Histone modifications through acetylation are fundamental for remodelling chromatin and consequently activating gene expression. The imbalance between acetylation and deacetylation activity causes transcriptional dysregulation associated with several disorders. Flavones, small molecules of plant origin, are known to interfere with class I histone deacetylase (HDAC) enzymes and to enhance acetylation, restoring cell homeostasis. To investigate the possible physical interactions of flavones on human HDAC1 and 2, we carried out in silico molecular docking simulations. Our data have revealed how flavone, and other two flavones previously investigated, i.e., apigenin and luteolin, can interact as ligands with HDAC1 and 2 at the active site binding pocket. Regulation of HDAC activity by dietary flavones could have important implications in developing epigenetic therapy to regulate the cell gene expression.
We investigated the potential role of apple phenolic compounds in human pathologies by integrating chemical characterization of phenolic compounds in three apple varieties, computational approaches to identify potential protein targets of the compounds, bioinformatics analyses on data from public archive of gene expression data, and functional analyses to hypothesize the effects of the selected compounds in molecular pathways. Starting by the analytic characterization of phenolic compounds in three apple varieties, i.e. Annurca, Red Delicious, and Golden Delicious, we used computational approaches to verify by reverse docking the potential protein targets of the identified compounds. Direct docking validation of the potential protein-ligand interactions has generated a short list of human proteins potentially bound by the apple phenolic compounds. By considering the known chemo-preventive role of apple antioxidants’ extracts against some human pathologies, we performed a functional analysis by comparison with experimental gene expression data and interaction networks, obtained from public repositories. The results suggest the hypothesis that chemo-preventive effects of apple extracts in human pathologies, in particular for colorectal cancer, may be the interference with the activity of nucleotide metabolism and methylation enzymes, similarly to some classes of anticancer drugs.
Background Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. Results The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. Conclusions We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.
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