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: 9384193
• 负责人: GARRISON W COTTRELL
• 金额: $ 55.15万
• 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/9384193
• 关键词: Algae; Algorithms; Architecture; Back; Bacteria; Biochemical Pathway; Biological; Biological Neural Networks; Chemicals; Classification; Communities; Complex; Cyanobacterium; Data; Data Set; Development; FDA approved; Family; Gene Cluster; Genomics; Goals; Grant; Informatics; Learning; Light; Marines; 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; Research Infrastructure; Source; Speed; Stream; Structure; Techniques; Time; analog; analytical tool; base; cost; 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.

映射海洋蓝细菌的继发代谢组 细菌是结构上独特和生物活性天然产品的多产来源， 来自各种迷人的生化途径。但是，完整的结构阐明了 天然产品通常是天然产品发现中最耗时，最昂贵的努力 程序。更复杂的是，基因组测序的进步已加速了鉴定 独特的模块化生物合成基因簇在原核生物中，并揭示了许多新化合物 分离，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MS2中各自的MS碎片特征 分子网络。建立这种连接的能力也将加速新的复合发现 随着基因簇和生物合成途径之间建立关联，并有助于快速准确的结构 阐明。结合了这种信息学方法，该拟议的延续项目探索了创新 通过增强的MS和NMR实验来解决复杂分子结构的方法以及 通过加速其分析的新算法的开发。因此，这个总体目标 赠款是开发有助于自动结构分类的有效方法 发现并最终有效地阐明天然产品（或任何小分子）并建造 与社区数据输入相互作用的基础架构。我们将通过以下四个实现这一目标 具体目的：目标1。将MS2分子网络与基因簇网络的整合到快速和 有效地定位具有独特分子体系结构的天然产品；目标2。开发高高的套件 自然产物结构阐明的灵敏度脉冲序列；目标3。开发基于NMR的分子 使用深层卷积神经网络（DCNN）的网络策略来促进分类和 有机化合物的结构阐明；目标4。整合NMR分子网络和基于MS2的网络 分子网络作为有效的结构表征和阐明策略。通过实现这些 目的我们将开发一个创新的工作流程，以查找新化合物并确定其结构， 既快速准确。基因簇和分子之间的联系将揭示 立体化学以及潜在的卤素化和甲基化。然后可以使用此信息 结合更有效的NMR和MS方法，以准确确定结构。这些工具将是 广泛共享的，例如通过全球天然产品社会（GNP）分子网络，以增强 天然产物和有机化学界的总体容量来解决复合物分子 结构。