Ebselen derivatives inhibit SARS-CoV-2 replication by inhibition of its essential proteins: PLpro and Mpro proteases, and nsp14 guanine N7-methyltransferase.

Proteases encoded by SARS-CoV-2 constitute a promising target for new therapies against COVID-19. SARS-CoV-2 main protease (Mpro, 3CLpro) and papain-like protease (PLpro) are responsible for viral polyprotein cleavage-a process crucial for viral survival and replication. Recently it was shown that 2-phenylbenzisoselenazol-3(2H)-one (ebselen), an organoselenium anti-inflammatory small-molecule drug, is a potent, covalent inhibitor of both the proteases and its potency was evaluated in enzymatic and antiviral assays. In this study, we screened a collection of 34 ebselen and ebselen diselenide derivatives for SARS-CoV-2 PLpro and Mpro inhibitors. Our studies revealed that ebselen derivatives are potent inhibitors of both the proteases. We identified three PLpro and four Mpro inhibitors superior to ebselen. Independently, ebselen was shown to inhibit the N7-methyltransferase activity of SARS-CoV-2 nsp14 protein involved in viral RNA cap modification. Hence, selected compounds were also evaluated as nsp14 inhibitors. In the second part of our work, we employed 11 ebselen analogues-bis(2-carbamoylaryl)phenyl diselenides-in biological assays to evaluate their anti-SARS-CoV-2 activity in Vero E6 cells. We present their antiviral and cytoprotective activity and also low cytotoxicity. Our work shows that ebselen, its derivatives, and diselenide analogues constitute a promising platform for development of new antivirals targeting the SARS-CoV-2 virus.

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years.

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 µM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19.

Background: Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients. Methods: The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools. Results: Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2. Conclusion: In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Discovery of Chalcone-Based Hybrid Structures as High Affinity and Site-Specific Inhibitors against SARS-CoV-2: A Comprehensive Structural Analysis Based on Various Host-Based and Viral Targets.

Previous studies indicated that natural-based chalcones have significant inhibitory effects on the coronavirus enzymes 3CLpro and PLpro as well as modulation of some host-based antiviral targets (HBATs). In this study, a comprehensive computational and structural study was performed to investigate the affinity of our compound library consisting of 757 chalcone-based structures (CHA-1 to CHA-757) for inhibiting the 3CLpro and PLpro enzymes and against twelve selected host-based targets. Our results indicated that CHA-12 (VUF 4819) is the most potent and multi-target inhibitor in our chemical library over all viral and host-based targets. Correspondingly, CHA-384 and its congeners containing ureide moieties were found to be potent and selective 3CLpro inhibitors, and benzotriazole moiety in CHA-37 was found to be a main fragment for inhibiting the 3CLpro and PLpro. Surprisingly, our results indicate that the ureide and sulfonamide moieties are integral fragments for the optimum 3CLpro inhibition while occupying the S1 and S3 subsites, which is fully consistent with recent reports on the site-specific 3CLpro inhibitors. Finding the multi-target inhibitor CHA-12, previously reported as an LTD4 antagonist for the treatment of inflammatory pulmonary diseases, prompted us to suggest it as a concomitant agent for relieving respiratory symptoms and suppressing COVID-19 infection.

Exploration of the potential common pathogenic mechanisms in COVID-19 and silicosis by using bioinformatics and system biology.

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

Broad-Spectrum Antivirals Derived from Natural Products.

Scientific advances have led to the development and production of numerous vaccines and antiviral drugs, but viruses, including re-emerging and emerging viruses, such as SARS-CoV-2, remain a major threat to human health. Many antiviral agents are rarely used in clinical treatment, however, because of their inefficacy and resistance. The toxicity of natural products may be lower, and some natural products have multiple targets, which means less resistance. Therefore, natural products may be an effective means to solve virus infection in the future. New techniques and ideas are currently being developed for the design and screening of antiviral drugs thanks to recent revelations about virus replication mechanisms and the advancement of molecular docking technology. This review will summarize recently discovered antiviral drugs, mechanisms of action, and screening and design strategies for novel antiviral agents.

Identification of Flavonoids from Scutellaria barbata D. Don as Inhibitors of HIV-1 and Cathepsin L Proteases and Their Structure-Activity Relationships.

