Cheminformatics Analysis of the Multitarget Structure-Activity Landscape of Environmental Chemicals Binding to Human Endocrine Receptors

利用化学信息学方法分析与人类内分泌受体结合的环境化学物质的多靶点结构-活性关系

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Abstract

In human exposome, environmental chemicals can target and disrupt different endocrine axes, ultimately leading to several endocrine disorders. Such chemicals, termed endocrine disrupting chemicals, can promiscuously bind to different endocrine receptors and lead to varying biological end points. Thus, understanding the complexity of molecule-receptor binding of environmental chemicals can aid in the development of robust toxicity predictors. Toward this, the ToxCast project has generated the largest resource on the chemical-receptor activity data for environmental chemicals that were screened across various endocrine receptors. However, the heterogeneity in the multitarget structure-activity landscape of such chemicals is not yet explored. In this study, we systematically curated the chemicals targeting eight human endocrine receptors, their activity values, and biological end points from the ToxCast chemical library. We employed dual-activity difference and triple-activity difference maps to identify single-, dual-, and triple-target cliffs across different target combinations. We annotated the identified activity cliffs through the matched molecular pair (MMP)-based approach and observed that a small fraction of activity cliffs form MMPs. Further, we structurally classified the activity cliffs and observed that R-group cliffs form the highest fraction among the cliffs identified in various target combinations. Finally, we leveraged the mechanism of action (MOA) annotations to analyze structure-mechanism relationships and identified strong MOA-cliffs and weak MOA-cliffs, for each of the eight endocrine receptors. Overall, insights from this first study analyzing the structure-activity landscape of environmental chemicals targeting multiple human endocrine receptors will likely contribute toward the development of better toxicity prediction models for characterizing the human chemical exposome.

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