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Pharmacophore generation,atom-based 3D-QSAR and molecular dynamics simulation analyses of pyridine-3-carboxamide-6-yl-urea analogues as potential gyrase B inhibitors
Authors:M A Azam  J Thathan
Institution:1. Department of Pharmaceutical Chemistry, JSS College of Pharmacy (A Constituent College of Jagadguru Sri Sivarathreeswara University, Mysuru), Tamil Nadu, Indiaafzal9azam@hotmail.com afzal@jsscpooty.org;3. Department of Pharmaceutical Chemistry, JSS College of Pharmacy (A Constituent College of Jagadguru Sri Sivarathreeswara University, Mysuru), Tamil Nadu, India
Abstract:DNA gyrase subunit B (GyrB) is an attractive drug target for the development of antibacterial agents with therapeutic potential. In the present study, computational studies based on pharmacophore modelling, atom-based QSAR, molecular docking, free binding energy calculation and dynamics simulation were performed on a series of pyridine-3-carboxamide-6-yl-urea derivatives. A pharmacophore model using 49 molecules revealed structural and chemical features necessary for these molecules to inhibit GyrB. The best fitted model AADDR.13 was generated with a coefficient of determination (r²) of 0.918. This model was validated using test set molecules and had a good r² of 0.78. 3D contour maps generated by the 3D atom-based QSAR revealed the key structural features responsible for the GyrB inhibitory activity. Extra precision molecular docking showed hydrogen bond interactions with key amino acid residues of ATP-binding pocket, important for inhibitor binding. Further, binding free energy was calculated by the MM-GBSA rescoring approach to validate the binding affinity. A 10 ns MD simulation of inhibitor #47 showed the stability of the predicted binding conformations. We identified 10 virtual hits by in silico high-throughput screening. A few new molecules were also designed as potent GyrB inhibitors. The information obtained from these methodologies may be helpful to design novel inhibitors of GyrB.
Keywords:GyrB  3D-QSAR  MM-GBSA  Dynamics simulation  in silico HTVS
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