对比和对比公开可用的结构性警报对蛋白质结合的覆盖范围
Mark D Nelms1,2, Ryan Lougee1,2, David W Roberts3
1Oak Ridge Institute for Science and Education (ORISE), 1299 Bethel Valley Road, Oak Ridge, TN 37830, USA.
Computational toxicology (Amsterdam, Netherlands)
|September 13, 2023
概括
识别电友化学结构是预测皮肤敏感性等毒理学效应的关键. 本研究将计算工具进行比较,以评估它们在不同化学库中识别这些反应性特征的有效性.
科学领域:
- 计算毒理学和化学信息学.
- 预测性毒理学和结构-活性关系.
背景情况:
- 许多毒理学途径的初步步骤涉及电友化学物质与蛋白质和DNA等生物分子共聚结合.
- 与蛋白质和DNA的共价 adduct 形成与皮肤敏感化和基因毒性密切相关.
- 识别电友结构特征对于描述化学反应性和预测毒性至关重要.
研究的目的:
- 为了比较三个已建立的计算方案来识别电友结构特征 (SMARTS模式).
- 为了评估这些方案的范围,覆盖范围和性能,与专家注释的皮肤敏感化数据集进行对比.
- 将方案应用于Tox21化学库,以识别潜在的反应性化合物.
主要方法:
- 三个在文献中发表的代表化学-生物反应领域的 SMARTS 模式集合的比较.
- 使用皮肤敏感化数据集与手动机械学域注释进行性能评估.
- 将共识 SMARTS 模式应用于 Tox21 库,并分析丰富的 ToxPrint 指纹.
主要成果:
- 该研究评估了三个计算方案的性能,用于识别电友特征.
- 对Tox21库的应用揭示了在特定机械学领域中可能表现出反应性的化学物质的比例.
- 丰富的ToxPrints有助于比较皮肤敏感化数据集和Tox21化学物质之间的结构特征,并确定了相关的ToxCast测试.
结论:
- 电友特征的计算识别是评估化学反应性和毒理潜力的有价值的起点.
- 通过比较不同方案,可以了解它们在特定毒理终点上的优缺点.
- 达成共识的方法和对丰富的结构特征的分析可以帮助优先考虑进一步测试的化学品,并确定信息分析.
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