Synthetic environmental peptide libraries as a source of novel antibiotics

项目总结： 从历史上看，细菌天然产物一直是新型抗生素的一个非常高产的来源。然而，现在是 显然，传统的、基于文化的、自然的产品发现方法的缺陷限制了我们的访问 到自然界中细菌生物合成多样性的一小部分。这些缺点是由以下事实造成的 我们只能培养出大多数环境样本中存在的一小部分(1%)细菌 而且，大多数生物合成的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。这项提议的目标是将 现有的下一代测序数据和具有生物信息学指导的新的元基因组克隆方法， 高通量化学合成开发一条丰富的新管道来识别新的抗生素，灵感来自 大量天然产物生物合成基因簇仍然无法通过 传统的、基于培养的发现方法。细菌基因组DNA的高通量测序 表明非核糖体肽的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。基于培养的非核糖体肽的鉴定 研究也被证明是抗生素的一个非常有效的来源。因此，获得访问更大池的权限 非核糖体多肽合成酶编码的多肽应该是鉴定新的 抗生素。非核糖体多肽的生物合成是独一无二的，因为我们对它的理解足够充分，生物信息学 算法已经发展到可以预测非核糖体肽的结构的地步 仅从初级序列数据。在过去的二十年里，一系列日益健壮的模型被 用于预测含有非核糖体的氨基酸的特性、顺序和修饰 仅基于其编码巨合酶基因的初级序列。同时，固相 合成结构不同的多肽已经变得快速和经济。在这里，我提议加入 固相肽的非核糖体肽结构预测工具和元基因组测序方法 合成提供了一种简单、高通量的策略，用于快速生成大量新颖的、 从基因组(Aim 1)和元基因组(Aim 2)衍生的基因中进化选择的抗菌肽 对数据进行群集。在目标3中，我提出了一种互补的异源表达策略，以探索大多数 我们从元基因组文库中恢复的复杂非核糖体肽生物合成基因簇 在AIM 2中构建。在所有三个AIMS中产生的分子将被筛选以对抗ESKAPE病原体 抗菌活性和最有力的打击将继续作用机制以及PK/Pd/毒性 学习。然后，最有希望的抗生素将在适当的动物模型中进行测试。

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.