Physical Biology and Deep Learning for Antibiotic Resistance and Discovery

抗生素耐药性和发现的物理生物学和深度学习

• 批准号: 10773228
• 负责人: Felix Wong
• 金额: $ 10.92万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10773228
• 关键词: Address; Antibiotic Resistance; Antibiotics; Antifungal Agents; Award; Bacterial Antibiotic Resistance; Bacterial Infections; Biological; Biological Process; Biology; Biotechnology; Cell Death; Cell Death Induction; Cells; Cellular Structures; Chemical Structure; Chemicals; Classification; Clinical; Clinical Treatment; Communicable Diseases; Computer Models; Cytoplasm; Data; Data Set; Development; Drug Kinetics; Drug Targeting; Dryness; Escherichia coli; Experimental Models; Fluorescence Microscopy; Fluoroquinolones; Genetic; Goals; Health; Health Care Costs; Human; In Vitro; Infection; Knock-out; Laboratories; Lead; Light; Machine Learning; Mass Spectrum Analysis; Measurement; Mechanics; Medical; Mentorship; Methods; Microfluidics; Microscopy; Mission; Modeling; Modification; Morbidity - disease rate; Multi-Drug Resistance; Mus; Optics; Oxazolidinones; Pathway interactions; Pharmaceutical Preparations; Phase; Phenotype; Prevention; Procedures; Property; Public Health; Research; Research Personnel; Source; System; Systems Biology; Testing; Training; United States National Institutes of Health; Work; bactericide; career; cell envelope; chemotherapy; clinically relevant; computational platform; cytotoxicity; deep learning; deep neural network; drug discovery; experimental study; graph neural network; high throughput screening; in silico; in vivo; infectious disease treatment; insight; laboratory experiment; mathematical model; novel; novel antibiotic class; novel therapeutics; overexpression; physical model; physical property; prospective; skills; small molecule libraries

PROJECT SUMMARY/ABSTRACT The dissemination of antibiotic resistance and the drying-up of antibiotic discovery pipelines threaten to increase morbidity from routine medical procedures and worsen the spread of infectious diseases. The main objective of this project is to address the dual challenges of antibiotic resistance and discovery by (1) pinpointing the cellular pathways involved in antibiotic-induced bacterial cell death and (2) leveraging these mechanistic data to discover and develop novel structural classes of antibiotics from chemical libraries of >11 million compounds. The working hypothesis is that antibiotic mechanisms of action (MoAs) manifest through physical changes to cellular structures, and that approaches to antibiotic discovery which integrate this MoA information can more reliably discover novel classes of antibiotics than current discovery pipelines. This hypothesis will be tested in two specific aims: (1) develop novel methods of probing and perturbing antibiotic lethality at the single-cell level (Years 1 to 4) and (2) develop a computational platform for deep learning classes of new antibiotics which exploits the mechanisms of action of known antibiotics (Years 2 to 5). During the first phase of this award, microfluidic, optical, and fluorescence microscopy will be used to record changes to physical properties of cytoplasmic and cell envelope components in Escherichia coli cells treated with various bactericidal antibiotics. Additional targeted experiments based on chemical perturbations, physical perturbations, and genetic overexpression and knockout will inform physical and mathematical models, based on multiscale continuum mechanics, that classify the phenotypes associated with antibiotic-induced cell death. During the second phase of this award, a deep learning platform which predicts both antibiotic leads and their predicted MoAs in silico will be developed. This mechanism-guided approach will be used to identify antibiotic leads and their MoAs from vast chemical spaces, and leads will be experimentally validated in vitro against laboratory strains and multidrug-resistant clinical isolates. Leads will be further investigated using human cell cytotoxicity, hit-to-lead, pharmacokinetic, and in vivo mouse bacterial infection experiments. A better understanding of antibiotic MoAs and the development of novel drug discovery efforts with detailed mechanistic underpinnings fit NIH’s public health mission and have direct implications for the prevention and treatment of infectious diseases. This work will establish a quantitative, model-guided platform for better characterizing and discovering antibiotics, one which promises to offer a fertile source of mechanistic information and chemical diversity. By providing the applicant the opportunity to develop his research career, acquire new wet-lab experimental skills, undertake translational coursework, and receive mentorship from leading experts in antibiotics, machine learning, biotechnology, and infectious diseases at MIT, the support and training provided by this award will enable the applicant’s development as an independent researcher.

项目摘要/摘要 抗生素耐药性的传播和抗生素发现管道的干涸威胁到 增加常规医疗程序的发病率，加剧传染病的传播。主 该项目的目标是通过(1)解决抗生素耐药性和发现的双重挑战 确定抗生素诱导细菌细胞死亡的细胞途径，以及(2)利用这些途径 从&gt；11的化学文库中发现和开发新的抗生素结构类的机械数据 上百万种化合物。工作假说是抗生素作用机制(MOA)通过 细胞结构的物理变化，以及整合这一模式的抗生素发现方法 与目前的发现管道相比，信息可以更可靠地发现新类别的抗生素。这 假设将在两个特定的目标中得到检验：(1)开发探测和干扰抗生素的新方法 单细胞水平的致命性(1至4岁)和(2)开发深度学习的计算平台 利用已知抗生素的作用机制的新抗生素类别(2至5岁)。在.期间 该奖项的第一阶段将使用微流控显微镜、光学显微镜和荧光显微镜来记录变化 对大肠杆菌细胞胞质和细胞膜成分的物理性质的影响 各种杀菌抗生素。基于化学扰动、物理扰动的其他定向实验 扰动、基因过度表达和基因敲除将告知物理和数学模型，基于 在多尺度连续介质力学上，对与抗生素诱导的细胞死亡相关的表型进行分类。 在该奖项的第二阶段，一个深度学习平台预测抗生素先导和他们的 预测了硅基MOAS的发展方向。这种机制导向的方法将被用于鉴定抗生素 来自巨大化学空间的铅及其MOA，以及铅将在体外进行实验验证，以对抗 实验室菌株和耐多药临床分离株。将利用人体细胞对铅进行进一步研究 细胞毒性、对铅的命中率、药代动力学、小鼠体内细菌感染实验。 更好地了解抗生素MOAS和新药发现工作的发展，详细介绍 机械基础符合NIH的公共卫生使命，并对预防和 传染病的治疗。这项工作将建立一个量化的、以模型为导向的平台，以更好地 表征和发现抗生素，一种承诺提供肥沃的机械性来源的抗生素 信息和化学多样性。通过为申请者提供发展其研究事业的机会， 获得新的湿实验室实验技能，承担翻译课程工作，并接受 麻省理工学院抗生素、机器学习、生物技术和传染病方面的领先专家，支持和 该奖项提供的培训将使申请者发展成为一名独立的研究人员。