Tools for rapid and accurate structure elucidation of natural products

海洋蓝藻次生代谢体的定位 细菌是结构独特和具有生物活性的天然产物的异常丰富的来源， 来源于各种迷人的生化途径。然而，对其完整的结构解释 天然产物往往是天然产物药物开发中最耗时、最昂贵的工作 程序。更糟糕的是，基因组测序的进步加速了对 原核生物中独特的模块化生物合成基因簇，并揭示了大量尚未发现的新化合物 孤立的，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连接到它们在MS2中各自的MS片段特征 分子网络。建立这种联系的能力也将加速新的化合物发现 AS在基因簇和生物合成途径之间建立联系，有助于快速准确的结构 澄清。与这种信息学方法相结合，这个拟议的延续项目探索了创新的 通过增强的MS和核磁共振实验解决复杂分子结构的方法以及 开发新的算法来加速他们的分析。因此，这一计划的首要目标是 GRANT是开发高效的方法，促进结构自动分类，结构特征 发现并最终有效地阐明天然产物(或任何小分子)的结构，并建立 与来自社区的数据输入交互的基础设施。我们将通过以下四个方面实现这一目标 具体目标：目标1.将MS2分子网络与基因簇网络相结合以快速和 高效定位具有独特分子结构的天然产物；目标2.开发一套高性能的 用于天然产物结构鉴定的灵敏脉冲序列；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)的网络策略以便于分类和 有机化合物的结构解析；目的4.将核磁共振分子网络与MS2相结合 分子网络作为一种有效的结构表征和解释策略。通过实现这些 目标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它们的结构， 既快速又准确。基因簇和分子之间的联系将有助于揭示 立体化学和潜在的卤化和甲基化。然后，该信息可用于 结合更有效的核磁共振和质谱学方法准确确定结构。这些工具将是 广泛共享，例如通过全球天然产品社会分子网络，以加强 天然产物和有机化学界解决复杂分子问题的总体能力 结构。

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.