电鳐乙酰胆碱酯酶活性中心负矩部位构成其构象决定族的一部分

目的：研究电鳐乙酰胆碱酯酶（ＡＣｈＥ）结构功能关系，探讨ＡＣｈＥ负矩部位是否是其构象决定簇的一个组成部分。方法：用ＥＬＩＳＡ及酶抑制实验观察ＡＣｈＥ负矩部位探针２－ＰＡＭ对ＡＣｈＥ与其ＭｅＡｂ ３Ｆ３之间的免疫反应性的影响。结果：ＭｃＡｂ３Ｆ３不能与２－ＰＡＭ及ＡＣｈＥ的复合物反应；２－ＰＡＭ浓度依赖性地降低ＭｃＡｂ ３Ｆ３对ＡＣｈＥ的抑制率；但不能解离ＡＣｈＥ与３Ｆ３构成的抗原抗体复合物。结论     

单克隆抗体3F3识别电鳐乙酰胆碱酯酶构象抗原决定簇(英文)

目的:研究天然电鳐乙酰胆碱酯酶(AChE)单克隆抗体3F3针对的抗原决定簇的类型.方法:以酶联免疫吸附试验测定抗原与抗体间的反应.结果:单克隆抗体3F3能与天然AChE反应,而不与还原并烷基化的AChE(RA-AChE)反应;梭曼不影响3F3与AChE的结合;化学合成的含AChE活性中心丝氨酸的24肽不与3F3反应.结论:3F3是识别电鳐AChE活性中心构象抗原决定簇的单克隆抗体,但它不占据AChE活性中心的丝氨酸残基.     

Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design

The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning prediction models arrive at their decisions, which are called explainable artificial intelligence. First, we developed the prediction models for metabolic stability, CYP inhibition, and P-gp and BCRP substrate recognition using 265 physicochemical parameters for designing the molecular structures. Four important parameters, including the well-known indicator h_logD, are common in some in vitro studies; as such, these can be used to optimize compounds simultaneously to address multiple pharmacokinetic concerns. Next, we developed machine learning models that had been programmed to show structurally active sites. Many types of machine learning models were developed using the results of in vitro metabolic stability study of around 30000 in-house compounds. The metabolic sites of in-house compounds predicted using some prediction models matched experimentally identified metabolically active sites, with a ratio of number of metabolic sites (predicted/actual) of over 90%. These models can be applied to several screening projects. These two approaches can be employed for obtaining lead compounds with desirable pharmacokinetic profiles efficiently.