Tools for rapid and accurate structure elucidation of natural products

项目摘要 应用程序5 R 01 GM 107550 -08“快速和准确的工具”总结 天然产物结构解析 细菌是结构独特且具有生物活性的天然产物的非常丰富的来源。 这些产品来自于多种迷人的生物化学途径。但 天然产物的完整结构解析通常是最耗时和昂贵的 天然产物药物发现计划的奋进。除此之外， 基因组测序加速了独特模块生物合成基因的鉴定 集群在原核生物，并揭示了丰富的新化合物尚未分离， 生物学和化学特性。因此，迫切和持续需要 该领域将生物合成基因簇与其各自的MS片段化特征联系起来， MS 2分子网络建立这种联系的能力将加速新的 化合物的发现以及建立基因簇和生物合成之间的关联 途径，并有助于快速和准确的结构解析。结合这些信息学 方法，这个拟议的延续项目探索创新的方法，以解决 复杂的分子结构，通过增强的MS和NMR实验，以及 开发新的算法，以加速其分析。因此，总体目标 这项拨款的目的是开发促进自动结构分类的有效方法， 天然产物（或 任何小分子），并建立一个基础设施， 社区我们将通过以下四个具体目标实现这一目标：目标1。一体化 MS 2分子网络与基因簇网络快速有效地定位 具有独特分子结构的天然产物;目标2.开发一套 高灵敏度脉冲序列用于天然产物结构解析;目的3.到 使用深度卷积神经网络开发基于NMR的分子网络策略 网络（DCNN），以促进分类和结构阐明有机 化合物;目标4.将NMR分子网络和基于MS 2的分子网络相结合， 网络作为一种有效的结构表征和阐明策略。通过实现 这些目标，我们将开发一个创新的工作流程，以寻找新的化合物， 快速准确地确定它们的结构基因簇之间的联系 和分子将揭示立体化学和潜在的卤化和甲基化。 这些信息可以与更有效的NMR和MS方法结合使用， 准确地确定结构。这些工具将得到广泛分享，例如通过全球 天然产物社会（GNPS）分子网络，以提高整体能力， 天然产物和有机化学社区，以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.