CADD Studies On The Projects Of HMGR, ThyX, MurF And Antioxidant

Computer-aided drug design (CADD) represents the most recent application of computers as tools in the drug design process. As a multidisciplinary research field based on the knowledge of chemistry, biology, physics and computer sciences, CADD has shown its potential advantages, and play more and more important roles in current pharmaceutical industry. In the post-genomic era, CADD is experiencing its most exciting periods. It can abundantly save the expense and time spent on the long-run drug discovery process, and has become effective tools in this area.In this thesis, CADD methods were employed to study four projects, including HMG-CoA reductase inhibitor, thymidylate synthase ThyX inhibitor, D-Ala-D-Ala adding enzyme MurF inhibitor and antioxidant. The thesis was divided into three parts.PART â…  Computer aided HMG-CoA Reductase Inhibitor DesignElevated concentrations of plasma cholesterol were a major risk factor for the development of coronary heart disease. 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGR), the natural target and the rate-limiting enzyme in the cholesterol biosynthetic pathway, was an attractive target in the searching for drugs to reduce plasma cholesterol concentrations. In this thesis, both structure-based and ligand-based drug design methods were used to discover new HMGR inhibitors.First, based on the reported protein crystal structure, HMGR inhibitor virtual screening models were constructed, which were proved to be reliable through reproducing crystal structure and comparison the docking scores with the compound activities. The models were used to screen specs and CNPD molecular databases. The hit compounds will be screened with experimental methods. Besides, active site analysis method was employed to explore the protein active cavity shape and surface properties, which will be useful for the identification of HMGR inhibitor pharmacophore and the optimization of lead compounds.Then, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were employed to construct HMGR inhibitor's 3D-QSAR models. The models was proved to be high-predictive by statistical data (q2>0.6) and accurate prediction of the test set compounds. Based on carefully analysis of protein-inhibitor interactions, also the results of active site analysis and 3D-QSAR, we identified the pharmacophore of HMGR inhibitors. At last, possible approach for HMGR inhibitor design was proposed.PART II Homology and Virtual Screening of Flavin-dependent Thymidylate Synthase TbyX inhibitorThyX is the second kind of thymidylate synthase in addition to ThyA. Their amino acid sequences, structures and catalyst machanism are totally different, though they catalyse the same reaction. ThyX has a wide but sporadic phylogenetica] distribution, particularly in bacterial genomes, thereby providing potential new targets for antimicrobial therapies. We constructed three-dimension protein structure of H. pylori ThyX by homology modeling mothod. Then using the modeled structure, specs and other important molecular databases were screened. 64 compounds in hit list of specs database were purchased and experimentally screened, and three of them were found to possess high binding ability to the target protein. One compound of them could inhibit 56% protein activity at a concentration of 5><10"5M. This was a good beginning for ThyX inhibitor development.PART III QSAR Studies tnMurFlnliibittr and AntloxidaiirslSARQSAR of MurF inhibitor The emergence of antibiotic resistant bacteria constitutes a serious public health threat and lays emphasis on the need to explore new avenues for developing efficacious antibiotics. The MurF enzyme catalyzes the last step in the synthesis of the cytoplasmic precursor of the bacterial cell wall peptidoglycan. Gene knockout studies have shown that MurF and other murein enzymes were essential for the survival of bacterial cell. Those made MurF an attractive target for new antibacterial discovery. Three reliable quantitative structure-activity relationship (QSAR) methods, viz., comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram QSAR (HQSAR), were employed to derive high predictive QSAR models for the designing of novel MurF inhibitors. Different alignment patterns for field-based method (CoMFA and CoMSIA), different fieldcombination for CoMSIA, and different fragment parameters for HQSAR were explored. QSAR models with high predictive ability were successfully constructed, in terms of cross-validation q2 and standard error, which were around 0.70 and 0.55, respectively. Compounds with indeterminate activities were used as test set to assess if the QSAR models could identify them as low-activity compounds. Results showed that CoMSIA had the best predictive-ability and simplicity, followed by HQSAR and CoMFA. All the models from different methods were in good agreement with each other. Based on these models, some key features for designing new MurF inhibitors were identified. Combined HQSAR and 3D field based QSAR methods (CoMFA/CoMSIA), a virtual database screen procedure was proposed, which are much automatic and reasonable. The results here will speed up the discovery of new potential MurF inhibitors.QSAR of antioxidant Antioxidants are of great interest because of their involvement in important biological and industrial process. They have been found to possess anticancer, anti-cardiovascular disease, anti-inflammation and many other activities. It was meaningful to study the quantitative structure-activity relationship (QSAR) of antioxidant and design novel, efficient, low-toxicity antioxidants. In this thesis, the potential of Eigen Values Analysis (EVA) method on the study of antioxidant QSAR was explored by means of four sets of antioxidants. All the EVA models obtained had PLS cross validation regression score (g2cv) large than 0.5 (with one as high as 0.927), showed good predictive power. EVA is alignment independence and has a series of advantages over other 3D QSAR methods. Our studies suggest that EVA is suitable for the study of antioxidant QSAR. This could provide a novel methodology for the QSAR study of antioxidants. Moreover, it was the first time to use EVA method to study the relationship of the structure and antioxidant activity.