Merbromin is a mixed-type inhibitor of 3-chyomotrypsin like protease of SARS-CoV-2

Silver N-heterocyclic carbene complexes are potent uncompetitive inhibitors of the papain-like protease with antiviral activity against SARS-CoV-2

The ongoing SARS-CoV-2 pandemic has caused a high demand for novel innovative antiviral drug candidates. Despite promising results, metal complexes have been relatively unexplored as antiviral agents in general and in particular against SARS-CoV-2. Here we report on silver NHC complexes with chloride or iodide counter ligands that are potent inhibitors of the SARS-CoV-2 papain-like protease (PL^pro) but inactive against 3C-like protease (3CL^pro) as another SARS-CoV-2 protease. Mechanistic studies on a selected complex confirmed zinc removal from a zinc binding domain of PL^pro as relevant factor of their activity. In addition, enzyme kinetic experiments revealed that the complex is an uncompetitive inhibitor and with this rare type of inhibition it offers great pharmacological advantages in terms selectivity. The silver NHC complexes with iodide ligands showed very low or absent host cell toxicity and triggered strong effects on viral replication in cells infected with SARS-CoV-2, making them promising future antiviral drug candidates.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Cotton flower metabolites inhibit SARS-CoV-2 main protease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading globally for over 2 years, causing serious contagious disease and incalculable damage. The introduction of vaccines has slowed the spread of SARS-CoV-2 to some extent, but there remains a need for specific and effective treatment. The high chemical diversity and safety profiles of natural products make them a potential source of effective anti-SARS-CoV-2 drugs. Cotton plant is one of the most important economic and medical crops and is the source of a large number of antiviral phytochemicals. In this work, we used SARS-CoV-2 main protein (M^pro ) as the target to identify potential anti-SARS-CoV-2 natural products in cotton. An in vitro assay showed that of all cotton tissues examined, cotton flower extracts (CFs) exhibited optimal inhibitory effects against M^pro . We proceeded to use the CF metabolite database to screen natural M^pro inhibitors by combining virtual screening and biochemical assays. We identified that several CF natural products, including astragalin, myricitrin, and astilbin, significantly inhibited M^pro with half-maximal inhibitory concentrations (IC50s) of 0.13, 10.73, and 7.92 μm, respectively. These findings may serve as a basis for further studies into the suitability of cotton as a source of potential therapeutics for SARS-CoV-2.

The SARS-CoV-2 main protease (Mpro): Structure, function, and emerging therapies for COVID-19

The main proteases (Mpro), also termed 3-chymotrypsin-like proteases (3CLpro), are a class of highly conserved cysteine hydrolases in β-coronaviruses. Increasing evidence has demonstrated that 3CLpros play an indispensable role in viral replication and have been recognized as key targets for preventing and treating coronavirus-caused infectious diseases, including COVID-19. This review is focused on the structural features and biological function of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease Mpro (also known as 3CLpro), as well as recent advances in discovering and developing SARS-CoV-2 3CLpro inhibitors. To better understand the characteristics of SARS-CoV-2 3CLpro inhibitors, the inhibition activities, inhibitory mechanisms, and key structural features of various 3CLpro inhibitors (including marketed drugs, peptidomimetic, and non-peptidomimetic synthetic compounds, as well as natural compounds and their derivatives) are summarized comprehensively. Meanwhile, the challenges in this field are highlighted, while future directions for designing and developing efficacious 3CLpro inhibitors as novel anti-coronavirus therapies are also proposed. Collectively, all information and knowledge presented here are very helpful for understanding the structural features and inhibitory mechanisms of SARS-CoV-2 3CLpro inhibitors, which offers new insights or inspiration to medicinal chemists for designing and developing more efficacious 3CLpro inhibitors as novel anti-coronavirus agents.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.