Unified Computation Tools for Natural Products Research

用于天然产物研究的统一计算工具

• 批准号: 10393694
• 负责人: GARRISON W COTTRELL
• 金额: $ 54.68万
• 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/10393694
• 关键词: Address; Binding; Biological; Biological Assay; Chemicals; Classification; Communities; Country; Cyanobacterium; Data; Data Analytics; Databases; Development; Ecosystem; Evaluation; FDA approved; Future; Genomics; Goals; Grant; Knowledge; Laboratories; Legal patent; Libraries; Machine Learning; Mainstreaming; Mass Spectrum Analysis; Methods; Molecular; Molecular Profiling; Molecular Structure; Natural Products; Noise; Nuclear Magnetic Resonance; Occupations; Organism; Paper; Pathway Analysis; Pharmaceutical Preparations; Positioning Attribute; Process; Property; Publications; Research; Research Activity; Research Personnel; Resources; Route; Source; Spectrometry; Structure; Students; System; Technology; Time; annotation system; base; computerized tools; deep learning; deep learning model; drug discovery; genome sequencing; genomic data; informatics tool; innovation; marine natural product; metabolomics; quantum; repository; small molecule; social; tool; web site

Summary The overarching goal for this proposed renewal application will be to further advance tools that are in development and to effectively integrate several types of analytical data with biological assay data and genomic information. This will create a powerful set of tools for faster and even more accurate identification of new molecules, dereplication of known ones, and to directly infer biological activities from spectroscopic information. In the current period of support, we have made substantial progress in developing highly useful tools for automatic annotations and identifications of organic molecules, specifically focused on natural products. The Global Natural Products Social (GNPS) Molecular Networking analysis and knowledge dissemination ecosystem has processed almost 160,000 jobs in nearly 160 countries worldwide, has 4-6,000 new job submissions per month and is accessed over 200,000 times a month (majority accessions are for reference library access, inspection of public data and previous jobs that the community shares as hyperlinks in papers), and has become a mainstream tool for the annotation of organic molecules deriving from diverse sources, especially in metabolomics workflows. The public website for Small Molecule Accurate Recognition Technology (SMART), a deep learning model for providing candidate structures based on 1H-13C HSQC NMR data, went live in December 2019 and already has over 3000 jobs in 50 countries. All tools developed in this proposal will become part of this analysis ecosystem. The four laboratories contributing to this proposed research activity have created an open and integrated team that is continuing to creatively innovate new informatic tools to enhance small molecule structure annotations and inference of their chemical and biological properties. We have four specific aims: 1) To complete the development and evaluation of a set of new and innovative tools for natural products analysis, and deploy these as freely available resources for the worldwide community. 2) To refine the structural characterization of molecules through leveraging repository scale mass spectral information along with NMR data and genomic inputs. 3) To create a new SMART-based tool that integrates mass spectrometry and HSQC NMR data as the input for a new deep learning system with the goal of achieving more accurate predictions of structure. 4) To use deep learning to enhance SMART with bioactivity data so as to enable SMART to predict activities of molecules based on spectroscopic features. The data will also augment the GNPS database with biological assay binding data. An additional consequence of these goals will be the further digitization of natural products analytical data so that they can be used in the computational tools planned herein, as well as other tools in the future. Completion of these four specific aims will create new integrated tools for the precise identification of new natural product structures, and enable inference of their structural relatedness to other classes of organic molecules and their biological properties. Thus, these new informatic tools will have the potential to greatly enhance the small molecule drug discovery process.

概括 此拟议更新应用程序的总体目标将是进一步推进正在开发的工具并 有效地将多种类型的分析数据与生物测定数据和基因组信息整合。这将创建一个 一套强大的工具，可以更快、更准确地识别新分子、消除已知分子，以及 从光谱信息直接推断生物活性。在当前的支持期间，我们做了 在开发用于自动注释和识别有机分子的非常有用的工具方面取得了实质性进展， 特别专注于天然产品。全球天然产品社交 (GNPS) 分子网络分析和 知识传播生态系统已在全球近 160 个国家处理了近 160,000 个工作岗位，拥有 4-6,000 个 每月提交新工作，每月访问次数超过 200,000 次（大多数访问是为了参考图书馆 访问、检查社区以论文中的超链接形式共享的公共数据和以前的工作），并已成为 用于注释不同来源的有机分子的主流工具，特别是在代谢组学中 工作流程。小分子精确识别技术（SMART）的公共网站，一种深度学习模型 用于提供基于 1H-13C HSQC NMR 数据的候选结构，于 2019 年 12 月上线，现已超过 50 个国家/地区的 3000 个工作岗位。本提案中开发的所有工具都将成为该分析生态系统的一部分。四个 为这项拟议的研究活动做出贡献的实验室已经创建了一个开放和综合的团队，该团队正在继续 创造性地创新新的信息工具，以增强小分子结构注释和化学推断 和生物学特性。我们有四个具体目标：1）完成一套新的开发和评估 和用于天然产物分析的创新工具，并将它们作为免费资源提供给全世界 社区。 2）通过利用存储库规模质量来完善分子的结构表征 光谱信息以及 NMR 数据和基因组输入。 3) 创建一个新的基于 SMART 的工具 集成质谱和 HSQC NMR 数据作为新深度学习系统的输入，其目标是 实现更准确的结构预测。 4）利用深度学习通过生物活性数据增强SMART 从而使SMART能够根据光谱特征预测分子的活动。数据也将增加 具有生物测定结合数据的 GNPS 数据库。这些目标的另一个后果将是进一步 天然产品分析数据的数字化，以便它们可以用于本文计划的计算工具中，如 以及未来的其他工具。完成这四个具体目标将为精确的 识别新的天然产物结构，并推断它们与其他类别的结构相关性 有机分子及其生物学特性。因此，这些新的信息工具将有潜力极大地 加强小分子药物发现过程。