Accelerating phage evolution and tools via synthetic biology and machine learning

通过合成生物学和机器学习加速噬菌体进化和工具

• 批准号: 10443537
• 负责人: Sam R Nugen
• 金额: $ 64.32万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10443537
• 关键词: Acinetobacter baumannii; Address; Alkynes; Amino Acid Sequence; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Azides; Bacteria; Bacterial Genome; Bacterial Infections; Bacteriophage T4; Bacteriophages; Binding; Binding Proteins; Biosensing Techniques; Biosensor; CRISPR/Cas technology; Capsid; Chemistry; Clinical; Computer Models; Consumption; Custom; DNA Restriction-Modification Enzymes; Data; Data Set; Detection; Development; Diagnosis; Disease; Drug resistance; Elements; Engineering; Enterobacteriaceae; Environment; Escherichia coli; Evolution; Family; Fiber; Food; Future; Genes; Genome; Goals; Health; Human; Infection; Innate Immune Response; Innate Immune System; Intervention; Life; Literature; Machine Learning; Magnetic nanoparticles; Magnetism; Methods; Mission; Modeling; Modification; Multi-Drug Resistance; Multidrug-resistant Acinetobacter; Multiple Bacterial Drug Resistance; Natural Immunity; Outcome; Patients; Persons; Phenotype; Polyethylene Glycols; Prevention; Process; Property; Public Health; Reporting; Research; Research Personnel; Resistance; Sampling; Site; Specificity; Surface; System; Tail; Technology; Therapeutic; Therapeutic Agents; Time; Training; United States National Institutes of Health; Viral; Virus; arms race; base; design; human disease; innovation; next generation; novel; pathogen; pathogenic bacteria; rapid detection; receptor; resistance mechanism; screening; synthetic biology; therapeutically effective; tool; unnatural amino acids

Summary Phages, which are the naturally evolved predators of bacteria, may hold the key to combating bacterial pathogens, including the looming threat of multidrug resistant bacteria. Phages are viruses which while harmless to humans and have been successfully engineered as tools to separate, concentrate, and detect their bacterial hosts. Additionally, phages have been used as therapeutic agents to treat patients infected with pathogens resistant to known antibiotics. While the potential benefits of phages are numerous, certain limitations must be addressed in order to fully employ them. The central hypothesis of this proposal is that both top-down and bottom-up approaches can be utilized to design and synthesize novel phages, through a combination of synthetic biology and machine learning. This will result in phage-based tools with increased functionality and customizable host ranges. The rationale for the proposed research is that as the threat of bacterial infections including those with multi-drug resistance continues to grow, phages, which have evolved to efficiently recognize and kill bacteria, will become indispensable tools. Therefore, the ability to rapidly design and engineer new phages for biosensing and therapeutics will be a critical advantage to human health. The proposal contains three specific aims which are supported by preliminary data and cited literature. Aim 1: Site-directed conjugation for advanced phage-based biosensors and therapeutics. Under this aim, phages will be modified with alkyne-containing unnatural amino acids allowing their direct conjugation to 1) azide decorated magnetic nanoparticles, and 2) azide terminated polyethylene glycol. The modifications will allow the development of magnetic phages for bacteria separation and detection, and phages that are more effective therapeutics due to their ability to avoid a patient’s innate immune response, respectively. Aim 2: Decoding phage biorecognition elements using machine learning. In this aim, machine learning will be used to model the binding of phages and their bacterial hosts. The model will enable the prediction of host interactions as well as allow the design and synthesis of novel phage tail fibers which can target specific bacterial isolates. Aim 3: Repurposing phage biorecognition for a broader host ranges. Under the final aim, phage-binding proteins will be replaced with those known to recognize conserved regions of the bacterial LPS, resulting in a phage with a much broader host range. This approach is innovative because it uses top-down characterizations for bottom-up design and synthesis of novel phages. Traditional phage screening methods will be replaced with the rapid synthesis of phages, which are optimized for a particular bacterial isolate. Following the successful completion of the specific aims, the expected outcome is the design and synthesis of phages that can be used to target a selected group of bacteria within Enterobacteriaceae for advanced biosensing and therapeutics. A publically available computer model will allow rapid design of custom phage biorecognition elements which can be added to functionalized phages. These technologies will allow researchers to tip the scales of the co-evolutionary arms race between phage and bacteria.

概括 噬菌体是细菌的自然进化的捕食者，可能是打击细菌的关键 病原体，包括耐多药细菌的损失威胁。噬菌体是无害的病毒 对人类，并已成功设计为分离，集中和检测细菌的工具 主持人。此外，噬菌体已被用作治疗感染病原体的患者的治疗剂 对已知抗生素的抗性。虽然噬菌体的潜在好处是很多，但某些局限性必须是 为了充分雇用它们而解决。该提议的核心假设是自上而下和 自下而上的方法可以通过合成的结合来设计和合成新颖的噬菌体 生物学和机器学习。这将导致基于噬菌体的工具具有增加功能和可自定义的工具 主机范围。拟议研究的理由是，作为细菌感染的威胁，包括 随着多药耐药性的持续增长，噬菌体已演变为有效地识别和杀死 细菌将成为必不可少的工具。因此，能够快速设计和设计新噬菌体的能力 生物传感和治疗将是人类健康的关键优势。该提案包含三个特定的 初步数据和引用文献支持的目的。 AIM 1：高级定向共轭 基于噬菌体的生物传感器和治疗。在此目标下，噬菌体将通过含酒精的噬菌体修改 不自然的氨基酸使其直接共轭达到1）叠氮化物装饰的磁性纳米颗粒，而2） 叠氮化物终止聚乙烯乙二醇。修改将使磁噬菌体的发展 细菌的分离和检测，以及由于其避免的能力而更有效治疗的噬菌体 患者的先天免疫反应。目标2：使用机器解码噬菌体生物识别元件 学习。在此目标中，机器学习将用于建模噬菌体及其细菌宿主的结合。这 模型将实现宿主相互作用的预测，并允许设计和合成新颖的噬菌体尾巴 可以靶向特定细菌分离株的纤维。 AIM 3：重新利用噬菌体生物识别的更广泛的宿主 范围。在最终目标下，噬菌体结合蛋白将被已知识别保守的蛋白取代 细菌LPS的区域，导致噬菌体范围更广泛。这种方法是创新的 因为它使用自上而下的角色来自下而上设计和合成新颖的噬菌体。传统的 噬菌体筛选方法将被噬菌体的快速合成代替，该方法已针对特定的 细菌分离株。在成功完成特定目标之后，预期的结果是设计 并合成可用于靶向肠杆菌中选定细菌的噬菌体的合成 晚期生物传感和治疗。公开可用的计算机模型将允许快速设计自定义 可以添加到功能化噬菌体中的噬菌体生物识别元件。这些技术将允许 研究人员将噬菌体和细菌之间的共同进化武器竞赛的尺度倾斜。