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ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

View ORCID ProfileAnastasia Sveshnikova, View ORCID ProfileHoma MohammadiPeyhani, View ORCID ProfileVassily Hatzimanikatis
doi: https://doi.org/10.1101/2021.12.06.471405
Anastasia Sveshnikova
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
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  • ORCID record for Anastasia Sveshnikova
Homa MohammadiPeyhani
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
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Vassily Hatzimanikatis
1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
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Abstract

Synthetic biology and metabolic engineering rely on computational search tools for predictions of novel biosynthetic pathways to industrially important compounds, many of which are derived from aromatic amino acids. Pathway search tools vary in their scope of covered reactions and compounds, as well as in metrics for ranking and evaluation. In this work, we present a new computational resource called ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer. It consists of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 28’000 known and 100’000 novel reactions predicted with generalized enzymatic reactions rules and over 70’000 compounds, of which 22’000 are known to biochemical databases and 48’000 only to PubChem. Over 1,000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included into the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways to predict valuable compound derivations. In line with the standards of open science, we have made the toolbox freely available to the scientific community at http://lcsb-databases.epfl.ch/arbre/. We envision that ARBRE will provide the community with a new computational toolbox and comprehensive search tool to predict and rank pathways towards industrially important aromatic compounds.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://lcsb-databases.epfl.ch/arbre/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 06, 2021.
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ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds
Anastasia Sveshnikova, Homa MohammadiPeyhani, Vassily Hatzimanikatis
bioRxiv 2021.12.06.471405; doi: https://doi.org/10.1101/2021.12.06.471405
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ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds
Anastasia Sveshnikova, Homa MohammadiPeyhani, Vassily Hatzimanikatis
bioRxiv 2021.12.06.471405; doi: https://doi.org/10.1101/2021.12.06.471405

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