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Title: Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

Abstract

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy ofmore » the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1];  [1]
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  1. University of Luxembourg, Belvaux (Luxembourg). Luxembourg Centre for Systems Biomedicine
Publication Date:
Research Org.:
University of Luxembourg, Belvaux (Luxembourg)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1424907
Grant/Contract Number:  
SC0010429
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Cheminformatics
Additional Journal Information:
Journal Volume: 9; 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; Atom mapping; Metabolic network reconstruction; Automation; RDT; DREAM; AutoMapper; CLCA; MWED; ICMAP; Recon 3D

Citation Formats

Preciat Gonzalez, German A., El Assal, Lemmer R. P., Noronha, Alberto, Thiele, Ines, Haraldsdóttir, Hulda S., and Fleming, Ronan M. T. Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D. United States: N. p., 2017. Web. doi:10.1186/s13321-017-0223-1.
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Preciat Gonzalez, German A., El Assal, Lemmer R. P., Noronha, Alberto, Thiele, Ines, Haraldsdóttir, Hulda S., & Fleming, Ronan M. T. Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D. United States. https://doi.org/10.1186/s13321-017-0223-1
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Preciat Gonzalez, German A., El Assal, Lemmer R. P., Noronha, Alberto, Thiele, Ines, Haraldsdóttir, Hulda S., and Fleming, Ronan M. T. Wed . "Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D". United States. https://doi.org/10.1186/s13321-017-0223-1. https://www.osti.gov/servlets/purl/1424907.
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@article{osti_1424907,
title = {Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D},
author = {Preciat Gonzalez, German A. and El Assal, Lemmer R. P. and Noronha, Alberto and Thiele, Ines and Haraldsdóttir, Hulda S. and Fleming, Ronan M. T.},
abstractNote = {The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.},
doi = {10.1186/s13321-017-0223-1},
journal = {Journal of Cheminformatics},
number = 1,
volume = 9,
place = {United States},
year = {Wed Jun 14 00:00:00 EDT 2017},
month = {Wed Jun 14 00:00:00 EDT 2017}
}
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https://doi.org/10.1186/s13321-017-0223-1
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Comparative evaluation of open source software for mapping between metabolite identifiers in metabolic network reconstructions: application to Recon 2
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Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks
journal, November 2016

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    Works referencing / citing this record:

    Recon3D enables a three-dimensional view of gene variation in human metabolism
    journal, February 2018

    • Brunk, Elizabeth; Sahoo, Swagatika; Zielinski, Daniel C.
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    Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0
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    Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift
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    Automatic mapping of atoms across both simple and complex chemical reactions
    journal, March 2019

    • Jaworski, Wojciech; Szymkuć, Sara; Mikulak-Klucznik, Barbara
    • Nature Communications, Vol. 10, Issue 1
    • DOI: 10.1038/s41467-019-09440-2
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