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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.

概括噬菌体是细菌自然进化的捕食者，可能是对抗细菌的关键病原体，包括多重耐药细菌迫在眉睫的威胁。噬菌体是病毒，虽然无害人类并已成功设计为分离、浓缩和检测细菌的工具主机。此外，噬菌体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耐药性。虽然噬菌体的潜在好处很多，但必须注意某些限制以便充分利用它们。该提案的中心假设是自上而下和自下而上的方法可用于通过合成的组合来设计和合成新型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更多的功能和可定制的宿主范围。拟议研究的基本原理是，随着细菌感染的威胁，包括那些随着多重耐药性的不断增长，噬菌体已经进化出能够有效识别和杀死细菌的能力。细菌，将成为不可或缺的工具。因此，快速设计和工程新噬菌体的能力生物传感和治疗将成为人类健康的关键优势。该提案包含三项具体内容由初步数据和引用文献支持的目标。目标 1：高级定点共轭基于噬菌体的生物传感器和疗法。在此目标下，噬菌体将被含有炔烃的修饰非天然氨基酸可直接与 1) 叠氮化物修饰的磁性纳米颗粒和 2) 缀合叠氮化物封端的聚乙二醇。这些修饰将允许开发磁性噬菌体细菌分离和检测，以及噬菌体，它们是更有效的治疗方法，因为它们能够避免分别是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。为此，机器学习将用于模拟噬菌体与其细菌宿主的结合。这该模型将能够预测宿主相互作用，并允许设计和合成新型噬菌体尾部可以针对特定细菌分离株的纤维。目标 3：为更广泛的宿主重新利用噬菌体生物识别范围。根据最终目标，噬菌体结合蛋白将被那些已知的识别保守蛋白的蛋白所取代。细菌 LPS 的区域，从而产生具有更广泛宿主范围的噬菌体。这个方法很有创新性因为它使用自上而下的表征来进行自下而上的设计和新型噬菌体的合成。传统的噬菌体筛选方法将被噬菌体快速合成所取代，噬菌体针对特定噬菌体进行了优化细菌分离物。成功完成具体目标后，预期结果就是设计以及噬菌体的合成，该噬菌体可用于靶向肠杆菌科中选定的一组细菌先进的生物传感和治疗。公开可用的计算机模型将允许快速设计定制可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扭转噬菌体和细菌之间共同进化军备竞赛的规模。