基于分子对接和QSAR方法预测B-Raf Ⅱ型抑制剂活性

Accurate Activity Predictions of B-Raf Type Ⅱ Inhibitors via Molecular Docking and QSAR Methods

B-Raf激酶在促分裂素原活化蛋白激酶(MAPK)信号转导通路中起着重要作用,已被确定为癌症治疗非常有吸引力的靶标.新型高效B-Raf抑制剂的开发成为癌症治疗的一个热门研究领域.本文以结构多样的B-Raf II型抑制剂为研究对象,联合应用分子对接和定量构效关系(QSAR)模型研究其定量构效关系去探讨抑制活性的起源.两个主题作为研究重点:生物活性构象和描述符.首先对分子对接方法(Glide、Gold、Ligand Fit、Cdocker和Libdock)进行准确性评价,后将研究的对象分子对接到B-Raf活性位点并获得生物活性构象.基于准确的对接结果,计算得到16个打分评价函数和21个能量描述符,以此构建定量构效关系模型.QSAR结果表明模型具有高度精确的拟合和强的预测能力(模型M1:r2=0.852,=0.790,=0.864;模型M2:r2=0.738,=0.812,=0.8605).同时探讨了对抑制活性有重要影响的描述符,结果表明打分评价函数(G_Score,-ECD,Dock_Score,PMF)与能量描述符(S(hb_ext),DE(int),Emodel)对抑制活性影响非常大.通过虚拟筛选和QSAR模型理论预测,一些新的具有潜在抑制活性的化合物作为B-Raf II型抑制剂被获得.上述信息对于进一步设计新颖高效的B-Raf II型抑制剂提供了有用的指导.

B-Raf kinase plays an important role in the mitogen-activated protein kinase（MAPK） signaling transmission pathway and has been identified as an attractive target for cancer therapy. The exploitation of novel and efficient B-Raf inhibitors has become a hot research topic. In this study, we investigated quantitative structure–activity relationship（QSAR） to probe the origins of the inhibitory activities of B-Raf Type II inhibitors.We used structurally diverse B-Raf Type II inhibitors and an integrated docking and QSAR extended method.We focused mainly on two themes： bioactive conformations and descriptors. First, various molecular docking methods（Glide, Gold, Ligand Fit, Cdocker, and Libdock） were evaluated, and then all molecules were docked into the B-Raf active site to obtain the bioactive conformations. Secondly, based on the docking results, 16 scoring functions and 21 docking-generated energy-based descriptors were calculated to construct regression models. The results gave highly accurate fitting and had strong predictive abilities（M1： r2 = 0.852, =0.790, = 0.864; M2： r2 = 0.738, = 0.812, = 0.8605）. The important descriptors were also explored to elucidate the main factors influencing the inhibition activities. The models suggested that the scoring functions（GScore,-ECD, DockScore, and PMF） and docking-generated energy-based descriptors（S（hbext）, DE（int）, and Emodel） were significant. Some new compounds that are potential B-Raf inhibitors were obtained through virtual screening and theoretical predictions using the established models. Such information is useful in guiding the design of novel and robust B-Raf Type II inhibitors.