In-silico screening for identification of potential inhibitors against SARS-CoV-2 transmembrane serine protease 2 (TMPRSS2)

New perspective of small-molecule antiviral drugs development for RNA viruses

High variability and adaptability of RNA viruses allows them to spread between humans and animals, causing large-scale infectious diseases which seriously threat human and animal health and social development. At present, AIDS, viral hepatitis and other viral diseases with high incidence and low cure rate are still spreading around the world. The outbreaks of Ebola, Zika, dengue and in particular of the global pandemic of COVID-19 have presented serious challenges to the global public health system. The development of highly effective and broad-spectrum antiviral drugs is a substantial and urgent research subject to deal with the current RNA virus infection and the possible new viral infections in the future. In recent years, with the rapid development of modern disciplines such as artificial intelligence technology, bioinformatics, molecular biology, and structural biology, some new strategies and targets for antivirals development have emerged. Here we review the main strategies and new targets for developing small-molecule antiviral drugs against RNA viruses through the analysis of the new drug development progress against several highly pathogenic RNA viruses, to provide clues for development of future antivirals.

Network analysis-guided drug repurposing strategies targeting LPAR receptor in the interplay of COVID, Alzheimer's, and diabetes

The COVID-19 pandemic caused by the SARS-CoV-2 virus has greatly affected global health. Emerging evidence suggests a complex interplay between Alzheimer's disease (AD), diabetes (DM), and COVID-19. Given COVID-19's involvement in the increased risk of other diseases, there is an urgent need to identify novel targets and drugs to combat these interconnected health challenges. Lysophosphatidic acid receptors (LPARs), belonging to the G protein-coupled receptor family, have been implicated in various pathological conditions, including inflammation. In this regard, the study aimed to investigate the involvement of LPARs (specifically LPAR1, 3, 6) in the tri-directional relationship between AD, DM, and COVID-19 through network analysis, as well as explore the therapeutic potential of selected anti-AD, anti-DM drugs as LPAR, SPIKE antagonists. We used the Coremine Medical database to identify genes related to DM, AD, and COVID-19. Furthermore, STRING analysis was used to identify the interacting partners of LPAR1, LPAR3, and LPAR6. Additionally, a literature search revealed 78 drugs on the market or in clinical studies that were used for treating either AD or DM. We carried out docking analysis of these drugs against the LPAR1, LPAR3, and LPAR6. Furthermore, we modeled the LPAR1, LPAR3, and LPAR6 in a complex with the COVID-19 spike protein and performed a docking study of selected drugs with the LPAR-Spike complex. The analysis revealed 177 common genes implicated in AD, DM, and COVID-19. Protein-protein docking analysis demonstrated that LPAR (1,3 & 6) efficiently binds with the viral SPIKE protein, suggesting them as targets for viral infection. Furthermore, docking analysis of the anti-AD and anti-DM drugs against LPARs, SPIKE protein, and the LPARs-SPIKE complex revealed promising candidates, including lupron, neflamapimod, and nilotinib, stating the importance of drug repurposing in the drug discovery process. These drugs exhibited the ability to bind and inhibit the LPAR receptor activity and the SPIKE protein and interfere with LPAR-SPIKE protein interaction. Through a combined network and targeted-based therapeutic intervention approach, this study has identified several drugs that could be repurposed for treating COVID-19 due to their expected interference with LPAR(1, 3, and 6) and spike protein complexes. In addition, it can also be hypothesized that the co-administration of these identified drugs during COVID-19 infection may not only help mitigate the impact of the virus but also potentially contribute to the prevention or management of post-COVID complications related to AD and DM.

Identification of potential Indonesian marine invertebrate bioactive compounds as TMPRSS2 and SARS-CoV-2 Omicron spike protein inhibitors through computational screening

The coronavirus pandemic led to the announcement of a worldwide health emergency. The SARS-CoV-2 Omicron variant, which swiftly spread worldwide, has fueled existing challenges. Appropriate medication is necessary to avoid severe SARS-CoV-2 disease. The human TMPRSS2 and SARS-CoV-2 Omicron spike protein, which are required for viral entry into the host phase, were identified as the target proteins through computational screening. Structure-based virtual screening; molecular docking; absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis; and molecular dynamics simulation were the methods applied for TMPRSS2 and spike protein inhibitors. Bioactive marine invertebrates from Indonesia were employed as test ligands. Camostat and nafamostat (co-crystal) were utilized as reference ligands against TMPRSS2, whereas mefloquine was used as a reference ligand against spike protein. Following a molecular docking and dynamics simulation, we found that acanthomanzamine C has remarkable effectiveness against TMPRSS2 and spike protein. Compared to camostat (-8.25 kcal/mol), nafamostat (-6.52 kcal/mol), and mefloquine (-6.34 kcal/mol), acanthomanzamine C binds to TMPRSS2 and spike protein with binding energies of -9.75 kcal/mol and -9.19 kcal/mol, respectively. Furthermore, slight variances in the MD simulation demonstrated consistent binding to TMPRSS2 and spike protein after the initial 50 ns. These results are highly valuable in the search for a treatment for SARS-CoV-2 infection.

