Tools for rapid and accurate structure elucidation of natural products

快速准确地解析天然产物结构的工具

• 批准号: 10390224
• 负责人: GARRISON W COTTRELL
• 金额: $ 2.04万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-05 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10390224
• 关键词: Algorithms; Architecture; Back; Bacteria; Biochemical Pathway; Biological; Chemicals; Classification; Communities; Complex; Consumption; Cyanobacterium; Data; Data Set; Development; FDA approved; Gene Cluster; Goals; Grant; Informatics; Infrastructure; Light; Methods; Methylation; Molecular; Molecular Structure; Natural Product Drug; Natural Products; Organic Chemistry; Pathway interactions; Pharmaceutical Preparations; Physiologic pulse; Prokaryotic Cells; Source; Speed; Stream; Structure; Time; analytical tool; base; convolutional neural network; cost; drug discovery; experimental study; fascinate; genome sequencing; halogenation; innovation; programs; small molecule; social; stereochemistry; tool

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.

项目摘要 应用程序5 R01 GM107550-08“快速准确的工具 天然产物的结构阐明” 细菌是结构独特和生物活性天然的多产源。 源自各种迷人的生化途径的产品。但是， 天然产品的完整结构通常是最耗时且昂贵的 自然产品药物发现计划的努力。加重这一问题，进步 基因组测序加速了识别独特的模块化生物合成基因 原核生物中的簇，并揭示了许多尚未隔离的新化合物 生物学和化学表征。结果，紧急而持续的需求 该领域将生物合成基因簇连接到其各自的MS碎片特征 MS2分子网络。建立这种连接的能力将加速新 复合发现以及基因簇和生物合成之间的关联 途径，并有助于快速，准确的结构阐明。结合这些信息 方法，该提议的延续项目探讨了解决的创新方法 通过增强的MS和NMR实验以及复杂的分子结构以及 开发新算法以加速其分析。那是总体目标 这项赠款的是开发有助于自动结构分类的有效方法， 结构特征发现并最终阐明天然产物的结构（或 任何小分子）并构建与来自数据输入相互作用的基础架构 社区。我们将以以下四个特定目标来实现这一目标：目标1。集成 MS2分子网络与基因簇网络迅速有效地定位 具有独特分子体系结构的天然产品；目标2。开发一套 自然产物结构阐明的高灵敏度脉冲序列；目标3 使用深卷积神经开发基于NMR的分子网络策略 网络（DCNN），以促进有机的类别和结构阐明 化合物；目标4。整合NMR分子网络和基于MS2的分子 网络作为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，以查找新化合物，并为 快速，准确地确定其结构。基因簇之间的连接 分子将阐明立体化学以及潜在的卤素化和甲基化。 然后可以将此信息与更有效的NMR和MS方法结合使用 准确确定的结构。这些工具将被广泛共享，例如通过全局 天然产品社交（GNP）分子网络，以增强 天然产物和有机化学群落解决复杂的分子结构。