A data-driven integrative platform for computational prediction of toxin biotransformation with a case study.

Dachuan Zhang, Ye Tian, Yu Tian, Huadong Xing, Sheng Liu, Haoyang Zhang, Shaozhen Ding, Pengli Cai, Dandan Sun, Tong Zhang, Yanhong Hong, Hongkun Dai, Weizhong Tu, Junni Chen, Aibo Wu, Qian-Nan Hu

Journal: Journal of hazardous materials 2021;408():124810

PMID: 33360695

Full Text on Elsevier Science View on PubMed

Abstract

Recently, biogenic toxins have received increasing attention owing to their high contamination levels in feed and food as well as in the environment. However, there is a lack of an integrative platform for seamless linking of data-driven computational methods with 'wet' experimental validations. To this end, we constructed a novel platform that integrates the technical aspects of toxin biotransformation methods. First, a biogenic toxin database termed ToxinDB (http://www.rxnfinder.org/toxindb/), containing multifaceted data on more than 4836 toxins, was built. Next, more than 8000 biotransformation reaction rules were extracted from over 300,000 biochemical reactions extracted from ~580,000 literature reports curated by more than 100 people over the past decade. Based on these reaction rules, a toxin biotransformation prediction model was constructed. Finally, the global chemical space of biogenic toxins was constructed, comprising ~550,000 toxins and putative toxin metabolites, of which 94.7% of the metabolites have not been previously reported. Additionally, we performed a case study to investigate citrinin metabolism in Trichoderma, and a novel metabolite was identified with the assistance of the biotransformation prediction tool of ToxinDB. This unique integrative platform will assist exploration of the 'dark matter' of a toxin's metabolome and promote the discovery of detoxification enzymes.

Address: CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China.; School of Biology and Pharmaceutical Engineering, Wuhan Polytechnic University, Wuhan 430023, PR China; Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, PR China.; College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an 710119, PR China.; CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China.; Shandong Runda Testing Technology Co. Limited, Weifang 261000, PR China.; Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, PR China.; CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. Electronic address: [email protected].; CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. Electronic address: [email protected].

DOI: 10.1016/j.jhazmat.2020.124810

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MeSH Terms: Biotransformation, Computational Biology, Databases, Factual, Humans, Metabolome