Pacific Northwest Advanced Compound Identification Core

太平洋西北高级化合物鉴定核心

• 批准号: 10260964
• 负责人: Thomas O Metz
• 金额: $ 15.25万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260964
• 关键词: Adoption; Algorithms; Analytical Chemistry; Attributes of Chemicals; Biological; Biological Markers; Biomedical Research; Chemical Structure; Chemicals; Clinical; Communities; Computers and Advanced Instrumentation; Computing Methodologies; Coupled; Data; Data Analyses; Databases; Dependence; Diet; Disease; Educational workshop; Engineering; Exposure to; Funding; Gases; Genetic; Goals; High Performance Computing; Human; Isotopes; Libraries; Liquid substance; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Metabolic; Methodology; Methods; Mission; Molecular; Outcome; Pacific Northwest; Phase; Predictive Analytics; Probability; Procedures; Property; Reference Standards; Research Personnel; Resolution; Resources; Sampling; Science; Serum; Source; Standardization; Structure; Supercomputing; Techniques; Technology; Testing; Time; Toxin; Training; Uncertainty; United States National Institutes of Health; Urine; Work; analytical method; base; chemical property; chemical standard; comparative; computational chemistry; computational pipelines; computerized tools; dark matter; drug candidate; drug discovery; experience; genetic information; human disease; improved; in silico; innovation; instrumentation; ion mobility; metabolome; metabolomics; non-genetic; novel; novel therapeutics; programs; quantum chemistry; small molecule libraries; stereochemistry; tool

OVERALL SUMMARY The capability to chemically identify thousands of metabolites and other chemicals in clinical samples will revolutionize the search for environmental, dietary, and metabolic determinants of disease. By comparison to near-comprehensive genetic information, comparatively little is understood of the totality of the human metabolome, largely due to insufficiencies in molecular identification methods. Through innovations in computational chemistry and advanced ion mobility separations coupled with mass spectrometry, we propose to overcome a significant, long standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of metabolites without relying on data from analysis of authentic chemical standards. A paradigm shift in metabolomics, we will use gas-phase molecular properties that can be both accurately predicted computationally and consistently measured experimentally, and which can thus be used for comprehensive identification of the metabolome without the need for authentic chemical standards. The outcomes of this proposal directly advance the mission and goals of the NIH Common Fund by: (i) transforming metabolomics science by enabling consideration of the totality of the human metabolome through optimized identification of currently unidentifiable molecules, eventually reaching hundreds of thousands of molecules, and (ii) developing standardized computational tools and analytical methods to increase the national capacity for biomedical researchers to identify metabolites quickly and accurately. This work is significant because it enables comprehensive and confident chemical measurement of the metabolome. This work is innovative because it utilizes an integrated quantum-chemistry and machine learning computational pipeline to accurately predict physical-chemical properties of metabolites coupled to measurements.

总体总结 以化学方式识别临床样本中数千种代谢物和其他化学物质的能力将带来革命性的变化 寻找疾病的环境、饮食和代谢决定因素。与近乎全面的遗传相比 人们对人类代谢组的整体了解相对较少，这主要是由于缺乏足够的信息 分子鉴定方法。通过计算化学和先进离子淌度分离的创新 与质谱相结合，我们建议克服代谢组学领域长期存在的重大障碍： 缺乏在不依赖分析数据的情况下准确、全面鉴定代谢物的方法 真实的化学标准。代谢组学的范式转变，我们将使用气相分子特性，可以 既可以通过计算准确预测，也可以通过实验一致测量，因此可用于 全面鉴定代谢组，无需可靠的化学标准。这样做的结果 该提案通过以下方式直接推进 NIH 共同基金的使命和目标：(i) 通过以下方式转变代谢组学科学： 通过优化识别当前无法识别的物质，能够考虑人类代谢组的整体 分子，最终达到数十万个分子，以及（ii）开发标准化计算工具 和分析方法，以提高生物医学研究人员快速识别代谢物的国家能力 准确。这项工作意义重大，因为它能够对 代谢组。这项工作具有创新性，因为它利用了集成的量子化学和机器学习 计算管道准确预测与测量耦合的代谢物的物理化学性质。