Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus’ replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively.

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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.

Chronic lymphocytic leukemia (CLL) is a B cell malignancy associated with increased levels of inflammatory cytokines. Similarly, expression of CD38 on CLL cells correlates with CLL cell survival and proliferation, but the mechanisms that regulate CD38 expression and inflammatory cytokines remain unclear. We have recently demonstrated that patients have CLL-specific Th cells that support CLL proliferation. In this article, we show that CLL cells attract such Th cells, thereby establishing an Ag-dependent collaboration. Blocking experiments performed in vitro as wells as in vivo, using a xenograft model, revealed that secretion of IFN-γ was a major mechanism by which CLL-specific Th cells increased CD38 on CLL cells. The expression of the transcription factor T-bet in peripheral blood CLL cells significantly correlated with CD38 expression, and transient transfection of CLL cells with T-bet resulted in T-bethiCD38hi cells. Finally, chromatin immunoprecipitation experiments revealed that T-bet can bind to regulatory regions of the CD38 gene. These data suggest that CLL cells attract CLL-specific Th cells and initiate a positive feedback loop with upregulation of T-bet, CD38, and type 1 chemokines allowing further recruitment of Th cells and increased type 1 cytokine secretion. This insight provides a cellular and molecular mechanism that links the inflammatory signature observed in CLL pathogenesis with CD38 expression and aggressive disease and suggests that targeting the IFN-γ/IFN-γR/JAK/STAT/T-bet/CD38 pathway could play a role in the therapy of CLL.