Synthetic environmental peptide libraries as a source of novel antibiotics

合成环境肽库作为新型抗生素的来源

• 批准号: 10394993
• 负责人: SEAN F BRADY
• 金额: $ 70.34万
• 依托单位: HACKENSACK UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10394993
• 关键词: Address; Algorithms; Amino Acids; Anabolism; Animal Model; Animal Testing; Animals; Anti-Bacterial Agents; Antibiotics; Bacitracin; Bacteria; Bacterial Genome; Bioinformatics; Clinical; Cloning; Collaborations; Complex; Daptomycin; Data; Data Set; ESKAPE pathogens; Environment; Enzymes; Family; Fermentation; Frequencies; Gene Cluster; Genes; Genome; Genomic DNA; Genomics; Geography; Goals; Grant; High-Throughput Nucleotide Sequencing; Individual; Instruction; Laboratories; Large-Scale Sequencing; Libraries; Metagenomics; Methods; Modeling; Modification; Molecular Target; Multi-Drug Resistance; Natural Products; Nature; Penicillins; Peptide Biosynthesis; Peptide Library; Peptide Synthesis; Peptides; Phase; Polymyxins; Production; Rewards; Sampling; Series; Soil; Solid; Source; Structure; Structure-Activity Relationship; Study models; System; Technology; Testing; Therapeutic; Toxic effect; Translating; alpha synuclein; base; biological systems; chemical synthesis; cost; design; genetic information; in vivo; innovation; metagenome; metagenomic sequencing; next generation sequencing; novel; peptide structure; peptide synthase; pharmacokinetics and pharmacodynamics; prediction algorithm; programs; public database; screening; small molecule; tool

Project summary: Bacterial natural products have historically been a very productive source of novel antibiotics. However, it is now clear that shortcomings in traditional, culture-based, natural product discovery methods have limited our access to only a small fraction of bacterial biosynthetic diversity in nature. These shortcomings are attributed to the fact that we are able to culture only a small fraction (<1%) of the bacteria present in most environmental samples and, furthermore, most biosynthetic gene clusters present in the genomes of this small fraction, comprising the cultured bacteria, remain silent under laboratory fermentation conditions. The goal of this proposal is to combine existing next generation sequencing data and novel metagenome cloning methods with bioinformatics-guided, high-throughput chemical synthesis to develop a rich, new pipeline for identifying new antibiotics, inspired by the large number of natural product biosynthetic gene clusters that have remained inaccessible to study by traditional, cultured-based discovery approaches. High-throughput sequencing of bacterial genomic DNA indicates that nonribosomal peptides biosynthetic gene clusters are likely to be the most common and diverse natural product biosynthetic systems in bacterial genomes. Nonribosomal peptides identified in culture-based studies have also proved to be a very productive source of antibiotics. Therefore, gaining access to a larger pool of nonribosomal peptide synthetase-encoded peptides should be a productive strategy for identifying novel antibiotics. Nonribosomal peptide biosynthesis is unique in that we understand it well enough that bioinformatic algorithms have advanced to the point where it is possible to predict the structure of an nonribosomal peptide from primary sequence data alone. Over the past two decades, a series of increasingly robust models have been developed for predicting the identity, order, and modification of the amino acids comprising a nonribosomal peptide, based solely on the primary sequence of its encoding megasynthetase gene. Concurrently, solid-phase peptide synthesis of structurally diverse peptides has become rapid and economical. Here, I propose to join nonribosomal peptide structure prediction tools and metagenome sequencing methods with solid-phase peptide synthesis to provide a simple, high-throughput strategy for rapidly generating a large number of novel, evolutionarily selected, antibacterial peptides from genomic (Aim 1) and metagenomic (Aim 2) derived gene clusters data. In Aim 3 I propose a complementary heterologous expression strategy for exploring the most complex nonribosomal peptide biosynthetic gene clusters that we recover from metagenomic libraries constructed in Aim 2. Molecules generated in all three aims will be screened against ESKAPE pathogens for antibacterial activity and the most potent hits will proceed to mechanism of action as well as PK/PD/toxicity studies. The most promising antibiotic will then be tested in the appropriate animal model.

项目摘要： 从历史上看，细菌天然产品一直是新型抗生素的非常有效的来源。但是，现在是 很明显，传统的基于文化的自然产品发现方法的缺点限制了我们的访问 在自然界中，只有一小部分细菌生物合成多样性。这些缺点归因于事实 我们只能培养大多数环境样品中存在的细菌的一小部分（<1％） 而且，此外，大多数生物合成基因簇存在于这一小部分的基因组中，包括 培养的细菌，在实验室发酵条件下保持沉默。该提议的目的是结合 现有的下一代测序数据和新颖的元基因组克隆方法具有生物信息学引导， 高通量化学综合，以开发出丰富的新管道来识别新抗生素，灵感来自 大量的天然产物生物合成基因簇仍然无法通过 传统的，基于文化的发现方法。细菌基因组DNA的高通量测序 表明非核糖体肽生物合成基因簇可能是最常见和最多的 细菌基因组中的天然产物生物合成系统。在基于培养的非深色肽 研究也被证明是非常有效的抗生素来源。因此，获得更大的游泳池 非深色肽合成酶代码肽的肽应成为识别新型的富有成效策略 抗生素。非透射体肽生物合成是独一无二的，因为我们足够了解生物学 算法已提高到可以预测非透视肽的结构的程度 仅从主要序列数据。在过去的二十年中，一系列越来越强大的模型已经 开发用于预测包含非核糖体的氨基酸的身份，顺序和修饰 肽，仅基于其编码巨核基因基因的主要序列。同时，固相 结构多样的肽的肽合成已变得快速和经济。在这里，我建议加入 具有固相肽的非透白体肽结构预测工具和元基因组测序方法 合成以提供简单，高通量的策略，以快速生成大量新颖的新颖， 从基因组（AIM 1）和宏基因组（AIM 2）衍生基因中选择的进化选择的抗菌肽 集群数据。在AIM 3中，我提出了一种互补的异源表达策略，以探索最多 复杂的非核糖体肽生物合成基因簇，我们从宏基因组库中恢复 在AIM 2中构建的所有三个目标中产生的分子将针对Eskape病原体进行筛选 抗菌活性和最有效的命中将继续采取行动机理以及PK/PD/毒性 研究。然后，最有希望的抗生素将在适当的动物模型中进行测试。