Scutellaria barbata D. Don (SB, Chinese: Ban Zhi Lian), a well-known medicinal plant used in traditional Chinese medicine, is rich in flavonoids. It possesses antitumor, anti-inflammatory, and antiviral activities. In this study, we evaluated the inhibitory activities of SB extracts and its active components against HIV-1 protease (HIV-1 PR) and SARS-CoV2 viral cathepsin L protease (Cat L PR). UPLC/HRMS was used to identify and quantify the major active flavonoids in different SB extracts, and fluorescence resonance energy transfer (FRET) assays were used to determine HIV-1 PR and Cat L PR inhibitions and identify structure-activity relationships. Molecular docking was also performed, to explore the diversification in bonding patterns of the active flavonoids upon binding to the two PRs. Three SB extracts (SBW, SB30, and SB60) and nine flavonoids inhibited HIV-1 PR with an IC50 range from 0.006 to 0.83 mg/mL. Six of the flavonoids showed 10~37.6% inhibition of Cat L PR at a concentration of 0.1 mg/mL. The results showed that the introduction of the 4'-hydroxyl and 6-hydroxyl/methoxy groups was essential in the 5,6,7-trihydroxyl and 5,7,4'-trihydroxyl flavones, respectively, to enhance their dual anti-PR activities. Hence, the 5,6,7,4'-tetrahydroxyl flavone scutellarein (HIV-1 PR, IC50 = 0.068 mg/mL; Cat L PR, IC50 = 0.43 mg/mL) may serve as a lead compound to develop more effective dual protease inhibitors. The 5,7,3',4'-tetrahydroxyl flavone luteolin also showed a potent and selective inhibition of HIV-1 PR (IC50 = 0.039 mg/mL).

Structure-activity relationships of dihydropyrimidone inhibitors against native and auto-processed human neutrophil elastase.

BACKGROUND: Human neutrophil elastase (HNE) is a key driver of systemic and cardiopulmonary inflammation. Recent studies have established the existence of a pathologically active auto-processed form of HNE with reduced binding affinity against small molecule inhibitors. METHOD: AutoDock Vina v1.2.0 and Cresset Forge v10 software were used to develop a 3D-QSAR model for a series of 47 DHPI inhibitors. Molecular Dynamics (MD) simulations were carried out using AMBER v18 to study the structure and dynamics of sc (single-chain HNE) and tcHNE (two-chain HNE). MMPBSA binding free energies of the previously reported clinical candidate BAY 85-8501 and the highly active BAY-8040 were calculated with sc and tcHNE. RESULTS: The DHPI inhibitors occupy the S1 and S2 subsites of scHNE. The robust 3D-QSAR model showed acceptable predictive and descriptive capability with regression coefficient of r2 = 0.995 and cross-validation regression coefficient q2 = 0.579 for the training set. The key descriptors of shape, hydrophobics and electrostatics were mapped to the inhibitory activity. In auto-processed tcHNE, the S1 subsite undergoes widening and disruption. All the DHPI inhibitors docked with the broadened S1'-S2' subsites of tcHNE with lower AutoDock binding affinities. The MMPBSA binding free energy of BAY-8040 with tcHNE reduced in comparison with scHNE while the clinical candidate BAY 85-8501 dissociated during MD. Thus, BAY-8040 may have lower inhibitory activity against tcHNE whereas the clinical candidate BAY 85-8501 is likely to be inactive. CONCLUSION: SAR insights gained from this study will aid the future development of inhibitors active against both forms of HNE.

An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds.

The recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.

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. Experiments in a database of 160 drug-M-pro pairs, from which the pIC50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the pIC50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the pIC50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro.

In Silico Structure-Based Vaccine Design.

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

Correlating the differences in the receptor binding domain of SARS-CoV-2 spike variants on their interactions with human ACE2 receptor.

Spike glycoprotein of SARS-CoV-2 variants plays a critical role in infection and transmission through its interaction with human angiotensin converting enzyme 2 (hACE2) receptors. Prior findings using molecular docking and biomolecular studies reported varied findings on the difference in the interactions among the spike variants with the hACE2 receptors. Hence, it is a prerequisite to understand these interactions in a more precise manner. To this end, firstly, we performed ELISA with trimeric spike glycoproteins of SARS-CoV-2 variants including Wuhan Hu-1(Wild), Delta, C.1.2 and Omicron. Further, to study the interactions in a more specific manner by mimicking the natural infection, we developed hACE2 receptors expressing HEK-293T cell line, evaluated their binding efficiencies and competitive binding of spike variants with D614G spike pseudotyped virus. In line with the existing findings, we observed that Omicron had higher binding efficiency compared to Delta in both ELISA and Cellular models. Intriguingly, we found that cellular models could differentiate the subtle differences between the closely related C.1.2 and Delta in their binding to hACE2 receptors. Our study using the cellular model provides a precise method to evaluate the binding interactions between spike sub-lineages to hACE2 receptors.

