Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics.
Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics.
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DOI：
10.1016/j.ejmech.2012.03.008
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发表时间：
2012-06
影响因子：
6.7
通讯作者：
Kellogg GE
中科院分区：
文献类型：
--
作者：
Sarkar A;Anderson KC;Kellogg GE
AcrA-AcrB-TolC外排泵从细菌细胞中挤出多种药物，是抗菌素耐药性的主要原因。因此，它们对于那些从事抗生素发现的人来说是最重要的。准确预测抗生素外排一直难以捉摸，尽管有几项研究旨在此目的。从文献中收集32种β-内酰胺类抗生素的最低抑菌浓度（MIC）比值。3-β-内酰胺抗生素结构的维度定量构效关系显示了看似预测模型（q2 = 0.53），但缺乏一般叠加规则，不允许其用于缺乏β-内酰胺部分的抗生素。由于MIC比率必须取决于抗生素与脂质膜和转运蛋白在抗生素从细菌细胞的流入、捕获和挤出期间的相互作用，计算代表这些因素的描述符并用于构建数学模型，该数学模型将抗生素定量分类为具有高/低流出（>93%准确度）。我们的模型提供了初步证据，如果考虑到抗生素进出细菌细胞的通道，就有可能预测抗生素外排的影响-这是描述符和基于场的QSAR模型无法做到的。虽然公共领域的数据缺乏仍然是这些研究的限制因素，但这些模型在预测方面比简单的基于LogP的回归模型有显着改进，应该为这一领域的进一步工作铺平道路。这种方法也应该可以扩展到其他药理学和生物学相关的转运蛋白。
AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q2 = 0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account – something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path towards further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins.
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