Vivek-Ananth R P, Sahoo Ajaya Kumar, Baskaran Shanmuga Priya, Ravichandran Janani, Samal Areejit
The Institute of Mathematical Sciences (IMSc), Chennai 600113, India; Homi Bhabha National Institute (HBNI), Mumbai 400094, India.
The Institute of Mathematical Sciences (IMSc), Chennai 600113, India; Homi Bhabha National Institute (HBNI), Mumbai 400094, India.
Sci Total Environ. 2023 May 15;873:162263. doi: 10.1016/j.scitotenv.2023.162263. Epub 2023 Feb 17.
Androgen mimicking environmental chemicals can bind to Androgen receptor (AR) and can cause severe effects on the reproductive health of males. Predicting such endocrine disrupting chemicals (EDCs) in the human exposome is vital for improving current chemical regulations. To this end, QSAR models have been developed to predict androgen binders. However, a continuous structure-activity relationship (SAR) wherein chemicals with similar structure have similar activity does not always hold. Activity landscape analysis can help map the structure-activity landscape and identify unique features such as activity cliffs. Here we performed a systematic investigation of the chemical diversity along with the global and local structure-activity landscape of a curated list of 144 AR binding chemicals. Specifically, we clustered the AR binding chemicals and visualized the associated chemical space. Thereafter, consensus diversity plot was used to assess the global diversity of the chemical space. Subsequently, the structure-activity landscape was investigated using SAS maps which capture the activity difference and structural similarity among the AR binders. This analysis led to a subset of 41 AR binding chemicals forming 86 activity cliffs, of which 14 are activity cliff generators. Additionally, SALI scores were computed for all pairs of AR binding chemicals and the SALI heatmap was also used to evaluate the activity cliffs identified using SAS map. Finally, we provide a classification of the 86 activity cliffs into six categories using structural information of chemicals at different levels. Overall, this investigation reveals the heterogeneous nature of the structure-activity landscape of AR binding chemicals and provides insights which will be crucial in preventing false prediction of chemicals as androgen binders and developing predictive computational toxicity models in the future.
模仿雄激素的环境化学物质可与雄激素受体（AR）结合，并可对男性生殖健康造成严重影响。预测人类暴露组中的此类内分泌干扰化学物质（EDC）对于完善现行化学物质法规至关重要。为此，已开发出定量构效关系（QSAR）模型来预测雄激素结合剂。然而，化学结构相似的化学物质具有相似活性的连续构效关系（SAR）并不总是成立。活性景观分析有助于绘制构效景观图，并识别诸如活性悬崖等独特特征。在此，我们对144种AR结合化学物质的精选列表的化学多样性以及全局和局部构效景观进行了系统研究。具体而言，我们对AR结合化学物质进行聚类，并可视化相关的化学空间。此后，使用共识多样性图评估化学空间的全局多样性。随后，使用捕获AR结合剂之间活性差异和结构相似性的SAS图研究构效景观。该分析导致41种AR结合化学物质的一个子集形成86个活性悬崖，其中14个是活性悬崖产生剂。此外，计算了所有AR结合化学物质对的SALI分数，并且SALI热图也用于评估使用SAS图识别的活性悬崖。最后，我们利用不同层次化学物质的结构信息将86个活性悬崖分为六类。总体而言，这项研究揭示了AR结合化学物质构效景观的异质性，并提供了重要见解，这对于防止将化学物质错误预测为雄激素结合剂以及未来开发预测性计算毒性模型至关重要。