Identification of Darunavir Derivatives for Inhibition of SARS-CoV-2 3CLpro.

The effective antiviral agents that treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently needed around the world. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a pivotal role in virus replication; it also has become an important therapeutic target for the infection of SARS-CoV-2. In this work, we have identified Darunavir derivatives that inhibit the 3CLpro through a high-throughput screening method based on a fluorescence resonance energy transfer (FRET) assay in vitro. We found that the compounds 29# and 50# containing polyphenol and caffeine derivatives as the P2 ligand, respectively, exhibited favorable anti-3CLpro potency with EC50 values of 6.3 µM and 3.5 µM and were shown to bind to SARS-CoV-2 3CLpro in vitro. Moreover, we analyzed the binding mode of the DRV in the 3CLpro through molecular docking. Importantly, 29# and 50# exhibited a similar activity against the protease in Omicron variants. The inhibitory effect of compounds 29# and 50# on the SARS-CoV-2 3CLpro warrants that they are worth being the template to design functionally improved inhibitors for the treatment of COVID-19.

Comprehensive DNA methylation profiling of COVID-19 and hepatocellular carcinoma to identify common pathogenesis and potential therapeutic targets.

BACKGROUND & AIMS: The effects of SARS-CoV-2 infection can be more complex and severe in patients with hepatocellular carcinoma (HCC) as compared to other cancers. This is due to several factors, including pre-existing conditions such as viral hepatitis and cirrhosis, which are commonly associated with HCC. METHODS: We conducted an analysis of epigenomics in SARS-CoV-2 infection and HCC patients, and identified common pathogenic mechanisms using weighted gene co-expression network analysis (WGCNA) and other analyses. Hub genes were identified and analyzed using LASSO regression. Additionally, drug candidates and their binding modes to key macromolecular targets of COVID-19 were identified using molecular docking. RESULTS: The epigenomic analysis of the relationship between SARS-CoV-2 infection and HCC patients revealed that the co-pathogenesis was closely linked to immune response, particularly T cell differentiation, regulation of T cell activation and monocyte differentiation. Further analysis indicated that CD4+ T cells and monocytes play essential roles in the immunoreaction triggered by both conditions. The expression levels of hub genes MYLK2, FAM83D, STC2, CCDC112, EPHX4 and MMP1 were strongly correlated with SARS-CoV-2 infection and the prognosis of HCC patients. In our study, mefloquine and thioridazine were identified as potential therapeutic agents for COVID-19 in combined with HCC. CONCLUSIONS: In this research, we conducted an epigenomics analysis to identify common pathogenetic processes between SARS-CoV-2 infection and HCC patients, providing new insights into the pathogenesis and treatment of HCC patients infected with SARS-CoV-2.

Docking-Based Evidence for the Potential of ImmunoDefender: A Novel Formulated Essential Oil Blend Incorporating Synergistic Antiviral Bioactive Compounds as Promising Mpro Inhibitors against SARS-CoV-2.

Essential oils (Eos) have demonstrated antiviral activity, but their toxicity can hinder their use as therapeutic agents. Recently, some essential oil components have been used within safe levels of acceptable daily intake limits without causing toxicity. The "ImmunoDefender," a novel antiviral compound made from a well-known mixture of essential oils, is considered highly effective in treating SARS-CoV-2 infections. The components and doses were chosen based on existing information about their structure and toxicity. Blocking the main protease (Mpro) of SARS-CoV-2 with high affinity and capacity is critical for inhibiting the virus's pathogenesis and transmission. In silico studies were conducted to examine the molecular interactions between the main essential oil components in "ImmunoDefender" and SARS-CoV-2 Mpro. The screening results showed that six key components of ImmunoDefender formed stable complexes with Mpro via its active catalytic site with binding energies ranging from -8.75 to -10.30 kcal/mol, respectively for Cinnamtannin B1, Cinnamtannin B2, Pavetannin C1, Syzyginin B, Procyanidin C1, and Tenuifolin. Furthermore, three essential oil bioactive inhibitors, Cinnamtannin B1, Cinnamtannin B2, and Pavetannin C, had significant ability to bind to the allosteric site of the main protease with binding energies of -11.12, -10.74, and -10.79 kcal/mol; these results suggest that these essential oil bioactive compounds may play a role in preventing the attachment of the translated polyprotein to Mpro, inhibiting the virus's pathogenesis and transmission. These components also had drug-like characteristics similar to approved and effective drugs, suggesting that further pre-clinical and clinical studies are needed to confirm the generated in silico outcomes.

