基于分子对接和QSAR方法预测B-Raf II型抑制剂活性

doi:10.3866/PKU.WHXB201510134

摘要/Abstract

摘要：

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、LigandFit、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, r_(CV)² = 0.790, r_pre² = 0.864;模型M2: r² = 0.738, r_(CV)² = 0.812, r_pre² = 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.

关键词: B-Raf II型抑制剂, 分子对接, 打分评价函数, 对接能量描述符, 定量构效关系模型

Abstract:

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, LigandFit, 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, r_(CV)² = 0.790, r_pre² = 0.864; M2: r² = 0.738, r_(CV)² = 0.812, r_pre² = 0.8605). The important descriptors were also explored to elucidate the main factors influencing the inhibition activities. The models suggested that the scoring functions (G_Score, -ECD, Dock_Score, and PMF) and docking-generated energy-based descriptors (S(hb_ext), 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.

Key words: B-Raf Type II inhibitor, Molecular docking, Scoring function, Docking-generated energybased descriptor, Quantitative structure-activity relationship model

MSC2000:

O641

刘海春,卢帅,冉挺,张艳敏,徐金星,熊潇,徐安阳,陆涛,陈亚东. 基于分子对接和QSAR方法预测B-Raf II型抑制剂活性[J]. 物理化学学报, 2015, 31(11): 2191-2206.

Hai-Chun. LIU,Shuai. LU,Ting. RAN,Yan-Min. ZHANG,Jin-Xing. XU,Xiao. XIONG,An-Yang. XU,Tao. LU,Ya-Dong. CHEN. Accurate Activity Predictions of B-Raf Type II Inhibitors via Molecular Docking and QSAR Methods[J]. Acta Phys. -Chim. Sin., 2015, 31(11): 2191-2206.

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