Pacific Northwest Advanced Compound Identification Core

总体汇总 化学鉴定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。 对疾病的环境、饮食和代谢决定因素的研究。与几乎全面的遗传学相比， 虽然没有足够的信息，但对人体代谢组的整体了解相对较少，主要是由于 分子鉴定方法通过在计算化学和先进的离子迁移率分离创新 结合质谱分析，我们提出克服代谢组学领域中一个重要的、长期存在的障碍： 缺乏不依赖分析数据而准确和全面鉴定代谢物的方法 真正的化学标准。代谢组学的范式转变，我们将使用气相分子特性， 这两个准确预测的计算和一致的测量实验，因此可以用于 代谢组的全面鉴定，而不需要真实的化学标准。这个结果 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 通过优化鉴定目前无法鉴定的代谢物， 分子，最终达到数十万个分子，以及（ii）开发标准化的计算工具 和分析方法，以提高国家生物医学研究人员快速鉴定代谢物的能力， 准确地这项工作是重要的，因为它使全面和可靠的化学测量的 代谢组这项工作是创新的，因为它利用了集成的量子化学和机器学习 计算流水线，以准确地预测与测量耦合的代谢物的物理化学性质。

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.