Beyond Acute COVID-19: A Review of Long-term Cardiovascular Outcomes

Statistics Canada estimated that approximately 1.4 million Canadians suffer from long COVID. Although cardiovascular changes during acute SARS-CoV-2 infection are well documented, long-term cardiovascular sequelae are less understood. In this review, we sought to characterize adult cardiovascular outcomes in the months after acute COVID-19 illness. In our search we identified reports of outcomes including cardiac dysautonomia, myocarditis, ischemic injuries, and ventricular dysfunction. Even in patients without overt cardiac outcomes, subclinical changes have been observed. Cardiovascular sequelae after SARS-CoV-2 infection can stem from exacerbation of preexisting conditions, ongoing inflammation, or as a result of damage that occurred during acute infection. For example, myocardial fibrosis has been reported months after hospital admission for COVID-19 illness, and might be a consequence of myocarditis and myocardial injury during acute disease. In turn, myocardial fibrosis can contribute to further outcomes including dysrhythmias and heart failure. Severity of acute infection might be a risk factor for long-term cardiovascular consequences, however, cardiovascular changes have also been reported in young, healthy individuals who had asymptomatic or mild acute disease. Although evolving evidence suggests that previous SARS-CoV-2 infection might be a risk factor for cardiovascular disease, there is heterogeneity in existing evidence, and some studies are marred by measured and unmeasured confounders. Many investigations have also been limited by relatively short follow-up. Future studies should focus on longer term outcomes (beyond 1 year) and identifying the prevalence of outcomes in different populations on the basis of acute and long COVID disease severity.

Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.

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.

Identification of some dietary flavonoids as potential inhibitors of TMPRSS2 through protein-ligand interaction studies and binding free energy calculations

The continuing threat of COVID-19 and deaths need an urgent cost-effective pharmacological approach. Here, we examine the inhibitory activity of a group of dietary bioactive flavonoids against the human protease TMPRSS2, which plays a major role in SARS CoV-2 viral entry. After the molecular docking studies of a large number of flavonoids, four compounds with high binding scores were selected and studied in detail. The binding affinities of these four ligands, Amentoflavone, Narirutin, Eriocitrin, and Naringin, at the active site of the TMPRSS2 target, were investigated using MD simulations followed by MM-PBSA binding energy calculations. From the studies, a number of significant hydrophobic and hydrogen bonding interactions between the ligands and binding site amino residues of TMPRSS2 are identified which showcase their excellent inhibitory activity against TMPRSS2. Among these ligands, Amentoflavone and Narirutin showed MM-PBSA binding energy values of -155.57 and -139.71 kJ/mol, respectively. Our previous studies of the inhibitory activity of these compounds against the main protease of SARS-COV2 and the present study on TMPRSS2 strongly highlighted that Amentoflavone and Naringin can exhibit promising multi-target activity against SARS-CoV-2. Moreover, due to their wide availability, no side effects, and low cost, these compounds could be recommended as dietary supplements for COVID patients or for the development of SARS-CoV-2 treatments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-01955-7.

High predictive QSAR models for predicting the SARS coronavirus main protease inhibition activity of ketone-based covalent inhibitors

In this research, a dataset including 29 ketone-based covalent inhibitors with SARS-CoV-1 3CL^pro inhibition activity was used to develop high predictive QSAR models. Twenty-two molecules were put in train set and seven molecules in test set. By using stepwise MLR method for molecules in train set, four molecular descriptors including Mor26p, Hy, GATS7p and Mor04v were selected to build QSAR models. MLR and ANN methods were used to create QSAR models for predicting the activity of molecules in both train and test sets. Both QSAR models were validated by calculating several statistical parameters. R² values for the test set of MLR and ANN models were 0.93 and 0.95, respectively, and RMSE values for their test sets were 0.24 and 0.17, respectively. Other calculated statistical parameters (especially \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q_{F3}^{2}$$\end{document}QF32 parameter) show that created ANN model has more predictive power with respect to developed MLR model (with four descriptor). Calculated leverages for all molecules show that predicted pIC₅₀ (by both QSAR models) for all molecules is acceptable, and drawn residuals plots show that there is no systematic error in building both QSAR modes. Also, based on developed MLR model, used molecular descriptors were interpreted.

Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking

Screening already approved drugs for activity against a novel pathogen can be an important part of global rapid-response strategies in pandemics. Such high-throughput repurposing screens have already identified several existing drugs with potential to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifically against this pathogen requires unambiguous identification of their corresponding targets, something the high-throughput screens are not typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom protein structures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plurality of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method with known drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentially suitable for repurposing to the inverse docking procedure. The most common preferential targets were the human enzymes TMPRSS2 and PIKfyve, followed by the viral enzymes Helicase and PLpro. All compounds that selected TMPRSS2 are known serine protease inhibitors, and those that selected PIKfyve are known tyrosine kinase inhibitors. Detailed structural analysis of the docking poses revealed important insights into why these selections arose, and could potentially lead to more rational design of new drugs against these targets.