How do cyclic antibiotics with activity against Gram-negative bacteria permeate membranes? A machine learning informed experimental study.
How do cyclic antibiotics with activity against Gram-negative bacteria permeate membranes? A machine learning informed experimental study.
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DOI：
10.1016/j.bbamem.2020.183302
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发表时间：
2020-08-01
期刊：
影响因子：
--
通讯作者：
Wong GCL
中科院分区：
文献类型：
--
作者：
Lee MW;de Anda J;Kroll C;Bieniossek C;Bradley K;Amrein KE;Wong GCL
所有抗生素都必须以某种方式与细菌的两亲性屏障（如富含脂多糖的外膜或基于磷脂的内膜）接触，要么直接破坏它们，要么渗透它们，从而允许抗生素进入细菌。有越来越多的一类循环抗生素，其中许多是细菌来源，表现出对革兰氏阴性细菌的活性，这构成了人类健康的一个紧迫问题。我们研究了这些循环抗生素的不同集合，包括天然的和合成的，包括结核菌素、多粘菌素B、八肽素、卷曲霉素和Kirshenbaum类肽，以确定它们与细菌脂质膜相互作用时的共同点。我们发现它们几乎都有能力在细菌膜中诱导负高斯曲率（NGC），这种曲率在几何上是渗透机制（如孔隙形成、起泡和出芽）所必需的。这是很有趣的，因为膜的渗透通常归因于来自先天免疫的抗菌肽（AMPs）的功能。作为循环抗生素的原型测试案例，我们使用我们最近开发的α-螺旋AMP序列训练的机器学习分类器分析了杆菌素、多粘菌素B和卷曲霉素的氨基酸序列。虽然最初的分类器没有对循环抗生素进行训练，但改进的分类器方法正确地预测了巴氏杆菌素和多粘菌素B具有诱导膜内NGC的能力，而卷曲霉素则没有。此外，分类器能够从多粘菌素B的丙氨酸扫描中总结出经验结构-活性关系。这些结果表明，杂交环状抗菌药物和线性抗菌药物的序列设计存在一些共同点。
All antibiotics have to engage bacterial amphiphilic barriers such as the lipopolysaccharide-rich outer membrane or the phospholipid-based inner membrane in some manner, either by disrupting them outright and/or permeating them and thereby allow the antibiotic to get into bacteria. There is a growing class of cyclic antibiotics, many of which are of bacterial origin, that exhibit activity against Gram-negative bacteria, which constitute an urgent problem in human health. We examine a diverse collection of these cyclic antibiotics, both natural and synthetic, which include bactenecin, polymyxin B, octapeptin, capreomycin, and Kirshenbaum peptoids, in order to identify what they have in common when they interact with bacterial lipid membranes. We find that they virtually all have the ability to induce negative Gaussian curvature (NGC) in bacterial membranes, the type of curvature geometrically required for permeation mechanisms such as pore formation, blebbing, and budding. This is interesting since permeation of membranes is a function usually ascribed to antimicrobial peptides (AMPs) from innate immunity. As prototypical test cases of cyclic antibiotics, we analyzed amino acid sequences of bactenecin, polymyxin B, and capreomycin using our recently developed machine-learning classifier trained on α-helical AMP sequences. Although the original classifier was not trained on cyclic antibiotics, a modified classifier approach correctly predicted that bactenecin and polymyxin B have the ability to induce NGC in membranes, while capreomycin does not. Moreover, the classifier was able to recapitulate empirical structure–activity relationships from alanine scans in polymyxin B surprisingly well. These results suggest that there exists some common ground in the sequence design of hybrid cyclic antibiotics and linear AMPs.
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