title: Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors.
abstract: Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes involved in protein biosynthesis in all living organisms and are an unexploited antibacterial targets, as many strains of bacteria have become resistant to all established classes of antibiotics. Therefore, the main aim of this study is to discover new lead molecules which would be useful as anti-bacterial compounds. Pharmacophore models were developed by using CATALYST HypoGen with a training set of 29 diverse methionyl-tRNA synthetase (MetRS) inhibitors. The best quantitative pharmacophore hypothesis (Hypo1) obtained a correlation coefficient of 0.975, root mean square deviation (RMSD) of 0.55 and cost difference (null cost-total cost) of 70.32. This Hypo1 was validated by two methods, first by using 104 test set molecules which resulted a correlation of 0.926 between HypoGen estimated activities versus experimental activities and secondly by Cat-Scramble validation method. This validated pharmacophore model was further used for screening databases for discovery of new MetRS inhibitors. The new lead compounds were further analyzed for drug-like properties. Homology modeled structure of Staphylococcus aureus MetRS was built and molecular docking studies were performed with many inhibitors using the newly built protein structure. Finally, it was found that the new leads exhibited good estimated inhibitory activity, calculated binding properties similar to experimentally proven compounds and also favorable drug-like properties.
authors.affiliation: Department of Biochemistry, Division of Applied Life Science, Environmental Biotechnology National Core Research Center, Gyeongsang National University, Jinju 660-701, Republic of Korea.
authors.forename: Nagakumar
authors.initials: N
authors.lastname: Bharatham
authors.forename: Kavitha
authors.initials: K
authors.lastname: Bharatham
authors.forename: Keun Woo
authors.initials: KW
authors.lastname: Lee
mesh_terms: D000595:Amino Acid Sequence; D000900:Anti-Bacterial Agents / Q000737:chemistry / Q000494:pharmacology; D001419:Bacteria / Q000187:drug effects / Q000201:enzymology / Q000235:genetics; D001665:Binding Sites; D002384:Catalysis / Q000187:drug effects; D003198:Computer Simulation; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology; D008718:Methionine-tRNA Ligase / Q000037:antagonists & inhibitors* / Q000235:genetics / Q000378:metabolism; D008958:Models, Molecular; D008969:Molecular Sequence Data; D015394:Molecular Structure; D011485:Protein Binding; D017433:Protein Structure, Secondary; D016415:Sequence Alignment; D013329:Structure-Activity Relationship
chemical_list: D000900:Anti-Bacterial Agents; D004791:Enzyme Inhibitors; D008718:Methionine-tRNA Ligase