代谢网络
化学空间
计算机科学
计算生物学
空格(标点符号)
代谢途径
数据科学
生物
生物信息学
药物发现
新陈代谢
生物化学
操作系统
作者
Homa MohammadiPeyhani,Jasmin Hafner,Anastasia Sveshnikova,Victor Viterbo,Vassily Hatzimanikatis
标识
DOI:10.1101/2021.02.17.431583
摘要
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 490 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 novel biochemical pathways and evaluates the biochemical vicinity of molecule classes ( https://lcsbdatabases.epfl.ch/Atlas2 ).
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