María José Ojeda-Montes

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.

Matrix metalloproteinases (MMPs) are the family of proteases that are mainly responsible for degrading extracellular matrix (ECM) components. In the skin, the overexpression of MMPs as a result of ultraviolet radiation triggers an imbalance in the ECM turnover in a process called photoaging, which ultimately results in skin wrinkling and premature skin ageing. Therefore, the inhibition of different enzymes of the MMP family at a topical level could have positive implications for photoaging. Considering that the MMP catalytic region is mostly conserved across different enzymes of the MMP family, in this study we aimed to design a virtual screening (VS) workflow to identify broad-spectrum MMP inhibitors that can be used to delay the development of photoaging. Our in silico approach was validated in vitro with 20 VS hits from the Specs library that were not only structurally different from one another but also from known MMP inhibitors. In this bioactivity assay, 18 of the 20 compounds inhibit at least one of the assayed MMPs at 100 μM (with 5 of them showing around 50% inhibition in all the tested MMPs at this concentration). Finally, this VS was used to identify natural products that have the potential to act as broad-spectrum MMP inhibitors and be used as a treatment for photoaging.

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.

Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.

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.

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

Protein tyrosine phosphatase 1B (PTP1B) is a potential drug target for diabetes and obesity. However, the design of PTP1B inhibitors that combine potency and bioavailability is a great challenge, and new leads are needed to circumvent this problem. Virtual screening (VS) workflows can be used to find new PTP1B inhibitors with little chemical similarity to existing inhibitors. Unfortunately, previous VS workflows for the identification of PTP1B inhibitors have several limitations, such as a small number of experimentally tested compounds and the low bioactivity of those compounds. We developed a VS workflow capable of identifying 15 structurally diverse PTP1B inhibitors from 20 compounds, the bioactivity of which was tested in vitro. Moreover, we identified two PTP1B inhibitors with the highest bioactivity reported by any VS campaign (i.e., IC₅₀ values of 1.4 and 2.1 μm), which could be used as new lead compounds.

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.

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.

Aim: Extracts from Ephedra species have been reported to be effective as antidiabetics. A previous in silico study predicted that ephedrine and five ephedrine derivatives could contribute to the described antidiabetic effect of Ephedra extracts by inhibiting dipeptidyl peptidase IV (DPP-IV). Finding selective DPP-IV inhibitors is a current therapeutic strategy for Type 2 diabetes mellitus management. Therefore, the main aim of this work is to experimentally determine whether these alkaloids are DPP-IV inhibitors. Materials & methods: The DPP-IV inhibition of Ephedra's alkaloids was determined via a competitive-binding assay. Then, computational analyses were used in order to find out the protein–ligand interactions and to perform a lead optimization. Results: Our results show that all six molecules are DPP-IV inhibitors, with IC50 ranging from 124 μM for ephedrine to 28 mM for N-methylpseudoephedrine. Conclusion: Further computational analysis shows how Ephedra's alkaloids could be used as promising lead ...