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Acta Physico-Chimica Sinca  2015, Vol. 31 Issue (11): 2191-2206    DOI: 10.3866/PKU.WHXB201510134
BIOPHYSICAL CHEMISTRY     
Accurate Activity Predictions of B-Raf Type II Inhibitors via Molecular Docking and QSAR Methods
Hai-Chun. LIU1,Shuai. LU1,Ting. RAN1,Yan-Min. ZHANG1,Jin-Xing. XU1,Xiao. XIONG1,An-Yang. XU1,Tao. LU1,2,*(),Ya-Dong. CHEN1,*()
1 School of Basic Science, China Pharmaceutical University, Nanjing 211198, P. R. China
2 State Key Laboratory of Natural Medcines, China Pharmaceutical University, Nanjing 210009, P. R. China
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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, rpre2 = 0.864; M2: r2 = 0.738, r(CV)2 = 0.812, rpre2 = 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 wordsB-Raf Type II inhibitor      Molecular docking      Scoring function      Docking-generated energybased descriptor      Quantitative structure-activity relationship model     
Received: 09 July 2015      Published: 13 October 2015
MSC2000:  O641  
Fund:  the National Natural Science Foundation of China(21102181);the National Natural Science Foundation of China(81302634)
Corresponding Authors: Tao. LU,Ya-Dong. CHEN     E-mail: lutao@cpu.edu.cn;ydchen@cpu.edu.cn
Cite this article:

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. Acta Physico-Chimica Sinca, 2015, 31(11): 2191-2206.

URL:

http://www.whxb.pku.edu.cn/10.3866/PKU.WHXB201510134     OR     http://www.whxb.pku.edu.cn/Y2015/V31/I11/2191

Fig 1 Structures of the approved B-Raf kinase inhibitors
Table 1 Molecular structures, actual activities vs predicted activities (Pre.) and residuals (Res.) of QSAR models (M1 and M2) for the training set and the test set of B-Raf inhibitors
Fig 2 Chemical space covered by training set molecules and test set molecules (blue spheres)
Docking method Scoring function Docking descriptors
Glide G_Score Lipo, Hbond, Rewards, EvdW, Ecoul, Erotb, Esite, Emodel, Energy, Einternal, LE
Gold Gold_Score S(hb_ext), S(vdw_ext), S(tor_ int), S(vdw _int)
Chem_Score DG, S(hbond), S(lipo), H(rot), DE(clash), DE(int)
Cdocker -ECD  
LigandFit Dock_Score, LigScore-1, LigScore-2, PLP*1, PLP2, Jain, PMF*, PMF04, Ludi1, Ludi2, Ludi3  
LibDock Libdockscore  
*G_Score: glide_score; -ECD: -Cdocker interaction energy; PLP: piecewise linear potential; PMF: potential of mean force; Lipo: lipophilic contact plus phobic attractive term in the GlideScore; Hbond: hydrogen-bonding term in the GlideScore; Rewards: various reward or penalty terms; Evdw: van der Waals energy; Ecoul: Coulomb energy; Erotb: penalty for freezing rotatable bonds in the GlideScore; Esite: term in the GlideScore for polar interactions in the active site; Emodel: model energy; Einternal: internal torsional energy; LE: ligand-efficiency; S(hb_ext): protein-ligand hydrogen bond score; S(vdw_ext): protein-ligand van der Waals score; S(vdw_int): score from intermolecular strain in the ligand; DG: estimated binding free energy; S(lipo): lipophilic contributions; H(rot): rotatable bond contribution; DE(clash): vdW overlap; DE(int): internal strain
Table 2 Molecular docking methods, scoring functions, and the various descriptors used for generating models
Conformation RMSD/nm
Glide Gold Cdocker LigandFit LibDock
1 0.0535 0.0893 0.0466 0.0358 0.0302
2 0.0526 0.0800 0.0487 0.0346 0.0292
3 0.0669 0.0695 0.2154 0.0653 0.0863
Table 3 Root mean square deviation (RMSD) values of Top3 configurations obtained through docking for docking reliability validated
Fig 3 Superimposition of co-crystallized Sorafenib (green) and redocked Top1 conformation by multiple docking (the other colors) in the active site of B-Raf Hinge region: magenta; DFG: blue; colors on web version
Fig 4 Schematic diagram of interaction patterns of B-Raf kinase inhibitor D represents a hydrogen bond donor (red ball); A represents a hydrogen bond acceptor (green ball), Hydro represents hydrophobic character (blue ball) (colors on web version).
Scoring function r2 Descriptor of glide function r2 Descriptor of gold function r2
G_Score 0.622 Lipo 0.200 S(hb_ext) 0.106
Gold_Score 0.274 Hbond 0.216 S(vdw_ext) 0.119
Chem_Score 0.307 Rewards 0.261 S(tor_ int) 0.009
-ECD 0.588 EvdW 0.179 S(vdw_int) 0.216
Dock_Score 0.596 ECoul 0.068    
LigScore-1 0.520 Erotb 0.086 DG 0.169
LigScore-2 0.577 Esite 0.139 S(hbond) 0.015
PLP1 0.498 Emodel 0.471 S(lipo) 0.103
PLP2 0.476 ENERGY 0.272 H(rot) 0.002
Jain 0.249 EInternal 0.150 DE(clash) 0.062
PMF 0.475 LE 0.239 DE(int) 0.046
PMF04 0.433        
Ludi1 0.284        
Ludi2 0.316        
Ludi3 0.471        
Libdockscore 0.027        
r2: correlation coefficient
Table 4 Correlations of individual scoring function and descriptors with the experimental pIC50 values
Model Equation S F r r2 $r_{({\rm{CV)}}}^2$ $r_{({\rm{pre)}}}^2$
M1 Y = –0.151G_Score + 0.044-ECD + 0.026Dock_Score + 0.009PMF –1.084 0.335 35.850 0.909 0.852 0.790 0.864
M2 Y = 0.112S(hb_ext) – 0.365DE(int) – 0.042Emodel + 3.189 0.463 31.812 0.859 0.738 0.812 0.861
S: standard deviation; CV: cross-validation F: Fisher test value
Table 5 QSAR modeS M1, M2 and the corresponding statistics
Fig 5 Actual activity vs predicted activity using QSAR model M1 based on scoring functions (a) training set, (b) test set
Fig 6 Actual activity vs predicted activity using QSAR model M2 based on scoring functions (a) training set, (b) test set
Table 6 Chemical structures and predicted activity values of the hit compounds
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