{Reference Type}: Journal Article
{Title}: A data-driven integrative platform for computational prediction of toxin biotransformation with a case study.
{Author}: Zhang D;Tian Y;Tian Y;Xing H;Liu S;Zhang H;Ding S;Cai P;Sun D;Zhang T;Hong Y;Dai H;Tu W;Chen J;Wu A;Hu QN;
{Journal}: J Hazard Mater
{Volume}: 408
{Issue}: 0
{Year}: 04 2021 15
{Factor}: 14.224
{DOI}: 10.1016/j.jhazmat.2020.124810
{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.