Acetylcholinesterase (AChE) inhibitors are the most effective drugs for Alzheimer's disease treatment. However, considering the potential and failure rates of AChE inhibitors, chemical scaffolds targeting cholinesterase specifically are still very limited. Herein, we report a new class of AChE inhibitors identified by employing a virtual screening approach that combines shape similarity with molecular docking calculations. Virtual screening followed by the evaluation of AChE inhibitory activity allowed us to identify 1,2,4-triazolylthioethanones as a novel class of AChE inhibitors. Thirteen compounds with 1,2,4-triazolylthiothanone core and IC50 values in the range of 0.15±0.07 to 3.32±0.92 μM have been reported here. Our findings shed light into a class of AChE inhibitors that could be useful starting point for the development of novel therapeutics to tackle Alzheimer's disease.
Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures.
Consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics. In this paper, consensus clustering is used for combining the clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Two graph-based consensus clustering methods were examined. The Quality Partition Index method (QPI) was used to evaluate the clusterings and the results were compared to the Ward's clustering method. Two homogeneous and heterogeneous subsets DS1-DS2 of MDL Drug Data Report database (MDDR) were used for experiments and represented by two 2D fingerprints. The results, obtained by a combination of multiple runs of an individual clustering and a single run of multiple individual clusterings, showed that graph-based consensus clustering methods can improve the effectiveness of chemical structures clusterings.
Present HIV antiviral therapy only targets structural proteins of HIV, but evidence shows that the targeting of accessory proteins will expand our options in combating HIV. HIV-1 Vpr, a multifunctional accessory protein involved in viral infection, replication and pathogenesis, is a potential target. Previously, we have shown that phenyl coumarin compounds can inhibit the growth arrest activity of Vpr in host cells and predicted that the inhibitors' binding site is a hydrophobic pocket on Vpr. To investigate our prediction of the inhibitors' binding site, we docked the coumarin inhibitors into the predicted hydrophobic binding pocket on a built model of Vpr and observed a linear trend between their calculated binding energies and prior experimentally determined potencies. Subsequently, to analyze the inhibitor-protein binding interactions in detail, we built homology models of Vpr mutants and performed docking studies on these models too. The results revealed that structural changes on the binding pocket that were caused by the mutations affected inhibitor binding. Overall, this study showed that the binding energies of the docked molecules are good indicators of the activity of the inhibitors. Thus, the model can be used in virtual screening to identify other Vpr inhibitors and for designing more potent inhibitors.