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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.
摘要：
最近,生物毒素由于其在饲料和食品以及环境中的高污染水平而受到越来越多的关注。然而,缺乏将数据驱动的计算方法与“湿”实验验证无缝连接的综合平台。为此,我们构建了一个新颖的平台，集成了毒素生物转化方法的技术方面。首先,称为ToxinDB的生物毒素数据库(http://www.rxnfinder.org/toxindb/),包含超过4836种毒素的多方面数据，已建成。接下来,在过去的十年中，从100多人策划的约58万篇文献报道中提取的300,000多个生化反应中，提取了8000多个生物转化反应规则。根据这些反应规则，建立了毒素生物转化预测模型。最后,构建了生物毒素的全球化学空间，包含约550,000种毒素和推定的毒素代谢物，其中94.7%的代谢物以前没有报道过。此外,我们进行了一个案例研究，以研究木霉菌中的桔霉素代谢，并在ToxinDB的生物转化预测工具的帮助下鉴定出了一种新的代谢产物。这个独特的综合平台将有助于探索毒素代谢组的“暗物质”，并促进解毒酶的发现。
