虚拟筛选技术辅助SERT抑制剂高通量筛选模型的建立及应用

Development of HTS model on SERT inhibitors combined biological screening model with HTVS

为提高化合物筛选效率、发现更多5-羟色胺转运体(serotonin transporter,SERT)抑制剂,本研究选择与中枢神经系统5-羟色胺(5-hydroxytryptamine,5-HT)能神经元具有极高相似性、能分泌5-HT、表达腺苷受体A3(A3AR)的RBL-2H3细胞,采用荧光底物ASP+进行SERT再摄取抑制率检测,建立生物筛选SERT抑制剂的模型。在大规模的化合物库中,基于配体的虚拟筛选技术,采用贝叶斯分类方法与分子指纹相似度检索筛选出待筛化合物;进一步结合生物筛选方法进行验证,找到具有SERT抑制活性的全新结构化合物。研究表明ASP+检测方法有效降低环境生物危害性、检测效率高,而将生物筛选和虚拟筛选两者有机结合明显提高了筛选的效率。

In order to improve the efficiency of drug screening on serotonin transporter（SERT） inhibitors, a high-throughput screening（HTS） model is established in RBL-2H3 cells. The RBL-2H3 cells are very similar to the serotonin genetic neuro, in modulation of post-receptor mechanisms and transduction pathway of SERT reactivated. Depending on a fluorescence substrate ASP＋ used in detection method of inhibitor rates, it＇s convenient, quick, accurate and effective, not making the environmental biohazard compared with radioactive experiments. Furthermore, biological screening model combined with computer aided virtual screening technique describing high-throughput virtual screening（HTVS）. Bayesian classification method and molecular fingerprint similarity were applied to virtual screening technique, for screening compounds in compound library. Some compounds have been found, and then validated further by biological screening model. Combination of HTS and HTVS improves the efficiency of screening SERT inhibitors.