Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx

Homa MohammadiPeyhani, Jasmin Hafner, Anastasia Sveshnikova, Victor Viterbo and Vassily Hatzimanikatis ()
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Homa MohammadiPeyhani: Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL
Jasmin Hafner: Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL
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

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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 ).

Date: 2022
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DOI: 10.1038/s41467-022-29238-z

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