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Pickaxe: a Python library for the prediction of novel metabolic reactions

BACKGROUND: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted...

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Detalles Bibliográficos
Autores principales: Shebek, Kevin M., Strutz, Jonathan, Broadbelt, Linda J., Tyo, Keith E. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031857/
https://www.ncbi.nlm.nih.gov/pubmed/36949401
http://dx.doi.org/10.1186/s12859-023-05149-8
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author Shebek, Kevin M.
Strutz, Jonathan
Broadbelt, Linda J.
Tyo, Keith E. J.
author_facet Shebek, Kevin M.
Strutz, Jonathan
Broadbelt, Linda J.
Tyo, Keith E. J.
author_sort Shebek, Kevin M.
collection PubMed
description BACKGROUND: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. RESULTS: Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. CONCLUSION: Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05149-8.
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spelling pubmed-100318572023-03-23 Pickaxe: a Python library for the prediction of novel metabolic reactions Shebek, Kevin M. Strutz, Jonathan Broadbelt, Linda J. Tyo, Keith E. J. BMC Bioinformatics Software BACKGROUND: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. RESULTS: Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. CONCLUSION: Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05149-8. BioMed Central 2023-03-22 /pmc/articles/PMC10031857/ /pubmed/36949401 http://dx.doi.org/10.1186/s12859-023-05149-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Shebek, Kevin M.
Strutz, Jonathan
Broadbelt, Linda J.
Tyo, Keith E. J.
Pickaxe: a Python library for the prediction of novel metabolic reactions
title Pickaxe: a Python library for the prediction of novel metabolic reactions
title_full Pickaxe: a Python library for the prediction of novel metabolic reactions
title_fullStr Pickaxe: a Python library for the prediction of novel metabolic reactions
title_full_unstemmed Pickaxe: a Python library for the prediction of novel metabolic reactions
title_short Pickaxe: a Python library for the prediction of novel metabolic reactions
title_sort pickaxe: a python library for the prediction of novel metabolic reactions
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031857/
https://www.ncbi.nlm.nih.gov/pubmed/36949401
http://dx.doi.org/10.1186/s12859-023-05149-8
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