Next, the transcriptional response on several GR-regulated genes was investigated

etails on the level of accuracy of docking experiments since our focus is to employ FEB values predicted from cross-docking experiments and to analyze them to identify optimal partitioning solutions from the clustering methods used. Redocking experiments were performed to take the input docking parameters for cross-docking experiments. The statistical analysis, i.e. average and standard deviation, represents FEB variations predicted by AutoDock4 along the production phase of the MD trajectory against each of the 20 ligands. From Table 2, we can concluded that, except for GEQ ligand, the variation of the FEB values in the cross-docking experiments was less than 0.9 kcal/mol in 68% of the MD conformations, concentrating a large quantity of conformations closely to the average FEB values. Clustering analyses on data sets from the MD trajectory This section reports and compares the results obtained for clustering three different data sets Oleandrin chemical information pubmed ID:http://www.ncbi.nlm.nih.gov/pubmed/19667396 from structural information of the FFR model. We applied the six clustering algorithms described in the Materials PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19667359 and Methods section. In this regard, we first executed the clustering algorithms for Cavity Attributes, Cavity RMSD, and Protein RMSD data sets varying the number of clusters from 10 to 200, and afterward we extracted the medoids from every generated partitioning. Solutions were evaluated based on statistical assessments in the predicted FEB values. We decided to start the clustering analyses from 10 since low k values shows poor level of scatter and, consequently are unable to reflect all possible movements of a 20 ns MD trajectory. In opposition, high numbers of clusters tend to represent better dispersion but we limit the cluster ranges up to 1% of all MD conformations since