Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition

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.

Anti-Inflammatory and Immunomodulatory Effects of the Grifola frondosa Natural Compound o-Orsellinaldehyde on LPS-Challenged Murine Primary Glial Cells. Roles of NF-κβ and MAPK

In response to foreign or endogenous stimuli, both microglia and astrocytes adopt an activated phenotype that promotes the release of pro-inflammatory mediators. This inflammatory mechanism, known as neuroinflammation, is essential in the defense against foreign invasion and in normal tissue repair; nevertheless, when constantly activated, this process can become detrimental through the release of neurotoxic factors that amplify underlying disease. In consequence, this study presents the anti-inflammatory and immunomodulatory properties of o-orsellinaldehyde, a natural compound found by an in silico approach in the Grifola frondosa mushroom, in astrocytes and microglia cells. For this purpose, primary microglia and astrocytes were isolated from mice brain and cultured in vitro. Subsequently, cells were exposed to LPS in the absence or presence of increasing concentrations of this natural compound. Specifically, the results shown that o-orsellinaldehyde strongly inhibits the LPS-induced inflammatory response in astrocytes and microglia by decreasing nitrite formation and downregulating iNOS and HO-1 expression. Furthermore, in microglia cells o-orsellinaldehyde inhibits NF-κB activation; and potently counteracts LPS-mediated p38 kinase and JNK phosphorylation (MAPK). In this regard, o-orsellinaldehyde treatment also induces a significant cell immunomodulation by repolarizing microglia toward the M2 anti-inflammatory phenotype. Altogether, these results could partially explain the reported beneficial effects of G. frondosa extracts on inflammatory conditions.

Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition

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.

The Light and Dark Sides of Virtual Screening: What Is There to Know?

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.

Anti-inflammatory and Proapoptotic Properties of the Natural Compound o-Orsellinaldehyde

Metabolic syndrome is a cluster of medical conditions that increases the risk of developing cardiovascular disease and type 2 diabetes. Numerous studies have shown that inflammation is directly involved in the onset of metabolic syndrome and related pathologies. In this study, in silico techniques were applied to a natural products database containing molecules isolated from mushrooms from the Catalan forests to predict molecules that can act as human nuclear-factor κβ kinase 2 (IKK-2) inhibitors. IKK-2 is the main component responsible for activating the nuclear-factor κβ transcription factor (NF-κβ). One of these predicted molecules was o-orsellinaldehyde, a molecule present in the mushroom Grifola frondosa. This study shows that o-orsellinaldehyde presents anti-inflammatory and pro-apoptotic properties by acting as IKK-2 inhibitor. Additionally, we suggest that the anti-inflammatory and pro-apoptotic properties of Grifola frondosa mushroom could partially be explained by the presence of o-orsellinaldehyde on its composition.

Activity and selectivity cliffs for DPP-IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

The inhibition of dipeptidyl peptidase-IV (DPP-IV) has emerged over the last decade as one of the most effective treatments for type 2 diabetes mellitus, and consequently (a) 11 DPP-IV inhibitors have been on the market since 2006 (three in 2015), and (b) 74 noncovalent complexes involving human DPP-IV and drug-like inhibitors are available at the Protein Data Bank (PDB). The present review aims to (a) explain the most important activity cliffs for DPP-IV noncovalent inhibition according to the binding site structure of DPP-IV, (b) explain the most important selectivity cliffs for DPP-IV noncovalent inhibition in comparison with other related enzymes (i.e., DPP8 and DPP9), and (c) use the information deriving from this activity/selectivity cliff analysis to suggest how virtual screening protocols might be improved to favor the early identification of potent and selective DPP-IV inhibitors in molecular databases (because they have not succeeded in identifying selective DPP-IV inhibitors with IC₅₀ ≤ 100 nM). All these goals are achieved with the help of available homology models for DPP8 and DPP9 and an analysis of the structure-activity studies used to develop the noncovalent inhibitors that form part of some of the complexes with human DPP-IV available at the PDB.

Resveratrol Potently Counteracts Quercetin Starvation-Induced Autophagy and Sensitizes HepG2 Cancer Cells to Apoptosis

SCOPE

Resveratrol (RSV) has been described as a potent antioxidant, antisteatotic, and antitumor compound, and it has also been identified as a potent autophagy inducer. On the other hand, quercetin (QCT) is a dietary flavonoid with known antitumor, anti-inflammatory, and antidiabetic effects. Additionally, QCT increases autophagy. To study the hypothetical synergistic effect of both compounds, we test the combined effect of QCT and RSV on the autophagy process in HepG2 cells.

METHODS AND RESULTS

Autophagy is studied by western blotting, real-time RT-PCR, and cellular staining. Our results clearly indicate a bifunctional molecular effect of RSV. Both polyphenols are individually able to promote autophagy. Strikingly, when RSV is combined with QCT, it promotes a potent reduction of QCT-induced autophagy and influences proapoptotic signaling.

CONCLUSION

RSV acts differentially on the autophagic process depending on the cellular energetic state. We further characterize the molecular mechanisms related to this effect, and we observe that AMP-activated protein kinase (AMPK) phosphorylation, heme oxygenase 1 (HO-1) downregulation, lysosomal membrane permeabilization (LMP), and Zinc (Zn²⁺ ) dynamics could be important modulators of such RSV-related effects and could globally represent a promising strategy to sensitize cancer cells to QCT treatment.