Ontology highlight
ABSTRACT: 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.
SUBMITTER: Sun XQ
PROVIDER: S-EPMC4076596 | biostudies-literature | 2014 Feb
REPOSITORIES: biostudies-literature
Acta pharmacologica Sinica 20131216 2
<h4>Aim</h4>To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors.<h4>Methods</h4>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 devel ...[more]