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Pickaxe: a Python library for the prediction of novel metabolic reactions.
Shebek, Kevin M; Strutz, Jonathan; Broadbelt, Linda J; Tyo, Keith E J.
Afiliación
  • Shebek KM; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
  • Strutz J; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA.
  • Broadbelt LJ; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA.
  • Tyo KEJ; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.
BMC Bioinformatics ; 24(1): 106, 2023 Mar 22.
Article en En | MEDLINE | ID: mdl-36949401
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fenómenos Bioquímicos / Escherichia coli Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fenómenos Bioquímicos / Escherichia coli Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos