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Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx
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 challe...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943196/ https://www.ncbi.nlm.nih.gov/pubmed/35322036 http://dx.doi.org/10.1038/s41467-022-29238-z |
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author | MohammadiPeyhani, Homa Hafner, Jasmin Sveshnikova, Anastasia Viterbo, Victor Hatzimanikatis, Vassily |
author_facet | MohammadiPeyhani, Homa Hafner, Jasmin Sveshnikova, Anastasia Viterbo, Victor Hatzimanikatis, Vassily |
author_sort | MohammadiPeyhani, Homa |
collection | PubMed |
description | 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). |
format | Online Article Text |
id | pubmed-8943196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89431962022-04-08 Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx MohammadiPeyhani, Homa Hafner, Jasmin Sveshnikova, Anastasia Viterbo, Victor Hatzimanikatis, Vassily Nat Commun Article 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). Nature Publishing Group UK 2022-03-23 /pmc/articles/PMC8943196/ /pubmed/35322036 http://dx.doi.org/10.1038/s41467-022-29238-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article MohammadiPeyhani, Homa Hafner, Jasmin Sveshnikova, Anastasia Viterbo, Victor Hatzimanikatis, Vassily Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx |
title | Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx |
title_full | Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx |
title_fullStr | Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx |
title_full_unstemmed | Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx |
title_short | Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx |
title_sort | expanding biochemical knowledge and illuminating metabolic dark matter with atlasx |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943196/ https://www.ncbi.nlm.nih.gov/pubmed/35322036 http://dx.doi.org/10.1038/s41467-022-29238-z |
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