Three Dimensions Quantitative Structure-activity Relationships And Molecular Docking Studies On Inhibitors Of Enzymes

The applications of computer-aided drug design (CADD) have shown its potential advantages in saving the expense and time in the long run drug discovery process. Now CADD plays more and more important roles in current pharmaceutical industry. Two 3D-QSAR methodologies, CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis), and molecular docking were commonly used in CADD and have been demonstrated to be successful. Besides the review of the methodologies of 3D-QSAR and molecular docking, we also introduced the developments and the principles of some approaches of CADD (e.g. conformation analysis, molecular alignment) which would be used in our studies.In this paper, CADD methodologies were employed to study several important enzymes and the corresponding inhibitors which were selected for different sakes. The followings are the abstract of the studies:1. Separate CoMFA and CoMSIA models were developed from our data set for the KDR, c-Kit and Tie-2 inhibitors based on Thieno/furopyrimidine scaffold. These models showed excellent internal predictability and consistency, and using test-set compounds yielded a good predictive power for pIC50 value. For targets belonging to the same family of receptors, inhibitors often act at more than one biological target and produce a synergistic effect. This would raise the problem of selectivity in receptor-ligand binding. Special efforts to design multiple activating drugs can be successfully made using computational methods to support biochemical studies and to design selective ligands for receptor subtypes. So we selected three kinases of the receptor tyrosine kinase (RTK) family to perform 3D-QSAR studies in order to design high selectivity inhibitors. The field contour maps (CoMFA and CoMSIA) corresponding to the KDR, c-Kit and Tie-2 kinase subtypes reflected the characteristic similarities and differences between these types. These maps provided valuable information to facilitate structural modifications of the inhibitor to increase selectivity for the KDR over c-Kit and Tie-2.2. Usually, there are several binding pockets in the same receptor and the corresponding non-competitive inhibitors (NNIs) would show different activities. In order to find the best class of the NNIs, some efforts must be achieved. Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies in urgent demand. The non-structural protein 5B (NS5B) has emerged as an attractive target for drug discovery efforts toward antiviral for HCV. Besides the active site, three distinct allosteric binding sites of NS5B polymerase and the corresponding non-nucleoside inhibitors have been reported.1) By using CoMFA and CoMSIA methods, we firstly built 3D-QSAR models with more than two hundred benzimidazole/indole derivative inhibitors. These studies indicated that our approach yielded statistically significant and high predictabilities of the QSAR models. Docking simulations were conducted on the series of potent inhibitors. The docking simulations gave deep insights into binding interactions between the inhibitors and the residues in one allosteric site of the polymerase. The flexible docking method positioned all of the inhibitors into the allosteric site to determine the probable binding conformation. The molecular interaction fields were also abtained to supply more information for rational drug design.2) Toward the same target several series of benzo-1, 2, 4- thiadazine based NS5B inhibitors that showed activities in the replicon assay have been reported. A diverse set of benzothiadiazine derivation inhibitors that were aligned in two different ways were subjected to QSAR analysis. Two CoMFA models and two CoMSIA models were built and yielded statistically significant and high predictabilities. A series of pharmacophore models were generated by the GASP method and offered the hydrophobic, hydrogen donor and acceptor phormacophores. As the complementary of the QSAR models, the pharmacophore models supplied more information for the rational design of new drugs.