Next generation, 'Standards-Free' Metabolite Identification Pipeline

研究计划摘要 对临床样本中数以千计的代谢物和其他化学物质进行化学鉴定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 遗传信息，对人类代谢组的整体了解相对较少，主要是由于 分子鉴定方法的不足。通过计算化学的创新，我们建议 克服代谢组学领域长期存在的一个重大障碍：缺乏准确和 全面识别小分子，而不依赖于分析可信的化学标准的数据。一个 在代谢组学的范式转换中，我们将利用气相分子的性质，碰撞截面，即可以 通过计算准确预测，并通过实验进行一致测量，因此可以用于 代谢物的全面鉴定，不需要真实的化学标准。这样做的结果是 该提案直接推动了NIH共同基金的使命和目标：(I)通过以下方式转变代谢组学 通过对当前代谢组的优化识别，能够考虑人类代谢组的整体 无法识别的分子，最终达到数十万个分子，以及(Ii)开发标准化 提高生物医学研究人员快速识别代谢物的国家能力的计算工具 准确地说。这项工作意义重大，因为它使全面和可信的化学测量成为可能 代谢体。这项工作具有创新性，因为它利用了高通量、高精度、基于量子化学的 计算和化学信息学平台从一开始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.