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Cyanobacteria are gram-positive, cosmopolitan, prokaryotic, photosynthetic organisms which represent a potential source of antibiotic molecules. These molecules are produced in a variety of concentrations, depending upon environmental conditions, have the potential to affect other organisms are now considered as the jewel in the crown by scientists due to their easy cultivation and simple inorganic nutrient requirements.
FtsZ, (Filamentous temperature sensitive- mutant Z), a 40-kDa protein is a highly promising target for new antibacterial drugs because of its central role in bacterial cell division. It is conserved in virtually all eubacteria, archeae and chloroplasts and is considered to be a homologue of tubulin, the eukaryotic cytoskeleton protein and undergoes GTP-dependent polymerization into filaments, which assemble into a highly dynamic polymeric structure known as the Z-ring on the inner membrane of the mid-cell. It defines the plane of cell division.
The objective of this study is to screen cyanobacterial secondary metabolites to target Fts-Z protein of B.subtilis through docking method.
In this study, 3D/ 2D structure of cyanobacterial compounds were retrieved along with their habitat details and then Virtual screening and protein ligand docking was performed using GLIDE module of Maestro (v-9.2).
The virtual screening was performed using Virtual Screening Workflow (VSW) module of the Maestro Suite which includes LigPrep, QikProp and Glide docking at the three precision levels, High Throughput Virtual Screening, Standard Precision, and Extra Precision. HTVS and SP are used for a large set of ligands and XP is more accurate than the above two methods. Sitemap tool was run to get possible sites for grid generation having the previously stated residues Arg 29 and Arg 191. Then a receptor-grid was prepared which included the site having both the above said residues and having maximum site volume. After ensuring that the receptor and ligands were in the correct form for docking, VSW was run. Glide generates conformations internally and passes these through a series of filters. The final energy estimation is done with Glide Score, and a single best pose is generated as the output for a particular ligand. Glide’s docking algorithm approximates a complete systematic search over ligand positions, orientations, and conformations in the receptor active site. Finally virtual screening retains the top 10 % compounds and is ranked based on the glide score.

Tanu Jindal

Appearances