Predictive Models for Small-Molecule Accumulation in Gram-Negative Bacteria

项目总结 革兰氏阴性菌小分子积累的预测模型。 耐药的革兰氏阴性细菌感染的发病率正在增加，新的抗生素正在 迫切需要与这一日益严重的公共卫生威胁作斗争。小说发展的主要障碍 抗生素是我们对与细菌相关的小分子结构特征的了解不足 穿透和外流。因此，虽然通常可以为新的靶点识别出有效的生化抑制剂， 事实证明，将它们开发成具有全细胞抗菌活性的化合物具有挑战性。 为了解决这一关键问题，我们在此提出了一种综合的、多学科的方法来开发 预测小分子在革兰氏阴性菌中渗透和外排的定量模型。我们有 开创了一个系统、定量评估小分子在细菌中积累的通用平台， 采用无标记LC-MS/MS检测和多元化学信息学分析。我们还开发了 野生型、多孔型、外排基因敲除和双重危害的大肠杆菌的独特等基因菌株组， 铜绿假单胞菌和鲍曼不动杆菌，使我们能够剖析外膜/内膜的个体贡献。 渗透率和主动外流到净积累，使用一个动力学模型，准确地概括了可用的 实验数据。此外，我们还开发了用于定量构效关系的机器学习和神经网络方法 (定量结构-活性关系)药理特性的建模，现在将用于 开发革兰氏阴性菌蓄积、渗透和外排的预测性化学信息学模型。 该项目将由一个多学科的SPAR-GN项目小组(小分子渗透和 耐抗生素革兰氏阴性杆菌的外流，“矛枪”)涉及Derek Tan(MSK，PI)的实验室，Helen Zgurskaya(OU，PI)，Bradley Sherborne(默克，主要合作者)，Valentin Rybenkov(OU，Co-I)，Adam DuerFeldt(OU，Co-I)、Carl Balibar(默克，协作者)和David McLaren(默克，协作者)，包括 在有机和以多样性为导向的合成、生物化学、微生物学、高 吞吐量筛选、质谱学、生物物理建模、化学信息学和药物化学。 在这里，我们将设计和合成具有不同结构和物理化学特征的化学库 特性；分析它们在高通量和高密度的等基因菌株集中的积累 分析格式；从最终的实验数据集中提取渗透和外排的动力学参数； 开发和验证用于累积、渗透和外流的稳健的QSAR模型；并演示其实用性 在药物化学活动中开发针对三种革兰氏阴性抗生素的新型模型 目标。该项目将在抗菌药物发现领域取得重大进展，为抗菌药物开发提供强大的 为科学界提供工具，以应对这一对公共卫生的重大威胁。

PROJECT SUMMARY Predictive Models for Small-Molecule Accumulation in Gram-Negative Bacteria. Antibiotic-resistant Gram-negative bacterial infections are increasing in incidence and novel antibiotics are urgently needed to combat this growing threat to public health. A major roadblock to the development of novel antibiotics is our poor understanding of the structural features of small molecules that correlate with bacterial penetration and efflux. As a result, while potent biochemical inhibitors can often be identified for new targets, developing them into compounds with whole-cell antibacterial activity has proven challenging. To address this critical problem, we propose herein a comprehensive, multidisciplinary approach to develop quantitative models to predict small-molecule penetration and efflux in Gram-negative bacteria. We have pioneered a general platform for systematic, quantitative evaluation of small-molecule accumulation in bacteria, using label-free LC-MS/MS detection and multivariate cheminformatic analysis. We have also developed unique isogenic strain sets of wild-type, hyperporinated, efflux-knockout, and doubly-compromised E. coli, P. aeruginosa, and A. baumannii that allow us to dissect the individual contributions of outer/inner membrane penetration and active efflux to net accumulation, using a kinetic model that accurately recapitulates available experimental data. Moreover, we have developed machine learning and neural network approaches to QSAR (quantitative structure–activity relationship) modeling of pharmacological properties that will now be used to develop predictive cheminformatic models for Gram-negative accumulation, penetration, and efflux. This project will be carried out by a multidisciplinary SPEAR-GN Project Team (Small-molecule Penetration & Efflux in Antibiotic-Resistant Gram-Negatives, “speargun”) involving the labs of Derek Tan (MSK, PI), Helen Zgurskaya (OU, PI), Bradley Sherborne (Merck, Lead Collaborator), Valentin Rybenkov (OU, Co-I), Adam Duerfeldt (OU, Co-I), Carl Balibar (Merck, Collaborator), and David McLaren (Merck, Collaborator), comprising extensive combined expertise in organic and diversity-oriented synthesis, biochemistry, microbiology, high- throughput screening, mass spectrometry, biophysical modeling, cheminformatics, and medicinal chemistry. Herein, we will design and synthesize chemical libraries with diverse structural and physicochemical properties; analyze their accumulation in the isogenic strain sets in both high-throughput and high-density assay formats; extract kinetic parameters for penetration and efflux from the resulting experimental datasets; develop and validate robust QSAR models for accumulation, penetration, and efflux; and demonstrate the utility of these models in medicinal chemistry campaigns to develop novel Gram-negative antibiotics against three targets. This project will provide a major advance in the field of antibacterial drug discovery, providing powerful enabling tools to the scientific community to address this major threat to public health.