An integrated drug repurposing strategy for the rapid identification of potential SARS-CoV-2 viral inhibitors

Chemical Proteomics Approaches for Screening Small Molecule Inhibitors Covalently Binding to SARS-Cov-2

Although various strategies have been used to prevent and treat SARS-CoV-2, the spread and evolution of SARS-CoV-2 is still progressing rapidly. The emerging variants Omicron and its sublineage have a greater ability to spread and escape nearly all current monoclonal antibodies treatments, highlighting an urgent need to develop therapeutics targeting current and emerging Omicron variants or recombinants with breadth and potency. Here, some small molecule drugs are rapidly identified that could covalently binding to receptor binding domain (RBD) protein of Omicron through the combined application of artificial intelligence (AI) and activity-based protein profiling (ABPP) technology. The surface plasmon resonance (SPR) and pseudo-virus neutralization experiments further reveal that an FDA-approved drug gallic acid has robust neutralization potency against Omicron pseudo-virus with the IC50 values of 23.56 × 10-6 m. Taken together, a platform combining AI intelligence, biochemical, SPR, molecular docking, and pseudo-virus-based screening for rapid identification and evaluation of potential anti-SARS-CoV-2 small molecule drugs is established and the effectiveness of the platform is validated.

DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search

A growing number of deep learning (DL) methodologies have recently been developed to design novel compounds and expand the chemical space within virtual libraries. Most of these neural network approaches design molecules to specifically bind a target based on its structural information and/or knowledge of previously identified binders. Fewer attempts have been made to develop approaches for de novo design of virtual libraries, as synthesizability of generated molecules remains a challenge. In this work, we developed a new Monte Carlo Search (MCS) algorithm, DrugSynthMC (Drug Synthesis using Monte Carlo), in conjunction with DL and statistical-based priors to generate thousands of interpretable chemical structures and novel drug-like molecules per second. DrugSynthMC produces drug-like compounds using an atom-based search model that builds molecules as SMILES, character by character. Designed molecules follow Lipinski's "rule of 5″, show a high proportion of highly water-soluble nontoxic predicted-to-be synthesizable compounds, and efficiently expand the chemical space within the libraries, without reliance on training data sets, synthesizability metrics, or enforcing during SMILES generation. Our approach can function with or without an underlying neural network and is thus easily explainable and versatile. This ease in drug-like molecule generation allows for future integration of score functions aimed at different target- or job-oriented goals. Thus, DrugSynthMC is expected to enable the functional assessment of large compound libraries covering an extensive novel chemical space, overcoming the limitations of existing drug collections. The software is available at https://github.com/RoucairolMilo/DrugSynthMC.

Examining sialic acid derivatives as potential inhibitors of SARS-CoV-2 spike protein receptor binding domain

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has been the primary reason behind the COVID-19 global pandemic which has affected millions of lives worldwide. The fundamental cause of the infection is the molecular binding of the viral spike protein receptor binding domain (SP-RBD) with the human cell angiotensin-converting enzyme 2 (ACE2) receptor. The infection can be prevented if the binding of RBD-ACE2 is resisted by utilizing certain inhibitors or drugs that demonstrate strong binding affinity towards the SP RBD. Sialic acid based glycans found widely in human cells and tissues have notable propensity of binding to viral proteins of the coronaviridae family. Recent experimental literature have used N-acetyl neuraminic acid (Sialic acid) to create diagnostic sensors for SARS-CoV-2, but a detailed interrogation of the underlying molecular mechanisms is warranted. Here, we perform all atom molecular dynamics (MD) simulations for the complexes of certain Sialic acid-based molecules with that of SP RBD of SARS CoV-2. Our results indicate that Sialic acid not only reproduces a binding affinity comparable to the RBD-ACE2 interactions, it also assumes the longest time to dissociate completely from the protein binding pocket of SP RBD. Our predictions corroborate that a combination of electrostatic and van der Waals energies as well the polar hydrogen bond interactions between the RBD residues and the inhibitors influence free energy of binding.Communicated by Ramaswamy H. Sarma.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Targeting the Spike: Repurposing Mithramycin and Dihydroergotamine to Block SARS-CoV-2 Infection

The urgency to find complementary therapies to current SARS-CoV-2 vaccines, whose effectiveness is preserved over time and not compromised by the emergence of new and emerging variants, has become a critical health challenge. We investigate the possibility of jamming the opening of the Receptor Binding Domain (RBD) of the spike protein of SARS-CoV-2 with small compounds. Through in silico screening, we identified two potential candidates that would lock the Receptor Binding Domain (RBD) in a closed configuration, preventing the virus from infecting the host cells. We show that two drugs already approved by the FDA, mithramycin and dihydroergotamine, can block infection using concentrations in the μM range in cell-based assays. Further STD-NMR experiments support dihydroergotamine's direct interaction with the spike protein. Overall, our results indicate that repurposing of these compounds might lead to potential clinical drug candidates for the treatment of SARS-CoV-2 infection.

