The SARS-CoV-2 pandemic has prompted global efforts to develop therapeutics. The main protease of SARS-CoV-2 (Mpro) and the papain-like protease (PLpro) are essential for viral replication and are key targets for therapeutic development. In this work, we investigate the mechanisms of SARS-CoV-2 inhibition by diphenyl diselenide (PhSe)2 which is an archetypal model of diselenides and a renowned potential therapeutic agent. The in vitro inhibitory concentration of (PhSe)2 against SARS-CoV-2 in Vero E6 cells falls in the low micromolar range. Molecular dynamics (MD) simulations and density functional theory (DFT) calculations [level of theory: SMD-B3LYP-D3­(BJ)/6-311G­(d,p), cc-pVTZ] are used to inspect non-covalent inhibition modes of both proteases via π-stacking and the mechanism of covalent (PhSe)2 + Mpro product formation involving the catalytic residue C145, respectively. The in vitro CC50 (24.61 μM) and EC50 (2.39 μM) data indicate that (PhSe)2 is a good inhibitor of the SARS-CoV-2 virus replication in a cell culture model. The in silico findings indicate potential mechanisms of proteases’ inhibition by (PhSe)2; in particular, the results of the covalent inhibition here discussed for Mpro, whose thermodynamics is approximatively isoergonic, prompt further investigation in the design of antiviral organodiselenides.

The main protease (M^pro) of SARS-CoV-2 is a current target for the inhibition of viral replication. Through a combined Docking and Density Functional Theory (DFT) approach, we investigated in-silico the molecular mechanism by which ebselen (IUPAC: 2-phenyl-1,2-benzoselenazol-3-one), the most famous and pharmacologically active organoselenide, inhibits M^pro. For the first time, we report on a mechanistic investigation in an enzyme for the formation of the covalent -S-Se- bond between ebselen and a key enzymatic cysteine. The results highlight the strengths and weaknesses of ebselen and provide hints for a rational drug design of bioorganic selenium-based inhibitors.

Acetylcholinesterase inhibitors (AChEIs) like donepezil are commonly used to treat Alzheimer’s disease. AChEIs have also been considered for other therapeutic uses, such as anti-inflammatory neuroprotective agents. Consequently, the use of natural plant products as potential AChEIs can have therapeutic benefits. We previously reported the anticholinesterase properties of the phenolics and alkaloids found in the leaf extracts of two tropical plants with nutritional and ethnobotanical importance—African eggplant (Solanum macrocarpon L) and Black nightshade (Solanum nigrum L). Here, we tested the ability of both extracts to inhibit human erythrocyte AChE (an indirect mediator of pro-inflammatory cytokines production via acetylcholine degradation). We further used molecular docking and MD simulation to identify the potential molecular mechanism(s) of phenolic and alkaloid compounds as human AChEIs. Special focus was given to compounds containing the benzyl group that can establish stacking interactions similar to donepezil (a standard AChEI). Flavone-luteolin rutinosides (LR) were identified as single-binding or dual-binding AChEIs; specifically, we attributed the dual-binding LR4 and LR5 to their linked hexose moiety. This characteristic allows the dual binders to occupy the catalytic triads and the peripheral anionic subsite, while exploring the catalytic gorge. We further delineated the inhibition of human erythrocyte AChE, as the flavone common to both plant extracts—luteolin rutinosides—had positive in silico interactions with AChE. These findings suggest that phytochemicals from S. macrocarpon and S. nigrum with dual binding properties can be potential AChE inhibitors. In fact, compounds such as LR4 and LR5 should be further investigated as potential inhibitors of human AChE and may represent important natural alternatives to donepezil. Communicated by Ramaswamy H. Sarma

Chalcogen-nitrogen chemistry deals with systems in which sulfur, selenium, or tellurium is linked to a nitrogen nucleus. This chemical motif is a key component of different functional structures, ranging from inorganic materials and polymers, to rationally designed catalysts, to bioinspired molecules and enzymes. The formation of a selenium–nitrogen bond, typically occurring upon condensation of an amine and the unstable selenenic acid, often leading to intramolecular cyclizations, and its disruption, mainly promoted by thiols, are rather common events in organic Se-catalyzed processes. In this work, focusing on examples taken from selenium organic chemistry and biochemistry, the selenium–nitrogen bond is described, and its strength and reactivity are quantified using accurate computational methods applied to model molecular systems. The intermediate strength of the Se–N bond, which can be tuned to necessity, gives rise to significant trends when comparing it to the stronger S– and weaker Te–N bonds, reaffirming also in this context the peculiar and valuable role of selenium in chemistry and life.

Allosteric regulation is essential for modulating protein function and represents a promising target for therapeutic intervention, yet the complex dynamics of the protein nanoenvironment hinder the reliable identification of allosteric sites. Traditional pocket-based predictors miss $\sim $18% of experimentally confirmed sites that lie outside surface invaginations. To overcome this limitation, we developed STINGAllo, an interactive web server that introduces a residue-centric machine-learning model. Using 54 optimized internal protein nanoenvironment descriptors, STINGAllo predicts allosteric site-forming residues at single-residue resolution. By integrating hydrophobic interaction networks, local density, graph connectivity, and a unique “sponge effect” metric, STINGAllo detects allosteric sites independently of surface geometry, including concave pockets, flat surfaces, or even cryptic regions. It achieves a success rate of $\sim $78% on benchmark datasets, substantially outperforming existing methods with a 60.2% overall success rate compared with 21.1%–24.2% for contemporary pocket-based predictors. Our analysis further reveals that nearly 52.7% of unique proteins in the Protein Data Bank [(PDB); 119 851 entries, 14 November 2024] contain at least one chain with a predicted allosteric site. STINGAllo accepts protein structures via PDB identifiers or custom uploads, provides interactive 3D visualization of predicted pockets, and supports integration into computational pipelines through a RESTful application programming interface. Overall, STINGAllo bridges advanced computational prediction with user-friendly design, offering a robust tool expected to deepen understanding of protein regulation and accelerate allosteric drug discovery. The server is freely accessible at https://www.stingallo.cbi.cnptia.embrapa.br/.