Suppr超能文献

对 6000 种化合物进行解偶联活性筛选:机制生物物理模型与结构警示筛选器 Mitotox 的比较。

Screening of 6000 Compounds for Uncoupling Activity: A Comparison Between a Mechanistic Biophysical Model and the Structural Alert Profiler Mitotox.

机构信息

Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research-UFZ, D-04318 Leipzig, Germany.

Institute of Chemistry, Martin Luther University, D-06120 Halle, Germany.

出版信息

Toxicol Sci. 2022 Jan 24;185(2):208-219. doi: 10.1093/toxsci/kfab139.

Abstract

Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes, or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in vitro uncoupling activity, using a biophysical model based on ab initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.

摘要

质子传递解偶联是评估化学物质毒性的一个重要因素,最近在药物研究中也成为关注焦点,涉及癌症、糖尿病或肥胖等疾病的治疗。因此,可靠地识别解偶联活性是一个有价值的目标。为此,我们使用基于从头算 COSMO-RS 输入参数的生物物理模型,仅以外层分子结构作为输入,筛选了超过 6000 种阴离子化合物,以评估其体外解偶联活性。我们将这些结果与基线毒性(麻醉)模型相结合。我们的模型在筛选数据集中确定了 1250 多种可能的解偶联剂,并确定了可能的新解偶联剂类别,如硫代磷酸。当该模型对数据集内的 423 种已知解偶联剂和 612 种已知非活性化合物进行测试时,其敏感性为 83%,特异性为 96%。在直接比较中,它显示出与 Mitotox(97%)相似的特异性,但敏感性远高于 Mitotox(47%)。因此,与现有的结构警报筛选器相比,该生物物理模型可更准确地筛选出解偶联活性。我们建议在药物开发和毒性评估中,将我们的模型用作筛选质子传递解偶联活性的大型数据集的补充工具。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验