IV binding pocket and interactions observed with important amino acids viz., Trp629, Lys544 and Arg125. Biological testing of the compound MolMall-20062 showed promising DPP-IV inhibition activity with IC50: 6.2 µM. Compound MolMall-20062 could be taken as a good lead for the development of DPP-IV inhibitors.
| Abbreviations | ||
| ADME | = | absorption, distribution, metabolism and excretion |
| ChEBI | = | chemical entities of biological interest |
| DPP-IV | = | dipeptidyl peptidase IV |
| DISCOtech | = | distance comparisons |
| HTVS | = | high throughput virtual screening |
| MD | = | molecular dynamics |
| MM-GBSA | = | molecular mechanics‐generalized born surface area |
| OGTT | = | oral glucose tolerance test |
| PBVS | = | pharmacophore-based virtual screening |
| PDB | = | protein data bank |
| RMSD | = | root mean square deviation |
| ROC | = | receiver operating characteristics |
| SP | = | standard precision |
| SBVS | = | structure-based virtual screening |
| VS | = | virtual screening |
| XP | = | extra precision |
Communicated by Ramaswamy H. Sarma
Acknowledgements
Authors are grateful to the Department of Biotechnology, New Delhi, India for providing BIF facility (via letter no. AS/MP (RES)/JH-5/2013) and Vice Chancellor, Jamia Hamdard for other facilities and support.
Disclosure statement
No potential conflict of interest was reported by the authors.