Molecular Modeling Study Of Some Kinase Inhibitors

Protein kinases (PK) constitute one of the largest protein families in humans. Their function is to catalyze phosphorylation of serine, threonine, or tyrosine residues, and to regulate the majority of signal transduction pathways in cells. Thus they play important roles in cell growth, metabolism, differentiation, and apoptosis. There are 518 PKs are predicted in the human kinome based on the information from the human genome sequence, approximately 1.7% of all human genes. Deregulation of protein kinases is implicated in a number of diseases including cancer, diabetes, and inflammation. Thus, protein kinases make up the second largest group of pharmaceutically relevant protein targets. It is estimated that approximately one-third of drug discovery programs target protein kinases.Targeted inhibition of protein kinases has thereby become an attractive therapeutic strategy in the treatment of relevant diseases. Current drug discovery efforts typically focus on developing ATP-competitive small molecule protein kinases inhibitors (PKI), mainly selective and multi-targeted inhibitors.As an important technology and tool for drug design, computer-aided drug design (CADD) has been applied to PKI discovery. Molecular modeling approaches, such as quantitative structure-activity relationship (QSAR), molecular docking, pharmacophore, homology modeling, molecular dynamic simulation, and quantum chemistry methods have been applied to PKI design and discovery.This dissertation applied several techniques (QSAR, molecular docking and pharmacophore) to build the correlation between molecular structure features and their bioactivity, and to study the interaction between the targeted protein and their inhibitors. We aim at gaining insights into the key structural and phamacophore features affecting activity, and the interaction mechanism for inhibitor-protein binding, guiding the design, structural modification and activity prediction of PKI to aid the design and synthesize of highly active drugs targeted PK. In Chapter 1, we gave a general introduction of protein kinase, corresponding inhibitors, and molecular modeling methods used in this thesis, such as QSAR, molecular modeling and pharmacophore. The application of computer-aided drug design methods in PKI discovery is also described.In Chapter 2, QSAR study on a series of aminothiazole derivatives as Aurora-A kinase inhibitors was performed. The 2D-QSAR model was built using the genetic algorithm-multiple linear regression (GA-MLR) method. The obtained results indicated that 3D-GETAWAY and WHIM descriptors exhibited significant contributions to the inhibitory activity. The 3D-QSAR models were established by using CoMFA and CoMSIA methods. The obtained 3D contour maps were in accordance with the structural features of these inhibitors and the X-ray structure, which suggested that further modification of compound 22 on the aniline substructure should consider steric, hydrophobic and hydrogen bond properties.In Chapter 3, molecular docking and 3D-QSAR approches were applied to molecular modeling of a series of Rho kinase inhibitors. Docking studies were performed to obtain the active conformations for the whole dataset and normal bingding mode. The CoMFA and CoMSIA analyses gave some insights into the key structural factors affecting the bioactivity of these inhibitors. The obtained 3D contour maps along with the docking results suggested that the 1H-indazole derivatives may be superior to isoquinoline derivatives and highlight that 1) electron-donating substituents on 4-position of phenyl and 2) bulky and hydrophobic groups in the P region of the binding pocket increase potency. According to the results of our molecular modeling study, we designed a series of 1H-indazole derivatives that are possible to have high Rho kinase inhibitions.In Chapter 4, molecular modeling studies on a series of EphB4 kinase inhibitors were performed. Molecular docking results show that these inhibitors form four hydrogen bonds with binding pocket:pyridyl-N formed a hydrogen bond with Met696 residue of the hinge region, urea C=O and NH formed three hydrogen bonds with Lys647 and Asp758 residues. Pharmacophore model presented the most important pharmacophore features and their distributions. CoMFA and CoMSIA analyses gave some insights into the key structural factors affecting bioactivity. The obtained 3D contour maps can help further design and structural modification of these inhibitors.In Chapter 5, molecular docking, pharmacophore and 3D-QSAR studies are performed on a series of V600EB-RAF kinase inhibitors. Molecular docking explored the binding mode between ligands and receptor. Pharmacophore model presented five most important pharmacophore features:two hydrogen bond receptor, one hydrogen bond donor, one hydrophobic feature and one aromatic ring. CoMFA and CoMSIA analyses based on docked conformers obtained optimal predictivity and disclosed the key structural factors affecting bioactivity.In Chapter 6, a series of MK-2 inhibitors were analyzed using molecular modeling methods, including molecular docking and 2D-QSAR study. The docking results showed that these compounds can bind in ATP binding pocket by forming four hygrogen bonds.2D-QSAR study was used to analyze the critical factors influencing the inhibitory activity. The obtained results showed that the number of aromatic ratio, hydrogen bond properties, topological information and NH groups could greatly affect the activity. Our study can provide guidance for inhibitors design and modification.In Chapter 7, we analyzed a series of inhibitors of AP-1 and NF-κB mediated transcriptional activation using CoMFA and CoMSIA methods. The influence of steric, electrostatic, hydrogen bond donor and hydrogen bond acceptor field around molecules were investigated. The obtained models can be used for activity prediction of newly designed inhibitors and suggested that further structural modification should consider steric, electrostatic and hydrogen bond donor properties.