The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors.

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Three consensus 3D-QSAR (c-3D-QSAR) models were built for 38, 34, and 78 inhibitors of β-secretase, histone deacetylase, and farnesyltransferase, respectively. To build an individual 3D-QSAR model, the structures of an inhibitor series are aligned through docking of a protein receptor into the active site using the program GOLD. CoMFA, CoMSIA, and Catalyst are then performed for the training set of each structurally aligned inhibitor series to obtain a 3D-QSAR model. Since the consensus in features identified is high for the same pharmacophore features selected for building a 3D-QSAR model by a 3D-QSAR method, a c-3D-QSAR model for each inhibitor series is constructed by combining the pharmacophore features selected for building the 3D-QSAR model using the SYBYL spread sheet and PLS module. Each c-3D-QSAR pharmacophore model built was examined visually and compared with that obtained by simultaneous mapping of the corresponding 3D-QSAR pharmacophores built onto a selected inhibitor structure. It was found that the c-3D-QSAR model built for an inhibitor series improves not only the overall prediction statistics for both training and test sets but also the prediction accuracy for some less active inhibitors of the series.

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BackgroundThe protein structures of the disease-associated proteins are important for proceeding with the structure-based drug design to against a particular disease. Up until now, proteins structures are usually searched through a PDB id or some sequence information. However, in the HDAPD database presented here the protein structure of a disease-associated protein can be directly searched through the associated disease name keyed in.DescriptionThe search in HDAPD can be easily initiated by keying some key words of a disease, protein name, protein type, or PDB id. The protein sequence can be presented in FASTA format and directly copied for a BLAST search. HDAPD is also interfaced with Jmol so that users can observe and operate a protein structure with Jmol. The gene ontological data such as cellular components, molecular functions, and biological processes are provided once a hyperlink to Gene Ontology (GO) is clicked. Further, HDAPD provides a link to the KEGG map such that where the protein is placed and its relationship with other proteins in a metabolic pathway can be found from the map. The latest literatures namely titles, journals, authors, and abstracts searched from PubMed for the protein are also presented as a length controllable list.ConclusionsSince the HDAPD data content can be routinely updated through a PHP-MySQL web page built, the new database presented is useful for searching the structures for some disease-associated proteins that may play important roles in the disease developing process for performing the structure-based drug design to against the diseases.