General platform for systematic quantitative evaluation of small-molecule permeability in bacteria.
General platform for systematic quantitative evaluation of small-molecule permeability in bacteria.
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DOI：
10.1021/cb5003015
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发表时间：
2014-11-21
影响因子：
4
通讯作者：
Tan, Derek S.
中科院分区：
文献类型：
--
作者：
Davis, Tony D.;Gerry, Christopher J.;Tan, Derek S.
影响小分子细菌膜渗透性的化学特征尚不清楚，因此缺乏预测渗透性的工具，这是发现和开发新型抗生素的主要障碍。众所周知，与非抗感染药物相比，抗菌药具有截然不同的结构和理化性质，如本文主成分分析（PCA）所示。为了了解这些特性如何影响细菌的渗透性，我们开发了一种系统的方法来评估不同化合物对具有不同细胞包膜的细菌的渗透。使用LC-MS/MS定量细胞内化合物积累，然后使用PCA和Pearson两两相关来确定与积累相关的结构和物理化学参数。一项对大肠杆菌、枯草芽孢杆菌和耻垢分枝杆菌中10种磺基腺苷的初步研究发现，不同细菌的化学结构和渗透性之间存在不明显的相关性。研究了外排泵抑制剂共处理的效果。这为使用该平台对不同化学型进行更大规模的前瞻性分析奠定了基础，以确定化学结构和细菌渗透性之间的全局关系，从而使预测工具的开发能够加速抗生素药物的发现。
The chemical features that impact small-molecule permeability across bacterial membranes are poorly understood, and the resulting lack of tools to predict permeability presents a major obstacle to the discovery and development of novel antibiotics. Antibacterials are known to have vastly different structural and physicochemical properties compared to nonantiinfective drugs, as illustrated herein by principal component analysis (PCA). To understand how these properties influence bacterial permeability, we have developed a systematic approach to evaluate the penetration of diverse compounds into bacteria with distinct cellular envelopes. Intracellular compound accumulation is quantitated using LC-MS/MS, then PCA and Pearson pairwise correlations are used to identify structural and physicochemical parameters that correlate with accumulation. An initial study using 10 sulfonyladenosines in Escherichia coli, Bacillus subtilis, and Mycobacterium smegmatis has identified nonobvious correlations between chemical structure and permeability that differ among the various bacteria. Effects of cotreatment with efflux pump inhibitors were also investigated. This sets the stage for use of this platform in larger prospective analyses of diverse chemotypes to identify global relationships between chemical structure and bacterial permeability that would enable the development of predictive tools to accelerate antibiotic drug discovery.
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