Current Computer - Aided Drug Design - Volume 13, Issue 3, 2017

Background: Econazole, sulconazole and tioconazole usage as antifungal agents is limited due to poor pharmacokinetic properties. Pristine and hydroxylated structures of the C240 fullerene and single walled carbon nanotube (SWCNT) were proposed as transporters of these imidazoles potentially enhancing their pharmacokinetics. Methods: To assess possibility of creation of the endohedral complexes of the azoles and carbon nanostructures, their adsorption and interaction energies were calculated with the hybrid exchange-correlation density functional B97-1 and 6-31(d,p) basis set. Interactions within the transporter - drug complexes were investigated with the Atoms in Molecules (AIM) Theory and Reduced Density Gradient (RDG). Results and Conclusions: The adsorption energies of the studied azoles depend on type and surface modification of the transporter. Hydroxylation of the fullerene and nanotube surface makes an opportunity for chemisorption of the investigated antifungal drugs. The pristine and hydroxylated nanotube complexes exhibit thermodynamic stability. The complexes of the fullerenes are thermodynamically unstable but its kinetic stability could be significant thus allowing for the such structures to exist. The energetic instability would enhance liberation of the encapsulated molecule from the complex. It is advantageous in the context of drug release.

Background: Kinase domain of VEGFR-2 displays conformational flexibility which leads to existence of two kinds of inhibitors viz. type I and type II inhibitors. They exhibit different binding modes and this necessitates the development of separate pharmacophore models for them. Methods: The virtual screening study for discovery of type I inhibitors of VEGFR-2 kinase was done by using combined pharmacophore (generated using PHASE and validated by 3D-QSAR) and docking (Glide) based approach. Validated pharmacophore was used as preliminary filter followed by docking. ADME properties were predicted for retrieved hits using QikProp. Results: ADHRR.94 with statistical parameters r2 test 0.94, r2 training 0.99, SD 0.0766, r2 0.9861, F 283.3, RMSE 0.2605, q2 0.8115 and Pearson's R 0.9723was identified as the best pharmacophore hypothesis for type I inhibitors of VEGFR-2 kinase. Virtual screening study was done for Asinex Elite Libraries comprising of 104400 molecules using ADHRR.94, HTVS docking and XP docking that resulted in twelve hits. Asinex ligand 5686 with docking score of -10.48kcal/mol was top-ranking hit. It made two hydrogen bonding interactions with Cys 919, one as an acceptor and other as a donor, which are characteristic of type I inhibitors. Additional interactions observed were π-cation with Lys 868 and π-πstacking with Phe 1047.Twelve hits had acceptable values for ADME properties. Conclusion: Twelve hits with best obtained docking scores ranging from -10.48 to -7.23 kcal/mol and mimicking characteristic type I inhibitor interactions were identified which could be probable inhibitors of VEGFR-2.

Background: Isoindolo[2,1-a]quinoxalines constitute an important class of compounds which demonstrated potent antiproliferative activity against different human tumor cell lines and topoisomerase I inhibitors. In particular, their water soluble imine or iminium salts recently synthesized showed potent growth inhibitory effect on NCI-60 tumor cell line panel and biological studies performed on the most active compounds demonstrated that they cause DNA damage via topoisomerase I poisoning. Objective: Herein, we investigate with molecular modeling methods, the common features responsible for topoisomerase I inhibition of the water-soluble isoindolo[2,1-a]quinoxalin-6-imines, by comparing them with known inhibitors. Methods: Different X-ray crystallographic structures with co-crystallized inhibitors were investigated and their binding modes were analyzed. The structures of the inhibitors were also compared through a pharmacophore analysis. As a validation of our docking method, the co-crystallized inhibitors were re-docked. Conclusion: Our docking studies performed on Isoindolo[2,1-a]quinoxalines and other inhibitors revealed very important common features responsible for topoisomerase I inhibition that can improve the design of new inhibitors.

Background: Adenosine-Monophosphate-Activated protein kinase (AMPK) is a conserved kinase that plays an important role in maintaining the homeostasis of cells. AMPK activation has a positive impact on treatment of diseases such as diabetes, obesity and cancer as well. This observation led to the development of AMPK activators. Certain naturally occurring compounds have also been known to activate AMPK. Methods: In this study, we retrieved the AMPK activators that include chemical drugs, xenobiotics and natural compounds and analyzed their interactions with AMPK via docking studies. Using this ligand dataset, a pharmacophore model was generated based upon ligand-based pharmacophore modeling strategy. The generated pharmacophore model was used to screen a library of ZINC database. The new hits which share the properties of our pharmacophore model were further analyzed via docking studies. Results: This study led to the identification of new chemical compounds which has the potential to activate AMPK. Even some of the screened hits showed better binding energies as compared to that of the ligand dataset used thus having the potential to activate AMPK more efficiently. The promising hits obtained after virtual screening of ZINC database were also checked against the Lipinski’s rule of five. Conclusion: Compound 7 out of the 10 compounds showed best binding energies even more efficient than the ligand dataset itself.

Background: Top five best hit compounds (ZINC59376795, ZINC60175365, ZINC36922620, ZINC39550705 and ZINC36953975) were obtained through our high throughput virtual screening (HTVS) analysis with resistant 5204-PBP2B (5204 Penicillin Binding Protein 2B) and sensitive R6-PBP2B (R6 Penicillin Binding Protein 2B) proteins of Streptococcus pneumoniae. Objective: To gain insight in molecular docking and dynamics simulations of these top five best hit compounds with both resistant 5204-PBP2B and sensitive R6-PBP2B targets. Methods: We have employed Glide XP docking and molecular dynamics simulations of these five best hit compounds with 5204-PBP2B and R6-PBP2B targets. The stability analysis has been carried out through DFT, prime-MM/GBSA binding free energy, RMSD, RMSF and Principal Component Analysis. Results: The reference drug, penicillin G forms stable complex with sensitive R6-PBP2B protein. Similar stability is observed for the mutant resistant 5204-PBP2B with the top scoring compound ZINC592376795 which implies that this compound may act as an effective potential inhibitor. The compound ZINC59376795 forms a total of five hydrogen bonds with resistant 5204-PBP2B protein of which three are with mutated residues. Similarly, the other four compounds including penicillin G also form hydrogen bonds with mutated residue. The MD simulations and stability analysis of the complexes of wild and mutant forms are evaluated for a trajectory period of 16ns and further MD simulations of ZINC59376795 with resistant 5204-PBP2B and sensitive R6-PBP2B confirmed the stability for 50 ns. Conclusion: These results suggest that the top five best hit compounds are found to be a promising gateway for the further development of anti-pneumococcal therapeutics.

Background: Quantitative structure–activity relationship (QSAR) models could provide both statistical significance and useful chemical insights for drug design. The QSAR method has found applications for predicting diverse properties of organic compounds, including antiviral activities, toxicities and biological activities. In this work, a quantitative structure-activity relationship was utilized for the prediction of allosteric BRAF (V600E) inhibitory activities. Methods: A data set which contains 54 molecules was classified into training and test sets. Stepwise (SW) and genetic algorithm (GA) methods were employed for feature selection. The models were validated using the cross-validation and external test set. Results: Results showed that the GA approach is a more powerful technique than SW for the selection of suitable descriptors. The squared cross-validated correlation coefficient for leave-one-out of 0.702 and squared correlation coefficient of 0.793 was obtained for the training set compounds by GA–MLR model. Conclusion: The obtained GA–MLR model could be applied as a worthwhile model for designing similar groups of the mentioned inhibitors.