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.

项目概要： 细菌天然产物在历史上一直是新型抗生素的高产来源。但现在 很明显，传统的，基于文化的，天然产品发现方法的缺点限制了我们的访问 自然界中细菌生物合成多样性的一小部分。这些缺点是由于 我们只能培养大多数环境样品中存在的一小部分（<1%）细菌 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。本提案的目标是将联合收割机 现有的下一代测序数据和生物信息学指导的新宏基因组克隆方法， 高通量化学合成，以开发用于鉴定新抗生素的丰富的新管道， 大量的天然产物生物合成基因簇仍然无法研究， 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样化的 细菌基因组中的天然产物生物合成系统。非核糖体肽在基于培养的 研究也证明了它是一种非常有效的抗生素来源。因此，获得更大的池 非核糖体肽合成酶编码的肽应该是一种有效的策略， 抗生素非核糖体肽的生物合成是独特的，因为我们对它有足够的了解， 算法已经发展到可以预测非核糖体肽的结构的程度 仅从原始序列数据。在过去的二十年里，一系列日益强大的模型已经被 开发用于预测组成非核糖体的氨基酸的身份、顺序和修饰， 肽，仅基于其编码巨合成酶基因的一级序列。同时，固相 结构不同的肽的肽合成已经变得快速和经济。在此，我提议加入 非核糖体肽结构预测工具和固相肽宏基因组测序方法 合成以提供简单、高通量的策略，用于快速产生大量新的， 从基因组（Aim 1）和宏基因组（Aim 2）衍生基因进化选择的抗菌肽 聚类数据。在目标3中，我提出了一种互补的异源表达策略，用于探索最重要的 我们从宏基因组文库中回收的复杂的非核糖体肽生物合成基因簇 在目标2中构建。在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作用机制以及PK/PD/毒性 问题研究最有希望的抗生素将在适当的动物模型中进行测试。