Subject(s)
Antineoplastic Agents/chemistry , Protein Kinase Inhibitors/chemistry , Quinazolines/chemistry , fms-Like Tyrosine Kinase 3/antagonists & inhibitors , Antineoplastic Agents/chemical synthesis , Antineoplastic Agents/pharmacology , Binding Sites , Computer Simulation , Drug Design , Humans , Protein Kinase Inhibitors/chemical synthesis , Protein Kinase Inhibitors/pharmacology , Quinazolines/chemical synthesis , Quinazolines/pharmacology , fms-Like Tyrosine Kinase 3/metabolismABSTRACT
In this study, chemical feature-based 3-dimensional (3D) pharmacophore models of Checkpoint kinase 1 (Chk1) inhibitors were developed based on the known inhibitors of Chk1. The best pharmacophore model Hypo1 was characterized by the best correlation coefficient (0.9577), and the lowest root mean square deviation (0.8871). Hypo1 consists of one hydrogen-bond acceptor, one hydrogen-bond donor, and two hydrophobic features, as well as one excluded volume. This pharmacophore model was further validated by both test set and cross validation methods. A comparison analysis of Hypo1 with chemical features in the active site of Chk1 indicates that the pharmacophore model Hypo1 can correctly reflect the interactions between Chk1 and its ligands. Then Hypo1 was used to screen chemical databases, including Specs and Chinese Nature Product Database (CNPD) for potential lead compounds. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking study to refine the retrieved hits. Finally some of the most potent (estimated) compounds were selected from the final refined hits and suggested for further experimental investigation.
Subject(s)
Enzyme Inhibitors/pharmacology , Models, Chemical , Protein Kinases/chemistry , Protein Kinases/metabolism , Catalytic Domain , Checkpoint Kinase 1 , Combinatorial Chemistry Techniques , Computer Simulation , Enzyme Inhibitors/chemistry , Models, Molecular , Structure-Activity RelationshipABSTRACT
Pharmacophore modeling, including ligand- and structure-based approaches, has become an important tool in drug discovery. However, the ligand-based method often strongly depends on the training set selection, and the structure-based pharmacophore model is usually created based on apo structures or a single protein-ligand complex, which might miss some important information. In this study, multicomplex-based method has been suggested to generate a comprehensive pharmacophore map of cyclin-dependent kinase 2 (CDK2) based on a collection of 124 crystal structures of human CDK2-inhibitor complex. Our multicomplex-based comprehensive pharmacophore map contains almost all the chemical features important for CDK2-inhibitor interactions. A comparison with previously reported ligand-based pharmacophores has revealed that the ligand-based models are just a subset of our comprehensive map. Furthermore, one most-frequent-feature pharmacophore model consisting of the most frequent pharmacophore features was constructed based on the statistical frequency information provided by the comprehensive map. Validations to the most-frequent-feature model show that it can not only successfully discriminate between known CDK2 inhibitors and the molecules of focused inactive dataset, but also is capable of correctly predicting the activities of a wide variety of CDK2 inhibitors in an external active dataset. Obviously, this investigation provides some new ideas about how to develop a multicomplex-based pharmacophore model that can be used in virtual screening to discover novel potential lead compounds.
