Virtual screening: identification of compounds with possible quorum sensing agonistic activity in Pseudomonas aeruginosa
AbstractBackground: Quorum sensing (QS) is a cell density dependent mechanism that allows bacteria to regulate the expression of specific genes in response to changes in their population density, thus controlling their activities in order to produce a response as a unit multicellular. These responses include production of virulence factors, formation of biofilm, bioluminescence, sporulation, among other behavior.Objectives: The objective of this work was to obtain pharmacophore models able to filter and identify molecules with possible agonist activity of quorum sensing and to find possible candidates based on calculations of molecular docking Methods: The structure of the receptor was taken from the Protein Data Bank (PDB). The program AutoDock 4.2 was used to perform docking calculations. The 3D structure of the ligand TP1 was extracted from the complex co-crystallized identified with the code PDB 3IX3. The geometries of ligands were optimized using the PM3 semiempirical method. Results: Two pharmacophoric models were designed, the first one was made using the most active compound (TP-1), highlighting the most important chemical characteristics for molecular recognition. The second model was based on the alignment of three of the most active ligands (TP-1, TP-3 and TP-4). These models were used as a filter in a screening on a database of conformations of several compounds with possible agonist activity of the main circuit of QS present in Pseudomona aeruginosa.The pharmacophoric model based on alignment of the most active compounds showed greater capacity to select or identify compounds exhibiting significant structural and chemical characteristics to be considered possible hit. With this model, 22 compounds were identified.These compounds were subjected to a series of calculations of docking. The outcomes of the docking were used to identify interactions making a SAR analysis and were used as support to understand how chemically distinct compounds can be accommodated by a highly selective receptor, and provide the framework for the development of novel quorum-sensing regulators, utilizing the 2-(benzamido)methyl)phenyl benzoate scaffold and to assess the possibility of synthetic routes, considering the structural similarity presenting between these compounds, generating in this way an alternative to find new compounds with modulating activity QS. These two strategies were used to select a list of potential modulators of quorum sensing or new pharmacophoric candidates. Conclusions: The two pharmacophoric models designed in this study, the number 2 (model based on the alignment of the most active compounds) showed greater ability to select or identify compounds that had important structural and chemical characteristics to be considered possible hits. With this model, 22 compounds were identified, which were subsequently subjected to docking calculations. In general, the docking protocol used is adequate, since in validating the conformation of the co-crystallized ligand.
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