Tools for rapid and accurate structure elucidation of natural products

项目摘要 应用5 R01 GM107550-08“快速、准确的工具”摘要 天然产物的结构解析“ 细菌是结构独特和具有生物活性的天然物质的极丰富来源 从各种迷人的生化途径中衍生出来的产品。然而， 天然产物的完整结构鉴定通常是最耗时和最昂贵的 致力于天然产物药物发现计划。更糟糕的是， 基因组测序加速了独特的模块化生物合成基因的鉴定 在原核生物中发现了大量尚未分离的新化合物 具有生物和化学特征的。因此，有一种迫切和持续的需求 这一领域将生物合成的基因簇与它们各自的MS片段特征联系在一起 MS2分子网络。建立这种联系的能力将加速新的 化合物的发现以及在基因簇和生物合成之间建立联系 途径，并有助于快速和准确的结构阐明。结合这一信息学 方法，这个拟议的延续项目探索了解决问题的创新方法 通过增强的MS和核磁共振实验获得了复杂的分子结构，以及 开发新的算法来加速他们的分析。因此，首要目标是 这笔赠款的目的是开发促进自动结构分类的有效方法， 天然产物的结构特征发现和最终有效的结构解析(或 任何小分子)，并建立一个基础设施，与来自 社区。我们将通过以下四个具体目标实现这一目标：目标1.整合 MS2分子网络与基因簇网络快速高效定位 具有独特分子结构的天然产物；目标2。开发一套 用于天然产物结构鉴定的高灵敏度脉冲序列；目标3至 利用深度卷积神经网络开发基于核磁共振的分子网络策略 促进有机化合物分类和结构阐明的网络(DCNN) 化合物；目的4.整合核磁共振分子网络和MS2分子 网络作为一种有效的结构表征和解释策略。通过实现 为了达到这些目标，我们将开发一种创新的工作流程，以寻找新的化合物和 快速准确地确定它们的结构。基因簇之间的联系 分子将揭示立体化学以及潜在的卤化和甲基化。 然后可以将这些信息与更有效的核磁共振和质谱学方法结合使用 准确确定结构。这些工具将被广泛共享，例如通过全球 天然产物社会(GNPS)分子网络，提升整体能力 天然产物和有机化学社区解决复杂的分子结构。

Project Summary Summary from the Application 5 R01 GM107550-08 “Tools for rapid and accurate structure elucidation of natural products” Bacteria are extraordinarily prolific sources of structurally unique and biologically active natural products that derive from a diversity of fascinating biochemical pathways. However, the complete structure elucidation of natural products is often the most time consuming and costly endeavor in natural product drug discovery programs. Compounding this, advancements in genome sequencing have accelerated the identification of unique modular biosynthetic gene clusters in prokaryotes and revealed a wealth of new compounds yet to be isolated and biologically and chemically characterized. Resultantly, there is an urgent and continuing need in this field to connect biosynthetic gene clusters to their respective MS fragmentation signatures in the MS2 molecular networks. The capacity to make such connections will accelerate new compound discovery as well as create associations between gene cluster and biosynthetic pathway, and aid in fast and accurate structure elucidations. Combined with this informatics approach, this proposed continuation project explores innovative methods by which to solve complex molecular structures by enhanced MS and NMR experiments, as well as the development of new algorithms by which to accelerate their analysis. Thus, the overarching goal of this grant is to develop efficient methods that facilitate automated structural classification, structural feature discovery and ultimately efficient structure elucidation of natural products (or any small molecule) and to build an infrastructure that interacts with data input from the community. We will achieve this with the following four specific aims: Aim 1. Integration of MS2 molecular networking with gene cluster networking to rapidly and efficiently locate natural products that have unique molecular architectures; Aim 2. To develop a suite of high sensitivity pulse sequences for natural product structure elucidation; Aim 3. To develop NMR based molecular networking strategies using Deep Convolutional Neural Networks (DCNNs) to facilitate the categorization and structure elucidation of organic compounds; Aim 4. To integrate NMR molecular networking and MS2 -based molecular networking as an efficient structure characterization and elucidation strategy. By achieving these aims we will develop an innovative workflow for finding new compounds and for determining their structures, both quickly and accurately. The connection between gene cluster and molecule will shed light on stereochemistry and potential halogenations and methylations. This information can then be used in combination with more efficient NMR and MS methods to accurately determine structures. These tools will be widely shared, such as through the Global Natural Products Social (GNPS) Molecular Network, to enhance the overall capacity of the natural products and organic chemistry communities to solve complex molecular structures.