Citation: LIU Hai-Chun, LU Shuai, RAN Ting, ZHANG Yan-Min, XU Jin-Xing, XIONG Xiao, XU An-Yang, LU Tao, CHEN Ya-Dong. Accurate Activity Predictions of B-Raf Type II Inhibitors via Molecular Docking and QSAR Methods[J]. Acta Physico-Chimica Sinica, ;2015, 31(11): 2191-2206. doi: 10.3866/PKU.WHXB201510134 shu

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

LIU Hai-Chun ¹ ,
LU Shuai ¹ ,
RAN Ting ¹ ,
ZHANG Yan-Min ¹ ,
XU Jin-Xing ¹ ,
XIONG Xiao ¹ ,
XU An-Yang ¹ ,
LU Tao ^1,2,, ,
CHEN Ya-Dong ^1,,

1.
1 School of Basic Science, China Pharmaceutical University, Nanjing 211198, P. R. China;
2.
2 State Key Laboratory of Natural Medcines, China Pharmaceutical University, Nanjing 210009, P. R. China

Corresponding author: LU Tao, CHEN Ya-Dong,
Received Date: 9 July 2015
Available Online: 12 October 2015
Fund Project: 国家自然科学基金(21102181, 81302634)资助项目 (21102181, 81302634)

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