Physical Biology and Deep Learning for Antibiotic Resistance and Discovery

项目总结/摘要 抗生素耐药性的传播和抗生素发现管道的枯竭威胁着 增加常规医疗程序的发病率，并加剧传染病的传播。主要 该项目的目标是通过以下方式解决抗生素耐药性和发现的双重挑战：（1） 精确定位参与抗生素诱导的细菌细胞死亡的细胞途径，以及（2）利用这些途径 从>11的化学库中发现和开发新的抗生素结构类别的机理数据 百万化合物工作假设是抗生素的作用机制（MoAs）通过以下方式表现出来： 细胞结构的物理变化，以及整合这种MoA的抗生素发现方法 信息可以比目前的发现管道更可靠地发现新型抗生素。这 将在两个具体目标中检验假设：（1）开发探测和干扰抗生素新方法 单细胞水平的杀伤力（1至4年级）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（第2至5年）。期间 该奖项的第一阶段，微流体，光学和荧光显微镜将用于记录变化 处理的大肠杆菌细胞中细胞质和细胞包膜成分的物理性质 各种杀菌抗生素。基于化学扰动、物理扰动、 干扰，基因过表达和敲除将告知物理和数学模型， 在多尺度连续介质力学上，分类与抗生素诱导的细胞死亡相关的表型。 在该奖项的第二阶段，一个深度学习平台，预测抗生素先导化合物及其 将开发预测的计算机模拟MoAs。这种机制指导的方法将用于识别抗生素 铅和他们的MoA从巨大的化学空间，铅将在体外实验验证， 实验室菌株和多重耐药临床分离株。将使用人类细胞进一步研究电极导线 细胞毒性、对铅的命中、药代动力学和体内小鼠细菌感染实验。 更好地了解抗生素MoAs和新药发现工作的发展， 机制基础符合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定量的，模型指导的平台，以更好地 表征和发现抗生素，这有望提供一个肥沃的来源， 信息和化学多样性。通过为申请人提供发展其研究事业的机会， 获得新的湿实验室实验技能，进行翻译课程，并接受导师 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， 该奖项提供的培训将使申请人能够发展成为独立的研究人员。

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.