Identification of Potent Virtual Leads Specific to S1’ Loop of ADAMTS4: Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Dynamic Studies

Title:Identification of Potent Virtual Leads Specific to S1’ Loop of ADAMTS4: Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Dynamic Studies

Volume: 19 Issue: 3

Author(s): P. Rathi Suganya, Sukesh Kalva and Lilly M. Saleena

Affiliation:

Keywords: ADAMTS4, pharmacophore, enrichment studies, docking studies, 3D-QSAR, molecular dynamics.

Abstract: ADAMTS4 (Aggrecanase-1) is an important enzyme, which belongs to ADAMTS family. Aggrecanase-1 is involved in aggrecan degradation of articular cartilage in osteoarthritis and rheumatoid arthritis. Overall variability of S1’ domain of ADAMTS4 has been the main selectivity determinant to design the unique inhibitors. 34 inhibitors from Binding database and literature were used to develop the pharmacophore model. The five featured pharmacophore model AHHRR had the best survival score of 3.493 and post-hoc score of 2.545, indicating that the model is highly reliable. The 3D-QSAR acquired had excellent r² value of 0.99 and GH score of 0.839. The validated pharmacophore model was used for insilico screening of Asinex and ZINC database for finding the potential lead compounds. ZINC00987406 and ASN04459656 which pose high glide score i.e >7 Kcal/mol and H-bond and hydrophobic interactions in the S1‘loop residues of ADAMTS4 were subjected to Molecular Dynamics Simulation studies. Molecular dynamic simulation result indicates that the RMSD and RMSF of backbone atoms for the above complexes were within the limit of 2.0 A˚. These compounds can be potential candidates for osteoarthritis by inhibiting ADAMTS4.

Cite this article as:

Suganya Rathi P., Kalva Sukesh and Saleena M. Lilly, Identification of Potent Virtual Leads Specific to S1’ Loop of ADAMTS4: Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Dynamic Studies, Combinatorial Chemistry & High Throughput Screening 2016; 19 (3) . https://dx.doi.org/10.2174/1386207319666160127111318