Skip to main content

Pacific Northwest Advanced Compound Identification Core


EMSL Project ID
50645

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 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.

Project Details

Start Date
2018-11-26
End Date
2021-09-30
Status
Closed

Team

Principal Investigator

Thomas Metz
Institution
Pacific Northwest National Laboratory

Team Members

Kent Bloodsworth
Institution
Pacific Northwest National Laboratory

Daniel Orton
Institution
Pacific Northwest National Laboratory

Ryan Renslow
Institution
Pacific Northwest National Laboratory

Yehia Ibrahim
Institution
Pacific Northwest National Laboratory

Richard Smith
Institution
Pacific Northwest National Laboratory

Related Publications

Colby S.M., J. Nunez, N.O. Hodas, C.D. Corley, and R.S. Renslow. 2020. "Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples." Analytical Chemistry 92, no. 2:1720-1729. PNNL-SA-144150. doi:10.1021/acs.analchem.9b02348 01/21/2020