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Metabolites 2016, 6(2), 17; doi:10.3390/metabo6020017

Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics

1
Department of Pharmacy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya 20400, Sri Lanka
2
Department of Pharmaceutical Sciences, University of Connecticut, 69 N Eagleville Rd, Storrs, CT 06269, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Peter D. Karp
Received: 17 April 2016 / Revised: 26 May 2016 / Accepted: 27 May 2016 / Published: 31 May 2016
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
View Full-Text   |   Download PDF [13302 KB, uploaded 31 May 2016]   |  

Abstract

Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not contain the unknown metabolite (i.e., for unknown-unknowns). For these cases, constrained structure generation with combinatorial structure generators provides a potential option. Here we evaluated structure generation constraints based on the specification of: (1) substructures required (i.e., seed structures); (2) substructures not allowed; and (3) filters to remove incorrect structures. Our approach (database assisted structure identification, DASI) used predictive models in MolFind to find candidate structures with chemical and physical properties similar to the unknown. These candidates were then used for seed structure generation using eight different structure generation algorithms. One algorithm was able to generate correct seed structures for 21/39 test compounds. Eleven of these seed structures were large enough to constrain the combinatorial structure generator to fewer than 100,000 structures. In 35/39 cases, at least one algorithm was able to generate a correct seed structure. The DASI method has several limitations and will require further experimental validation and optimization. At present, it seems most useful for identifying the structure of unknown-unknowns with molecular weights <200 Da. View Full-Text
Keywords: nontargeted metabolomics; mass spectrometry; liquid chromatography; in silico structure generation nontargeted metabolomics; mass spectrometry; liquid chromatography; in silico structure generation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Menikarachchi, L.C.; Dubey, R.; Hill, D.W.; Brush, D.N.; Grant, D.F. Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics. Metabolites 2016, 6, 17.

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