Low molecular weight compounds from mushrooms as potential Bcl-2 inhibitors: docking and virtual screening studies

Mushrooms have the ability to promote apoptosis in tumor cell lines, but the mechanism of action is not quite well understood. Inhibition of the interaction between Bcl-2 and pro-apoptotic proteins could be an important step that leads to apoptosis. Therefore, the discovery of compounds with the ability to inhibit Bcl-2 is an ongoing research topic in drug discovery. In this study, we started by analyzing Bcl-2 experimental structures that are currently available in Protein Data Bank database. After analysis of the more relevant Bcl-2 structures, 4 were finally selected. An analysis of the best docking methodology was then performed using a cross-docking and re-docking approach while testing 2 docking softwares: AutoDock 4 and AutoDock Vina. Autodock4 provided the best docking results and was selected to perform a virtual screening study applied to a dataset of 40 Low Molecular Weight (LMW) compounds present in mushrooms, using the selected Bcl-2 structures as target. Results suggest that steroid are the more promising family, among the analyzed compounds, and may have the ability to interact with Bcl-2 and this way promoting tumor apoptosis. The steroids that presented lowest estimated binding energy (ΔG) were: Ganodermanondiol, Cerevisterol, Ganoderic Acid X and Lucidenic Lactone; with estimated ΔG values between -8,45 and -8,23 Kcal/mol. A detailed analysis of the docked conformation of these 4 top ranked LMW compounds was also performed and illustrates a plausible interaction between the 4 top raked steroids and Bcl-2, thus substantiating the accuracy of the predicted docked poses. Therefore, tumoral apoptosis promoted by mushroom might be related to Bcl-2 inhibition mediated by steroid family of compounds.

Evaluation of the conductor-like screening model for real solvents for the prediction of the water activity coefficient at infinite dilution in ionic liquids

Ionic liquids (ILs) have attracted great attention, from both industry and academia, as alternative fluids for very different types of applications. The large number of cations and anions allow a wide range of physical and chemical characteristics to be designed. However, the exhaustive measurement of all these systems is impractical, thus requiring the use of a predictive model for their study. In this work, the predictive capability of the conductor-like screening model for real solvents (COSMO-RS), a model based on unimolecular quantum chemistry calculations, was evaluated for the prediction water activity coefficient at infinite dilution, gamma(infinity)(w), in several classes of ILs. A critical evaluation of the experimental and predicted data using COSMO-RS was carried out. The global average relative deviation was found to be 27.2%, indicating that the model presents a satisfactory prediction ability to estimate gamma(infinity)(w) in a broad range of ILs. The results also showed that the basicity of the ILs anions plays an important role in their interaction with water, and it considerably determines the enthalpic behavior of the binary mixtures composed by Its and water. Concerning the cation effect, it is possible to state that generally gamma(infinity)(w) increases with the cation size, but it is shown that the cation-anion interaction strength is also important and is strongly correlated to the anion ability to interact with water. The results here reported are relevant in the understanding of ILs-water interactions and the impact of the various structural features of its on the gamma(infinity)(w) as these allow the development of guidelines for the choice of the most suitable lLs with enhanced interaction with water.