Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors
Abstract
Xian-qiang SUN1, 2, #, Lei CHEN1, #, Yao-zong LI1, 3, Wei-hua LI1, Gui-xia LIU1, Yao-quan TU2, *, Yun TANG1, *
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China; 2Division of Theoretical Chemistry and Biology, School of Biotechnology, KTH Royal Institute of Technology, S-106 91 Stockholm, Sweden; 3Department of Chemistry, Umeå University, S-90187 Umeå, Sweden
Aim: To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors.
Methods: One hundred thirty nine EGFR TK inhibitors were classified into 3 clusters. Ensemble docking of these inhibitors with 19 EGFR TK crystal structures was performed. Three protein structures that showed the best recognition of each cluster were selected based on the docking results. Then, a novel QSAR (ensemble-QSAR) building method was developed based on the ligand conformations determined by the corresponding protein structures.
Results: Compared with the 3D-QSAR model, in which the ligand conformations were determined by a single protein structure, ensemble-QSAR exhibited higher R2 (0.87) and Q2 (0.78) values and thus appeared to be a more reliable and better predictive model. Ensemble-QSAR was also able to more accurately describe the interactions between the target and the ligands.
Conclusion: The novel ensemble-QSAR model built in this study outperforms the traditional 3D-QSAR model in rationality, and provides a good example of selecting suitable protein structures for docking prediction and for building structure-based QSAR using available protein structures.
Keywords: epidermal growth factor receptor; tyrosine kinase; ensemble docking; ensemble-QSAR; drug design
This work was supported by the National Natural Science Foundation of China (Grant 21072059), the 863 Project (Grant 2012AA020308), the Fundamental Research Funds for the Central Universities (WY1113007) and the Shanghai Committee of Science and Technology (11DZ2260600). Xianqiang Sun would like to thank China Scholarship Council for supporting his study in Sweden.
# These authors contributed equally to this work.
* To whom correspondence should be addressed.
E-mail tu@theochem.kth.se (Yao-quan TU); ytang234@ecust.edu.cn (Yun TANG).
Received 2013-07-03 Accepted 2013-09-10
Keywords:
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China; 2Division of Theoretical Chemistry and Biology, School of Biotechnology, KTH Royal Institute of Technology, S-106 91 Stockholm, Sweden; 3Department of Chemistry, Umeå University, S-90187 Umeå, Sweden
Aim: To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors.
Methods: One hundred thirty nine EGFR TK inhibitors were classified into 3 clusters. Ensemble docking of these inhibitors with 19 EGFR TK crystal structures was performed. Three protein structures that showed the best recognition of each cluster were selected based on the docking results. Then, a novel QSAR (ensemble-QSAR) building method was developed based on the ligand conformations determined by the corresponding protein structures.
Results: Compared with the 3D-QSAR model, in which the ligand conformations were determined by a single protein structure, ensemble-QSAR exhibited higher R2 (0.87) and Q2 (0.78) values and thus appeared to be a more reliable and better predictive model. Ensemble-QSAR was also able to more accurately describe the interactions between the target and the ligands.
Conclusion: The novel ensemble-QSAR model built in this study outperforms the traditional 3D-QSAR model in rationality, and provides a good example of selecting suitable protein structures for docking prediction and for building structure-based QSAR using available protein structures.
Keywords: epidermal growth factor receptor; tyrosine kinase; ensemble docking; ensemble-QSAR; drug design
This work was supported by the National Natural Science Foundation of China (Grant 21072059), the 863 Project (Grant 2012AA020308), the Fundamental Research Funds for the Central Universities (WY1113007) and the Shanghai Committee of Science and Technology (11DZ2260600). Xianqiang Sun would like to thank China Scholarship Council for supporting his study in Sweden.
# These authors contributed equally to this work.
* To whom correspondence should be addressed.
E-mail tu@theochem.kth.se (Yao-quan TU); ytang234@ecust.edu.cn (Yun TANG).
Received 2013-07-03 Accepted 2013-09-10