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: 9921415
• 负责人: GARRISON W COTTRELL
• 金额: $ 51.44万
• 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 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9921415
• 关键词: Algae; Algorithms; Architecture; Back; Bacteria; Biochemical Pathway; Biological; Chemicals; Classification; Communities; Complex; Consumption; Cyanobacterium; Data; Data Set; Development; FDA approved; Family; Gene Cluster; Genomics; Goals; Grant; Informatics; Infrastructure; Light; Mass Spectrum Analysis; Methods; Methylation; Molecular; Molecular Structure; Natural Product Drug; Natural Products; Organic Chemistry; Pathway interactions; Pharmaceutical Preparations; Physiologic pulse; Progress Reports; Prokaryotic Cells; Source; Speed; Stream; Structure; Techniques; Time; analog; analytical tool; base; convolutional neural network; cost; deep learning; drug discovery; experimental study; fascinate; genome sequencing; halogenation; innovation; metabolome; novel; programs; prototype; scaffold; small molecule; social; stereochemistry; tool

Mapping the Secondary Metabolomes of Marine Cyanobacteria 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.

海洋蓝细菌次级代谢物组图谱的构建 细菌是结构独特和具有生物活性的天然产物的非常丰富的来源， 都是由多种奇妙的生化途径衍生而来的。然而，完整的结构解析 天然产物通常是天然产物药物发现中最耗时和最昂贵的奋进 程序.更糟糕的是，基因组测序的进步加速了对 在原核生物中发现了独特的模块化生物合成基因簇，并揭示了大量尚未被发现的新化合物。 分离并进行生物学和化学表征。因此，迫切且持续地需要 该字段用于将生物合成基因簇与MS 2中它们各自的MS片段化特征相连接 分子网络建立这种联系的能力也将加速新化合物的发现 as在基因簇和生物合成途径之间建立联系，并有助于快速准确的结构 说明。结合这种信息学方法，这个拟议的延续项目探索了创新的 通过增强的MS和NMR实验解决复杂分子结构的方法，以及 开发新的算法来加速它们的分析。因此，这一总体目标 格兰特是开发有效的方法，促进自动结构分类，结构特征， 天然产物（或任何小分子）的发现和最终有效的结构解析，并建立 与来自社区的数据输入进行交互的基础设施。我们将通过以下四个方面来实现这一目标 具体目标：目标1。MS 2分子网络与基因簇网络的整合， 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 天然产物结构解析的灵敏度脉冲序列;目的3.开发基于核磁共振的分子 使用深度卷积神经网络（DCNN）的网络策略，以促进分类和 有机化合物的结构解析;目的4.为了整合NMR分子网络和基于MS 2的 分子网络作为一种有效的结构表征和阐明策略。通过实现这些 我们将开发一种创新的工作流程来寻找新的化合物并确定其结构， 既快速又准确。基因簇和分子之间的联系将揭示 立体化学和潜在的卤化和甲基化。这些信息可以用于 结合更有效的NMR和MS方法，以准确确定结构。这些工具将是 广泛分享，如通过全球天然产品社会（GNPS）分子网络，以加强 天然产物和有机化学界解决复杂分子的总体能力 结构.