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. 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 gas-phase molecular properties, collision cross section, MS/MS spectra, accurate mass, and isotopic distribution that can be both accurately predicted computationally and consistently measured experimentally, and which can thus be used for comprehensive identification of the metabolome. The outcomes of this proposal directly advance the mission and goals of the NIH Common Fund by: (i) accurately calculating chemical properties using an integrated, scalable high-performance computational chemistry pipeline, (ii) generating in silico reference data for an initial target of 500,000 molecules comprising both known and novel metabolites, (iii) developing and validating a multi-property feature matching approach for unambiguous chemical identification in biomedical samples, and (iv) disseminating the computational tools, algorithms, and resources . 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.
| Couvillion, Sneha P; Zhu, Ying; Nagy, Gabe et al. (2018) New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells. Analyst : |
