This review makes a critical evaluation of 61 peer‐reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS‐CoV‐2 M‐pro (M‐pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS‐CoV‐2 M‐pro inhibitors extracted from the literature and a second set of 81 M‐pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS‐CoV‐2 M‐pro inhibitor. Using two SARS‐CoV‐2 M‐pro structures and five protein‐ligand docking programs, we proved that the correlation between the pIC50 and docking scores is not good. Neither was any correlation found between the pIC50 and the ∆G calculated with an MM‐GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M‐pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M‐pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS‐CoV‐2 M‐pro inhibitors presented here could be used for validating future VS protocols which aim to predict M‐pro inhibitors.

In this review, we collected 1765 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M-pro inhibitors from the bibliography and other sources, such as the COVID Moonshot project and the ChEMBL database. This set of inhibitors includes only those compounds whose inhibitory capacity, mainly expressed as the half-maximal inhibitory concentration (IC50) value, against M-pro from SARS-CoV-2 has been determined. Several covalent warheads are used to treat covalent and non-covalent inhibitors separately. Chemical space, the variation of the IC50 inhibitory activity when measured by different methods or laboratories, and the influence of 1,4-dithiothreitol (DTT) are discussed. When available, we have collected the values of inhibition of viral replication measured with a cellular antiviral assay and expressed as half maximal effective concentration (EC50) values, and their possible relationship to inhibitory potency against M-pro is analyzed. Finally, the most potent covalent and non-covalent inhibitors that simultaneously inhibit the SARS-CoV-2 M-pro and the virus replication in vitro are discussed.

Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model’s Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.

Background Extracellular vesicles (EVs) play a crucial role in intercellular communication, participating in the paracrine trophic support or in the propagation of toxic molecules, including proteins. RTP801 is a stress-regulated protein, whose levels are elevated during neurodegeneration and induce neuron death. However, whether RTP801 toxicity is transferred trans-neuronally via EVs remains unknown. Methods We overexpressed or silenced RTP801 protein in cultured cortical neurons and isolated the neural-derived EVs (RTP801-EVs, or shRTP801-EVs respectively). We characterized their protein content by mass spectrometry (MS) and western blotting (WB). RTP801-EVs toxicity was assessed by treating cultured neurons with these EVs and quantifying apoptotic neuron death and branching. We also tested shRTP801-EVs functionality in the pathologic in vitro model of 6-Hydroxydopamine (6-OHDA) by biochemical analyses. Results Expression of RTP801 increased the number of EVs released by neurons. Moreover, RTP801 led to a distinct proteomic signature of neural-derived EVs, containing more pro-apoptotic markers. Hence, we observed that RTP801 toxicity was transferred to neurons via EVs, activating apoptosis and impairing neuron morphology complexity. In contrast, EVs derived from neurons where RTP801 was silenced were able to increase the arborization of recipient neurons. We also showed that 6-OHDA neurotoxin elevated protein levels of RTP801 in both cell lysates and EVs. Furthermore, neural-derived EVs induced phosphorylation of Ser473-Akt and Ser235/236-RPS6 in recipient neurons, and this effect was lost when EVs were derived from neurons treated with 6-OHDA. Interestingly, EVs derived from neurons where RTP801 was silenced prior to exposing them to 6-OHDA maintained Akt and RPS6 transactivation in recipient neurons. Conclusion Taken together, these results suggest that RTP801 toxicity can be spread via EVs and therefore, it could contribute to the progression of neurodegenerative diseases in which RTP801 is involved.

The non‐canonical functions of the transcription factor STAT3 have been poorly studied in comparison to its canonical mechanisms of gene expression activation. Here, Köhler et al. put the spotlight on a novel unconventional repressing mechanism of STAT3 over the REDD1 gene, named DDIT4. These findings are crucial to expand the knowledge of the stress‐induced short‐lived REDD1 protein that inactivates mTOR and the consequences of this fine‐tuned regulation in the context of pathological conditions such as cancer or neurodegenerative diseases.