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In this study, the pentachlorothiophenol (PCTP) and Chinese rare minnow (Gobiocypris rarus) transthyretin were selected as the objects of study. We determined the potential binding affinity data of pentachlorothiophenol on Chinese rare minnow transthyretin, revealed the underlying molecular recognition mechanism between pentachlorothiophenol and the protein, and derived the binary classification models for distinguishing whether a given substance was a potential Chinese rare minnow transthyretin disruptor or not by using the Chinese rare minnow transthyretin disrupting data measured in this study and previous studies. The experimental results indicated that the observed water solubility value of pentachlorothiophenol was (0.636 ± 0.0871) mg/L (25℃). The relative competing potency of pentachlorothiophenol with 3,3′,5,5′-Tetraiodo-L-thyronine (T₄) binding to Chinese rare minnow transthyretin (logRP) was (0.0200 ± 0.0533), implying that pentachlorothiophenol was a high potency Chinese rare minnow transthyretin disruptor. The molecular simulation results documented that the binding interaction between pentachlorothiophenol and Chinese rare minnow transthyretin was controlled by the halogen hydrogen bond, halogen bond, and hydrophobic interactions. In the modelling, three optimum binary classification models (i.e. k nearest neighbour model, random forest model, decision tree model) were successfully developed. The statistical parameters (e.g. predictive accuracy (Q)) values of those optimum models were 1, highlighting that those optimum models had great classification performance. Finally, the tool named "TTR Profiler" that used to identify potential transthyretin disruptors was updated by integrating the newly developed optimum models, which means that the application domain of the related models in the new tool covered the thiophenols.

, authors=Song-shan ZHAO, Jia-wei ZHANG, Zheng ZHANG, Hui-hui LIU, Xian-hai YANG^*, authorsList=Song-shan ZHAO, Jia-wei ZHANG, Zheng ZHANG, Hui-hui LIU, Xian-hai YANG, authorCompany=null, correspAuthors=Xian-hai YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314923083803227075, articleId=1314923083178275776, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=五氯苯硫酚与稀有鮈鲫运甲状腺素蛋白相互作用研究, columnId=1234106394006311784, journalTitle=中国环境科学, columnName=环境毒理与健康, runingTitle=null, highlight=null, articleAbstract=

以五氯苯硫酚（PCTP）和稀有鮈鲫（Gobiocypris rarus）运甲状腺素蛋白为研究对象，测定了五氯苯硫酚与该蛋白的亲和力数据，辨析了五氯苯硫酚与该蛋白的分子识别机制，结合本研究及文献中关于有机新污染物与稀有鮈鲫运甲状腺素蛋白间的干扰效应数据，构建了可预测苯硫酚等新污染物潜在稀有鮈鲫运甲状腺素蛋白干扰效应的二元分类预测模型．结果表明：五氯苯硫酚的水溶解度值为（0.636 ± 0.0871）mg/L（25 ℃）；五氯苯硫酚与稀有鮈鲫运甲状腺素蛋白结合的相对结合势（logRP）值为（0.0200 ± 0.0533），表明其属于具有目标蛋白高结合能力的新污染物；五氯苯硫酚与蛋白间主要存在卤氢键、卤键、疏水相互作用等非键相互作用；成功构建了三个最优二元分类模型（即k-最邻近模型、随机森林模型、决策树模型），三个模型预测准确性（Q）等统计参数值均为1，说明模型具有较好的预测能力；基于最优预测模型，更新了运甲状腺素蛋白干扰物识别工具”TTR Profiler”，使该工具相关预测模型应用域涵盖了苯硫酚类物质．

, authors=赵嵩山, 张嘉伟, 张政, 刘会会, 杨先海^*, authorsList=赵嵩山, 张嘉伟, 张政, 刘会会, 杨先海, authorCompany=null, correspAuthors=杨先海, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=qgeKTq3djMrtdRBJv/7Fmw==, magXml=ZnYErwYjhVaPdl2XqCBUOw==, pdfUrl=null, pdf=l1afXkTa2tVTbglgU8pBHw==, pdfFileSize=516254, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PpN6VVc8Kw7qH0oKoTjZ4g==, mapNumber=null, fund=null)}, authors=[Author(id=1314923085573223368, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923083178275776, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=sszhao@njust.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314923085791327178, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923083178275776, authorId=1314923085573223368, language=EN, stringName=Song-shan ZHAO, firstName=Song-shan, middleName=null, lastName=ZHAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314923085883601867, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923083178275776, authorId=1314923085573223368, language=CN, stringName=赵嵩山, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=南京理工大学环境与生物工程学院，江苏 南京 210094, bio={"content":"

赵嵩山（1994-），男，甘肃省武威市人，南京理工大学硕士研究生，主要研究方向为环境毒理学．发表论文2篇．sszhao@njust.edu.cn.

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赵嵩山（1994-），男，甘肃省武威市人，南京理工大学硕士研究生，主要研究方向为环境毒理学．发表论文2篇．sszhao@njust.edu.cn.

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