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 该提议的续订应用程序的总体目标是进一步推进正在开发的工具和 有效地将几种类型的分析数据与生物测定数据和基因组信息整合在一起。这将创建一个 强大的一组工具，可更快，更准确地识别新分子，对已知分子的删除以及 从光谱信息中直接推断生物学活动。在当前的支持时期，我们已经做到了 在开发有机分子的自动注释和识别的非常有用的工具方面的实质进展， 特别专注于天然产品。全球天然产品社会（GNP）分子网络分析和 知识传播生态系统已在全球近160个国家 /地区处理近160,000个工作岗位，拥有4-6,000个 每月的新工作提交，每月访问超过200,000次（多数访问是参考库 访问，检查社区在论文中共享超链接的公共数据和以前的工作），并已成为 主流工具，用于衍生自各种来源的有机分子的注释，尤其是在代谢组中 工作流程。小分子准确识别技术（SMART）的公共网站，一种深度学习模型 为基于1H-13C HSQC NMR数据提供候选结构，该数据于2019年12月上线，已经结束 50个国家 /地区的3000个工作。本提案中开发的所有工具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创造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及其化学物质的推断 和生物学特性。我们有四个具体的目标：1）完成一组新的开发和评估 以及用于天然产品分析的创新工具，并将这些工具作为全球范围内的自由提供资源 社区。 2）通过利用存储库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）创建一个新的基于智能的工具 将质谱和HSQC NMR数据集成为新的深度学习系统的输入 实现更准确的结构预测。 4）使用深度学习通过生物活性数据增强智能 为了使SMART能够根据光谱特征预测分子的活动。数据还将增加 具有生物测定结合数据的GNP数据库。这些目标的另一个后果将进一步 天然产品分析数据的数字化，以便可以将其用于此处计划的计算工具，作为 以及将来的其他工具。完成这四个特定目标的完成将为精确创建新的集成工具 识别新的天然产品结构，并可以推断其结构相关性与其他类别的相关性 有机分子及其生物学特性。因此，这些新的信息工具将有可能极大地 增强小分子药物发现过程。