In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators

Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-β-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future.

SARS-CoV-2 papain-like protease PLpro in complex with natural compounds reveal allosteric sites for antiviral drug design

SARS-CoV-2 papain-like protease (PLpro) covers multiple functions. Beside the cysteine-protease activity, PLpro has the additional and vital function of removing ubiquitin and ISG15 (Interferon-stimulated gene 15) from host-cell proteins to aid coronaviruses in evading the hosts innate immune responses. We established a high-throughput X-ray screening to identify inhibitors by elucidating the native PLpro structure refined to 1.42 Angstroms and performing co-crystallization utilizing a diverse library of selected natural compounds. We identified three phenolic compounds as potential inhibitors. Crystal structures of PLpro inhibitor complexes, obtained to resolutions between 1.7-1.9 Angstroms, show that all three compounds bind at the ISG15/Ub-S2 allosteric binding site, preventing the essential ISG15-PLpro molecular interactions. All compounds demonstrate clear inhibition in a deISGylation assay, two exhibit distinct antiviral activity and one inhibited a cytopathic effect in a non-cytotoxic concentration range. These results highlight the druggability of the rarely explored ISG15/Ub-S2 PLpro allosteric binding site to identify new and effective antiviral compounds. Importantly, in the context of increasing PLpro mutations in the evolving new variants of SARS-CoV-2, the natural compounds we identified may also reinstate the antiviral immune response processes of the host that are down-regulated in COVID-19 infections.

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

Allostery is a fundamental process in regulating proteins’ activity. The discovery, design and development of allosteric drugs demand for better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we present a computational model using automated machine learning for allosteric site prediction. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 89.2% of allosteric pockets appeared among the top 3 positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (https://passer.smu.edu) to facilitate allosteric drug discovery.

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

Allostery is a fundamental process in regulating proteins’ activity. The discovery, design and development of allosteric drugs demand for better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we present a computational model using automated machine learning for allosteric site prediction. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 89.2% of allosteric pockets appeared among the top 3 positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (https://passer.smu.edu) to facilitate allosteric drug discovery.

Synthesis and Pharmacological Evaluation of Novel 2,3,4,5-tetrahydro[1,3]diazepino[1,2-a]benzimidazole Derivatives as Promising Anxiolytic and Analgesic Agents

A number of novel 2,3,4,5-tetrahydro[1,3]diazepino[1,2-a]benzimidazole derivatives 2 were obtained by alkylation mainly in the 1H-tautomeric form of 2,3,4,5-tetrahydro[1,3]diazepino[1,2-a]benzimidazole or its 8,9-dimethyl-substituted analog 4-chlorobenzyl bromide, 4-chloroacetic acid fluoroanilide, and 4-tert-butylphenacyl bromide in neutral medium. Compounds 3 were cyclized and synthesized earlier with 11-phenacyl-substituted diazepino[1,2-a]benzimidazoles upon heating in conc. HBr. The chemical structures of the compounds were clarified by using the 1H Nuclear Magnetic Resonance Spectroscopy (1H-NMR) technique. Anxiolytic properties were evaluated using the elevated plus maze (EPM) and open field (OF) tests. The analgesic effect of compounds was estimated with the tail flick (TF) and hot plate (HP) methods. Besides, possible the influence of the test compounds on motor activities of the animals was examined by the Grid, Wire, and Rotarod tests. Compounds 2d and 3b were the most active due to their prominent analgesic and anxiolytic potentials, respectively. The results of the performed in silico analysis showed that the high anxiolytic activity of compound 3b is explained by the combination of a pronounced interaction mainly with the benzodiazepine site of the GABAA receptor with a prominent interaction with both the specific and allosteric sites of the 5-HT2A receptor.