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Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics
Lochana C Menikarachchi1,2, Ritvik Dubey3, Dennis W Hill4
1Department of Pharmacy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya 20400, Sri Lanka. lochanac@gmail.com.
Metabolites
|June 4, 2016
Summary
Identifying unknown metabolites in metabolomics is challenging. A new method, database assisted structure identification (DASI), uses candidate structures to generate constraints, improving the identification of unknown-unknowns, especially those under 200 Da.
Area of Science:
- Metabolomics
- Computational Chemistry
- Bioinformatics
Background:
- Metabolite structure identification is crucial for nontargeted metabolomics.
- Exact mass database searching fails for novel or unknown compounds (unknown-unknowns).
- Combinatorial structure generation offers a potential solution for unknown structures.
Purpose of the Study:
- To evaluate the effectiveness of structure generation constraints for identifying unknown metabolites.
- To develop and assess a database-assisted structure identification (DASI) approach.
Main Methods:
- DASI utilizes predictive models (MolFind) to generate candidate structures with similar properties.
- Candidate structures are used to generate seed structures via eight algorithms.
- Constraints include required/disallowed substructures and filters to remove incorrect structures.
Main Results:
- One algorithm successfully generated correct seed structures for 21 out of 39 test compounds.
- Eleven seed structures were large enough to limit combinatorial generation to <100,000 possibilities.
- A correct seed structure was generated in 35 out of 39 cases.
Conclusions:
- The DASI method shows promise for identifying unknown-unknown metabolites.
- The approach is currently most effective for small molecules (<200 Da).
- Further experimental validation and optimization are required for DASI.