DHFR Inhibitors Display a Pleiotropic Anti-Viral Activity against SARS-CoV-2: Insights into the Mechanisms of Action.

During the COVID-19 pandemic, drug repurposing represented an effective strategy to obtain quick answers to medical emergencies. Based on previous data on methotrexate (MTX), we evaluated the anti-viral activity of several DHFR inhibitors in two cell lines. We observed that this class of compounds showed a significant influence on the virus-induced cytopathic effect (CPE) partly attributed to the intrinsic anti-metabolic activity of these drugs, but also to a specific anti-viral function. To elucidate the molecular mechanisms, we took advantage of our EXSCALATE platform for in-silico molecular modelling and further validated the influence of these inhibitors on nsp13 and viral entry. Interestingly, pralatrexate and trimetrexate showed superior effects in counteracting the viral infection compared to other DHFR inhibitors. Our results indicate that their higher activity is due to their polypharmacological and pleiotropic profile. These compounds can thus potentially give a clinical advantage in the management of SARS-CoV-2 infection in patients already treated with this class of drugs.

In silico Antivirus Repurposing and its Modification to Organoselenium Compounds as SARS-CoV-2 Spike Inhibitors.

<b>Background and Objective:</b> The COVID-19, which has been circulating since late 2019, is caused by SARS-CoV-2. Because of its high infectivity, this virus has spread widely throughout the world. Spike glycoprotein is one of the proteins found in SARS-CoV-2. Spike glycoproteins directly affect infection by forming ACE-2 receptors on host cells. Inhibiting glycoprotein spikes could be one method of treating COVID-19. In this study, the antivirus marketed as a database will be repurposed into an antiviral SARS-CoV-2 and the selected compounds will be modified to become organoselenium compounds. <b>Materials and Methods:</b> The research was carried out using <i>in silico</i> methods, such as rigid docking and flexible docking. To obtain information about the interaction between spike glycoprotein and ligands, MOE 2014.09 was used to perform the molecular docking simulation. <b>Results:</b> The analysis of binding energy values was used to select the ten best ligands from the first stage of the molecular docking simulation, which was then modified according to the previous QSAR study to produce 96 new molecules. The second stage of molecular docking simulation was performed with modified molecules. The best-modified ligand was chosen by analyzing the ADME-Tox property, RMSD value and binding energy value. <b>Conclusion:</b> The best three unmodified ligands, Ombitasvir, Elbasvir and Ledipasvir, have a binding energy value of -15.8065, -15.3842 and -15.1255 kcal mol¹, respectively and the best three modified ligands ModL1, ModL2 and ModL3 has a binding value of -15.6716, -13.9489 and -13.2951 kcal mol¹, respectively with an RMSD value of 1.7109 Å, 2.3179 Å and 1.7836 Å.

Multiscale Simulations of the Covalent Inhibition of the SARS-CoV-2 Main Protease: Four Compounds and Three Reaction Mechanisms.

We report the results of computational modeling of the reactions of the SARS-CoV-2 main protease (MPro) with four potential covalent inhibitors. Two of them, carmofur and nirmatrelvir, have shown experimentally the ability to inhibit MPro. Two other compounds, X77A and X77C, were designed computationally in this work. They were derived from the structure of X77, a non-covalent inhibitor forming a tight surface complex with MPro. We modified the X77 structure by introducing warheads capable of reacting with the catalytic cysteine residue in the MPro active site. The reaction mechanisms of the four molecules with MPro were investigated by quantum mechanics/molecular mechanics (QM/MM) simulations. The results show that all four compounds form covalent adducts with the catalytic cysteine Cys 145 of MPro. From the chemical perspective, the reactions of these four molecules with MPro follow three distinct mechanisms. The reactions are initiated by a nucleophilic attack of the thiolate group of the deprotonated cysteine residue from the catalytic dyad Cys145-His41 of MPro. In the case of carmofur and X77A, the covalent binding of the thiolate to the ligand is accompanied by the formation of the fluoro-uracil leaving group. The reaction with X77C follows the nucleophilic aromatic substitution SNAr mechanism. The reaction of MPro with nirmatrelvir (which has a reactive nitrile group) leads to the formation of a covalent thioimidate adduct with the thiolate of the Cys145 residue in the enzyme active site. Our results contribute to the ongoing search for efficient inhibitors of the SARS-CoV-2 enzymes.