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Site of Reactivity Models Predict Molecular Reactivity of Diverse Chemicals with Glutathione

机译:反应模型的位点可预测多种化学物质与谷胱甘肽的分子反应性

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摘要

Drug toxicity is often caused by electrophilic reactive metabolites that covalently bind to proteins. Consequently, the quantitative strength of a molecules reactivity with glutathione (GSH) is a frequently used indicator of its toxicity. Through cysteine, GSH (and proteins) scavenges reactive molecules to form conjugates in the body. GSH conjugates to specific atoms in reactive molecules: their sites of reactivity. The value of knowing a molecules sites of reactivity is unexplored in the literature. This study tests the value of site of reactivity data that identifies the atoms within 1213 reactive molecules that conjugate to GSH and builds models to predict molecular reactivity with glutathione. An algorithm originally written to model sites of cytochrome P450 metabolism (called XenoSite) finds clear patterns in molecular structure that identify sites of reactivity within reactive molecules with 90.8% accuracy and separate reactive and unreactive molecules with 80.6% accuracy. Furthermore, the model output strongly correlates with quantitative GSH reactivity data in chemically diverse, external data sets. Site of reactivity data is nearly unstudied in the literature prior to our efforts, yet it contains a strong signal for reactivity that can be utilized to more accurately predict molecule reactivity and, eventually, toxicity.
机译:药物毒性通常是由与蛋白质共价结合的亲电反应性代谢物引起的。因此,与谷胱甘肽(GSH)反应的分子的定量强度是其毒性的常用指标。 GSH(和蛋白质)通过半胱氨酸清除反应性分子,从而在体内形成结合物。 GSH与反应性分子中的特定原子结合:它们的反应位点。文献中尚未探讨了解分子的反应位点的价值。这项研究测试了反应性数据位点的价值,该数据可识别与GSH共轭的1213个反应性分子中的原子,并建立模型来预测与谷胱甘肽的分子反应性。最初编写用于模拟细胞色素P450代谢位点的算法(称为XenoSite)可在分子结构中找到清晰的模式,以90.8%的精度识别反应性分子内的反应位点,并以80.6%的精度分离反应性和非反应性分子。此外,模型输出与化学上多样化的外部数据集中的定量GSH反应性数据密切相关。在我们努力之前,文献中几乎没有研究反应性数据的位点,但是它包含一个强烈的反应性信号,可用于更准确地预测分子反应性并最终预测毒性。

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