SARS-CoV-2 main protease (M-pro) mutational profiling: An insight into mutation coldspots.

SARS-CoV-2 and the COVID-19 pandemic have marked a milestone in the history of scientific research worldwide. To ensure that treatments are successful in the mid-long term, it is crucial to characterize SARS-CoV-2 mutations, as they might lead to viral resistance. Data from >5,700,000 SARS-CoV-2 genomes available at GISAID was used to report SARS-CoV-2 mutations.

Are protein-ligand docking programs good enough to predict experimental poses of noncovalent ligands bound to the SARS-CoV-2 main protease?

Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs.

Challenges in distinguishing functional proteins from polyproteins in databases: implications for drug discovery.

This opinion article addresses a major issue in molecular biology and drug discovery by highlighting the complications that arise from combining polyproteins and their functional products within the same database entry. This problem, exemplified by the discovery of novel inhibitors for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease, has an influence on our ability to retrieve precise data and hinders the development of targeted therapies. It also emphasizes the need for improved database practices and underscores their significance in advancing scientific research.

The Mutational Landscape of SARS-CoV-2.

Mutation research is crucial for detecting and treating SARS-CoV-2 and developing vaccines. Using over 5,300,000 sequences from SARS-CoV-2 genomes and custom Python programs, we analyzed the mutational landscape of SARS-CoV-2. Although almost every nucleotide in the SARS-CoV-2 genome has mutated at some time, the substantial differences in the frequency and regularity of mutations warrant further examination.

Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model.

The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug.

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.

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.

A Review of the Current Landscape of SARS-CoV-2 Main Protease Inhibitors: Have We Hit the Bullseye Yet?

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 (IC) value, against M-pro from SARS-CoV-2 has been determined. Several covalent warheads are used to treat covalent and non-covalent inhibitors separately.

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.

Identification of Broad-Spectrum MMP Inhibitors by Virtual Screening.

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.

Anti-Inflammatory and Immunomodulatory Effects of the Natural Compound -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 -orsellinaldehyde, a natural compound found by an in silico approach in the mushroom, in astrocytes and microglia cells.

An Unsupervised Algorithm for Host Identification in Flaviviruses.

Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus ), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts.

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.

Understanding the variability of the S1' pocket to improve matrix metalloproteinase inhibitor selectivity profiles.

Matrix metalloproteinases (MMPs) are a family of proteins involved in a range of pathologies. Given that MMP broad-spectrum inhibition is associated with severe adverse effects, selectivity has become a priority in the design of MMP inhibitors, and is often achieved by targeting the variable S1' pocket. However, the specific characteristics of the S1' pocket that determine inhibitor selectivity are often not described and, in many cases, challenging to identify.

Mining large databases to find new leads with low similarity to known actives: application to find new DPP-IV inhibitors.

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.

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.

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.

Combined Ligand- and Receptor-Based Virtual Screening Methodology to Identify Structurally Diverse Protein Tyrosine Phosphatase 1B Inhibitors.

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.

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.

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.

Ephedrine as a lead compound for the development of new DPP-IV inhibitors.

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.

Peroxisome Proliferator-Activated Receptor γ (PPARγ) and Ligand Choreography: Newcomers Take the Stage.

Thiazolidinediones (TZDs), such as rosiglitazone and pioglitazone, are peroxisome proliferator-activated receptor γ (PPARγ) full agonists that have been widely used in the treatment of type 2 diabetes mellitus. Despite the demonstrated beneficial effect of reducing glucose levels in the plasma, TZDs also induce several adverse effects. Consequently, the search for new compounds with potent antidiabetic effects but fewer undesired effects is an active field of research.

Tools for in silico target fishing.

Computational target fishing methods are designed to identify the most probable target of a query molecule. This process may allow the prediction of the bioactivity of a compound, the identification of the mode of action of known drugs, the detection of drug polypharmacology, drug repositioning or the prediction of the adverse effects of a compound. The large amount of information regarding the bioactivity of thousands of small molecules now allows the development of these types of methods.

Molecular fingerprint similarity search in virtual screening.

Molecular fingerprints have been used for a long time now in drug discovery and virtual screening. Their ease of use (requiring little to no configuration) and the speed at which substructure and similarity searches can be performed with them - paired with a virtual screening performance similar to other more complex methods - is the reason for their popularity. However, there are many types of fingerprints, each representing a different aspect of the molecule, which can greatly affect search performance.