Ma Ying, Jin Yuan-Yuan, Wang Ye-Liu, Wang Run-Ling, Lu Xin-Hua, Kong De-Xin, Xu Wei-Ren
Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China.
Chem Biol Drug Des. 2014 Jun;83(6):697-709. doi: 10.1111/cbdd.12283.
Given the special role of insulin and leptin signaling in various biological responses, protein-tyrosine phosphatase-1B (PTP1B) was regarded as a novel therapeutic target for treating type 2 diabetes and obesity. However, owing to the highly conserved (sequence identity of about 74%) in active pocket, targeting PTP1B for drug discovery is a great challenge. In this study, we employed the software package Discovery Studio to develop 3D QSAR pharmacophore models for PTP1B and TCPTP inhibitors. It was further validated by three methods (cost analysis, test set prediction, and Fisher's test) to show that the models can be used to predict the biological activities of compounds without costly and time-consuming synthesis. The criteria for virtual screening were also validated by testing the selective PTP1B inhibitors. Virtual screening experiments and subsequent in vitro evaluation of promising hits revealed a novel and selective inhibitor of PTP1B over TCPTP. After that, a most likely binding mode was proposed. Thus, the findings reported here may provide a new strategy in discovering selective PTP1B inhibitors.
鉴于胰岛素和瘦素信号在各种生物学反应中的特殊作用,蛋白酪氨酸磷酸酶-1B(PTP1B)被视为治疗2型糖尿病和肥胖症的新型治疗靶点。然而,由于活性口袋中的高度保守性(序列同一性约为74%),针对PTP1B进行药物研发是一项巨大挑战。在本研究中,我们使用Discovery Studio软件包为PTP1B和TCPTP抑制剂开发3D QSAR药效团模型。通过三种方法(成本分析、测试集预测和Fisher检验)进一步验证,结果表明这些模型可用于预测化合物的生物活性,而无需进行昂贵且耗时的合成。通过测试选择性PTP1B抑制剂,虚拟筛选标准也得到了验证。虚拟筛选实验以及随后对有前景的命中化合物的体外评估揭示了一种新型的、对PTP1B具有选择性而非TCPTP的抑制剂。之后,提出了一种最可能的结合模式。因此,本文报道的研究结果可能为发现选择性PTP1B抑制剂提供一种新策略。