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Protein Modeling in Rational Drug Design ; Case Study with Deoxyxylulose Phosphate Reductoisomerase Enzyme

其他書名	Delineating a Powerful Virtual Screening Approach for GPCR's ; Application to Salvinorin A, a Selective Kappa Opioid Receptor Agonist
出版	University of Mississippi, 2007
URL	http://books.google.com.hk/books?id=nBvUPgAACAAJ&hl=&source=gbs_api

註釋Part A.A mevalonate-independent pathway for isoprenoid biosynthesis offers the potential for the development of novel antibacterial, antimalarial and herbicidal compounds. This pathway includes 1-deoxy- D-xylulose-5-phosphate (DXP) as a key metabolite. Interestingly, several pathogenic bacteria and the malarial parasite rely exclusively on this new pathway; whereas mammals utilize the mevalonate pathway for isoprenoid synthesis. Hence, all enzymes of the DXP pathway are attractive targets for the development of new antibiotics and antimalarials. We focused on the second enzyme of the pathway, DXP reductoisomerase. In the absence of crystal structure for this enzyme, we built homology models of the Plasmodium falciparum and Mycobacterium tuberculosis DXP reductoisomerase enzyme that will serve as templates to facilitate screening of large chemical databases or to conduct de novo design of potential broad spectrum antibiotic, antimalarials and herbicidal agents. Part B. Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we developed a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the Kappa opioid receptor (KOR) employing a combined approach. Utilizing a set of salvinorin A derivatives, a structurally unique KOR agonist, a pharmacophore model was developed. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based 'agonist-bound' hKOR model was also generated. The model provided more accurate information about the putative binding site of salvinorin A-like ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOR. The predictive ability of the model was evaluated by docking a set of known KOR agonists into the active site. The docked scores correlated reasonably well with experimental p Ki values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.