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Title: MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

Abstract

Metabolomics have proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations formore » 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.« less

Authors:
 [1];  [2];  [3];  [3];  [4];  [5];  [4];  [6];  [5];  [2]
+ Show Author Affiliations
  1. Northwestern Univ., Evanston, IL (United States); Argonne National Lab., Argonne, IL (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Univ. of California, Davis, CA (United States)
  4. Univ. of Florida, Gainesville, FL (United States)
  5. Northwestern Univ., Evanston, IL (United States)
  6. Univ. of California, Davis, CA (United States); King Abdulaziz Univ., Jeddah (Saudi Arabia)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1221820
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Cheminformatics
Additional Journal Information:
Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 1758-2946
Publisher:
Chemistry Central Ltd.
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING; enzyme promiscuity; untargeted metabolomics; liquid chromatography–mass spectrometry; metabolite identification

Citation Formats

Jeffryes, James G., Colastani, Ricardo L., Elbadawi-Sidhu, Mona, Kind, Tobias, Niehaus, Thomas D., Broadbelt, Linda J., Hanson, Andrew D., Fiehn, Oliver, Tyo, Keith E. J., and Henry, Christopher S. MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. United States: N. p., 2015. Web. doi:10.1186/s13321-015-0087-1.
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Jeffryes, James G., Colastani, Ricardo L., Elbadawi-Sidhu, Mona, Kind, Tobias, Niehaus, Thomas D., Broadbelt, Linda J., Hanson, Andrew D., Fiehn, Oliver, Tyo, Keith E. J., & Henry, Christopher S. MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. United States. https://doi.org/10.1186/s13321-015-0087-1
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Jeffryes, James G., Colastani, Ricardo L., Elbadawi-Sidhu, Mona, Kind, Tobias, Niehaus, Thomas D., Broadbelt, Linda J., Hanson, Andrew D., Fiehn, Oliver, Tyo, Keith E. J., and Henry, Christopher S. Fri . "MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics". United States. https://doi.org/10.1186/s13321-015-0087-1. https://www.osti.gov/servlets/purl/1221820.
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@article{osti_1221820,
title = {MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics},
author = {Jeffryes, James G. and Colastani, Ricardo L. and Elbadawi-Sidhu, Mona and Kind, Tobias and Niehaus, Thomas D. and Broadbelt, Linda J. and Hanson, Andrew D. and Fiehn, Oliver and Tyo, Keith E. J. and Henry, Christopher S.},
abstractNote = {Metabolomics have proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.},
doi = {10.1186/s13321-015-0087-1},
journal = {Journal of Cheminformatics},
number = 1,
volume = 7,
place = {United States},
year = {Fri Aug 28 00:00:00 EDT 2015},
month = {Fri Aug 28 00:00:00 EDT 2015}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1186/s13321-015-0087-1
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Citation Metrics:
Cited by: 140 works
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Figures / Tables:

Fig. 1 Fig. 1: MINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.
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Mass Spectral Reference Libraries: An Ever-Expanding Resource for Chemical Identification
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MolFind: A Software Package Enabling HPLC/MS-Based Identification of Unknown Chemical Structures
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Extending biochemical databases by metabolomic surveys.
journal, August 2011

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Metabolite and reaction inference based on enzyme specificities
journal, August 2009

Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds
journal, April 2011

Metabolite identification and molecular fingerprint prediction through machine learning
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PathPred: an enzyme-catalyzed metabolic pathway prediction server
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journal, March 2010

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Systematic Applications of Metabolomics in Metabolic Engineering
journal, December 2012

CASMI: And the Winner is . . .
journal, May 2013

Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID
journal, June 2013

  • Shen, Huibin; Zamboni, Nicola; Heinonen, Markus
  • Metabolites, Vol. 3, Issue 2
  • DOI: 10.3390/metabo3020484
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