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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

基本信息

批准号：
10663875
负责人：
Sam R Nugen
金额：
--
依托单位：
CORNELL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-16 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10663875
关键词：
Acceleration 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 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 design human disease innovation next generation novel pathogen pathogenic bacteria rapid detection receptor resistance mechanism screening synthetic biology therapeutically effective tool unnatural amino acids

项目摘要

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.

总结噬菌体是自然进化的细菌捕食者，可能是对抗细菌的关键。病原体，包括多重耐药细菌的威胁。噬菌体是一种病毒，并已成功地被设计为分离，浓缩和检测其细菌的工具 hosts.此外，抗真菌药物已被用作治疗剂来治疗感染病原体的患者对已知抗生素有抗药性虽然物联网的潜在好处很多，但必须考虑到某些限制。为的是充分利用它们。这一建议的核心假设是，自上而下和自下而上的方法可以用来设计和合成新的药物，生物学和机器学习这将导致基于噬菌体的工具具有更强的功能和可定制性主机范围。拟议研究的理由是，由于细菌感染的威胁，包括那些随着多药耐药性的持续增长，已经进化到有效识别和杀死细菌，将成为不可或缺的工具。因此，快速设计和设计新的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的这些目标得到了初步数据和引用文献的支持。目的1：用于晚期乳腺癌的定点缀合基于噬菌体的生物传感器和疗法。在此目标下，将用含炔的非天然氨基酸，允许它们直接缀合至1）叠氮化物修饰的磁性纳米颗粒，和2）叠氮封端的聚乙二醇。这些修改将允许开发磁性磁记录仪，细菌分离和检测，以及噬菌体，由于它们能够避免细菌感染，因此是更有效的治疗方法。患者的先天免疫反应。目的2：利用机器解码噬菌体生物识别元件学习在这个目标中，机器学习将被用来模拟细菌和它们的细菌宿主的结合。的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可以针对特定细菌分离物的纤维。目的3：将噬菌体生物识别重新用于更广泛的宿主范围.在最终的目标下，噬菌体结合蛋白将被那些已知识别保守的细菌LPS的区域，导致噬菌体具有更广泛的宿主范围。这种做法是创新的因为它使用自上而下的表征来进行自下而上的设计和新颖的MEMS的合成。传统噬菌体筛选方法将被快速合成噬菌体所取代，细菌分离物。在成功完成具体目标后，预期的成果是设计以及合成可用于靶向肠杆菌科中选定的细菌组的β-内酰胺酶，先进的生物传感和治疗技术。一个可供选择的计算机模型将允许快速设计定制的噬菌体生物识别元件，其可以加入到功能化的噬菌体中。这些技术将使研究人员试图改变噬菌体和细菌之间的共同进化军备竞赛的规模。