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: 10017215
• 负责人: Sam R Nugen
• 金额: $ 63.85万
• 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/10017215
• 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; Phenotype; Polyethylene Glycols; Prevention; Process; Property; Public Health; Race; 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; arm; 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.

摘要 噬菌体是细菌的自然进化的捕食者，它可能是对抗细菌的关键 病原体，包括迫在眉睫的多重耐药细菌的威胁。噬菌体是一种病毒，虽然无害 已经被成功地设计成分离、浓缩和检测其细菌的工具 主持人。此外，噬菌体已被用作治疗感染病原体的患者的药物。 对已知的抗生素具有抗药性。虽然噬菌体的潜在好处很多，但必须有一定的限制 解决这些问题，以便充分利用他们。这一提议的中心假设是，自上而下和 自下而上的方法可以用来设计和合成新的噬菌体，通过组合合成的 生物学和机器学习。这将导致基于噬菌体的工具具有更多的功能和可定制 主机范围。提出这项研究的理由是，由于细菌感染的威胁包括 随着多重耐药性的持续增长，噬菌体已经进化成能够有效识别和杀死 细菌，将成为不可或缺的工具。因此，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。该提案包含三个具体内容 有初步数据和引用文献支持的目的。目标1：定位结合治疗晚期 基于噬菌体的生物传感器和疗法。在这一目标下，噬菌体将被含有炔烃的修饰 非天然氨基酸允许其直接结合到1)叠氮修饰的磁性纳米颗粒，以及2) 叠氮封端聚乙二醇酯。这些修饰将允许开发用于 细菌分离和检测，以及噬菌体，由于它们能够避免 患者的先天免疫反应。目标2：利用机器对噬菌体生物识别元件进行解码 学习。在这个目标中，机器学习将被用于模拟噬菌体和它们的细菌宿主的结合。这个 该模型将能够预测宿主相互作用，并允许设计和合成新的噬菌体尾巴 可以针对特定细菌分离物的纤维。目标3：为更广泛的宿主改变噬菌体生物识别的目的 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所取代。 细菌内毒素的区域，导致噬菌体具有更广泛的宿主范围。这种方法是创新的。 因为它使用自上而下的特征来进行自下而上的设计和合成新型噬菌体。传统型 噬菌体筛选方法将被快速合成噬菌体取代，后者针对特定的 细菌分离物。在成功完成特定目标之后，预期的结果就是设计 和噬菌体的合成，这些噬菌体可用于靶向肠杆菌科内选定的一组细菌 先进的生物传感和治疗技术。公开可用的计算机模型将允许快速设计定制 噬菌体生物识别元件，可添加到功能化噬菌体中。这些技术将允许 研究人员试图揭开噬菌体和细菌之间共同进化的军备竞赛的天平。