Next generation, 'Standards-Free' Metabolite Identification Pipeline

研究计划摘要 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。相比之下， 遗传信息，对人类代谢组的整体了解相对较少，主要是由于 在分子鉴定方法中的不确定性。通过计算化学的创新，我们建议 克服了代谢组学领域中一个重要的长期存在的障碍：缺乏准确和 小分子的全面鉴定，而不依赖于来自真实化学标准品的分析数据。一 在代谢组学的范式转变，我们将使用气相分子的性质，碰撞截面，这可以是两个 准确预测计算和一致的实验测量，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 能够通过优化鉴定当前的代谢物来考虑人体代谢组的整体性， 无法识别的分子，最终达到数十万个分子，和（ii）开发标准化的 计算工具，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，因为它利用了高通量，高精度，量子化学为基础的 计算和化学信息学平台，用于从第一个阶段预测代谢物的物理化学性质 原则

Research Plan Abstract 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, we propose to overcome a significant, long standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of small molecules without relying on data from analysis of authentic chemical standards. A paradigm shift in metabolomics, we will use a gas-phase molecular property, collision cross section, 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 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 a high throughput, high accuracy, quantum-chemistry-based computational and chemical informatics platform to predict physical-chemical properties of metabolites from first principles.