虚拟筛选:铜绿假单胞菌中可能具有群体感应激动活性的化合物的鉴定

R. Vivas-Reyes, Maicol Ahumedo, Margarita Velazquez
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引用次数: 1

摘要

背景:群体感应(Quorum sensing, QS)是一种依赖于细胞密度的机制,它允许细菌调节特定基因的表达,以响应其种群密度的变化,从而控制其活动,从而作为一个单位多细胞产生反应。这些反应包括毒力因子的产生、生物膜的形成、生物发光、产孢等行为。目的:本工作的目的是获得能够过滤和识别可能具有群体感应激动剂活性的分子的药效团模型,并根据分子对接方法的计算找到可能的候选分子:受体的结构取自蛋白质数据库(PDB)。使用AutoDock 4.2程序进行对接计算。从编码为PDB 3IX3的共晶配合物中提取配体TP1的三维结构。采用PM3半经验方法对配体的几何形状进行了优化。结果:设计了2种药效模型,第1种药效模型采用最活性化合物TP-1,突出了分子识别中最重要的化学特征;第二个模型是基于三个最活跃的配体(TP-1, TP-3和TP-4)的排列。这些模型被用作筛选数据库中几种可能具有铜绿假单胞菌QS主回路激动剂活性的化合物的过滤器。基于最活性化合物的排列的药效模型显示出更大的能力来选择或识别具有重要结构和化学特征的化合物,被认为可能被击中。利用该模型,共鉴定出22种化合物。对这些化合物进行了一系列的对接计算。对接的结果用于识别相互作用,进行SAR分析,并作为理解化学上不同的化合物如何被高选择性受体容纳的支持,并为开发新的群体感应调节剂提供框架,利用2-(苯并胺)甲基苯甲酸酯支架,并评估合成路线的可能性,考虑到这些化合物之间呈现的结构相似性。以这种方式产生了一种寻找具有调节活性QS的新化合物的替代方法。这两种策略被用来选择群体感应的潜在调节剂或新的药效候选物。结论。本研究设计的两种药效模型,数字2(基于最活性化合物排列的模型)显示出更强的选择或识别具有重要结构和化学特征的化合物的能力,这些化合物被认为是可能的打击。利用该模型,确定了22种化合物,随后进行对接计算。一般来说,使用的对接协议是足够的,因为在验证共结晶配体的构象
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VIRTUAL SCREENING: IDENTIFICATION OF COMPOUNDS WITH POSSIBLE QUORUM SENSING AGONISTIC ACTIVITY IN Pseudomonas aeruginosa
Background: 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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