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Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx

Author

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  • Homa MohammadiPeyhani

    (Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL
    Pharmaceutical Research and Early Development, Roche Glycart AG)

  • Jasmin Hafner

    (Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL
    EAWAG Swiss Federal Institute of Aquatic Science and Technology)

  • Anastasia Sveshnikova

    (Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL)

  • Victor Viterbo

    (Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL)

  • Vassily Hatzimanikatis

    (Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL)

Abstract

Metabolic “dark matter” describes currently unknown metabolic processes, which form a blind spot in our general understanding of metabolism and slow down the development of biosynthetic cell factories and naturally derived pharmaceuticals. Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 489 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of biochemical pathways and evaluates the biochemical vicinity of molecule classes ( https://lcsb-databases.epfl.ch/Atlas2 ).

Suggested Citation

  • Homa MohammadiPeyhani & Jasmin Hafner & Anastasia Sveshnikova & Victor Viterbo & Vassily Hatzimanikatis, 2022. "Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29238-z
    DOI: 10.1038/s41467-022-29238-z
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    Cited by:

    1. Wenna Li & Zhao Zhou & Xianglai Li & Lin Ma & Qingyuan Guan & Guojun Zheng & Hao Liang & Yajun Yan & Xiaolin Shen & Jia Wang & Xinxiao Sun & Qipeng Yuan, 2022. "Biosynthesis of plant hemostatic dencichine in Escherichia coli," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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