Exploring the disruption of SARS-CoV-2 RBD binding to hACE2

The COVID-19 pandemic was declared due to the spread of the novel coronavirus, SARS-CoV-2. Viral infection is caused by the interaction between the SARS-CoV-2 receptor binding domain (RBD) and the human ACE2 receptor (hACE2). Previous computational studies have identified repurposed small molecules that target the RBD, but very few have screened drugs in the RBD-hACE2 interface. When studies focus solely on the binding affinity between the drug and the RBD, they ignore the effect of hACE2, resulting in an incomplete analysis. We screened ACE inhibitors and previously identified SARS-CoV-2 inhibitors for binding to the RBD-hACE2 interface, and then conducted 500 ns of unrestrained molecular dynamics (MD) simulations of fosinopril, fosinoprilat, lisinopril, emodin, diquafosol, and physcion bound to the interface to assess the binding characteristics of these ligands. Based on MM-GBSA analysis, all six ligands bind favorably in the interface and inhibit the RBD-hACE2 interaction. However, when we repeat our simulation by first binding the drug to the RBD before interacting with hACE2, we find that fosinopril, fosinoprilat, and lisinopril result in a strongly interacting trimeric complex (RBD-drug-hACE2). Hydrogen bonding and pairwise decomposition analyses further suggest that fosinopril is the best RBD inhibitor. However, when lisinopril is bound, it stabilizes the trimeric complex and, therefore, is not an ideal potential drug candidate. Overall, these results reveal important atomistic interactions critical to the binding of the RBD to hACE2 and highlight the significance of including all protein partners in the evaluation of a potential drug candidate.

Identification of inhibitors against SARS-CoV-2 variants of concern using virtual screening and metadynamics-based enhanced sampling

Among the variants of SARS-CoV-2, some are more infectious than the Wild-type. Interestingly, these mutations enable the virus to evade the therapeutic efforts. Hence, there is a need for candidate drug molecules that can potently bind with all the variants. We have adopted a strategy combining virtual screening, molecular docking followed by rigorous sampling by metadynamics simulations to find candidate molecules. From our results we found four highly potent drug candidates that can bind to the Spike-RBD of all the variants of the virus. Additionally, we also found that certain signature residues on the RBM region commonly bind to each of these inhibitors. Thus, our study not only gives information on the chemical compounds, but also residues on the proteins which could be targeted for future drug and vaccine development studies.

A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2

Minimizing the human and economic costs of the COVID-19 pandemic and future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a new computational pipeline for the rapid identification and characterization of binding sites in viral proteins along with the key chemical features, which we call chemotypes, of the compounds predicted to interact with those same sites. The composition of source organisms for the structural models associated with an individual binding site is used to assess the site's degree of structural conservation across different species, including other viruses and humans. We propose a search strategy for novel therapeutics that involves the selection of molecules preferentially containing the most structurally rich chemotypes identified by our algorithm. While we demonstrate the pipeline on SARS-CoV-2, it is generalizable to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate predicted structures can be constructed.

Molecular Insights into the Binding Behavior of Imidazolium Ionic Liquids to the Receptor Binding Domain of the SARS-CoV-2 Spike Protein

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is considered as a key target for the design and development of COVID-19 drugs and inhibitors. Due to their unique structure and properties, ionic liquids (ILs) have many special interactions with proteins, showing great potential in biomedicine. Nevertheless, few research studies have been carried out on ILs and the spike RBD protein. Here, we explore the interaction of ILs and the RBD protein through large-scale molecular dynamics simulations (4 μs in total). It was found that IL cations with long alkyl chain lengths (nchain) could spontaneously bind to the cavity region of the RBD protein. The longer the alkyl chain is, the stabler the cations bind to the protein. The binding free energy (ΔG) had the same trend, peaking at nchain = 12 with -101.19 kJ/mol. The cationic chain lengths and their fit to the pocket are decisive factors that influence the binding strength of cations and proteins. The cationic imidazole ring has a high contact frequency with phenylalanine and tryptophan, and the hydrophobic residues phenylalanine, valine, leucine, and isoleucine are the most interacting residues with side chains of cations. Meanwhile, through analysis of the interaction energy, the hydrophobic and π-π interactions are the main contributors to the high affinity between cations and the RBD protein. In addition, the long-chain ILs would also act on the protein through clustering. These studies not only provide insights into the molecular interaction between ILs and the RBD of SARS-CoV-2 but also contribute to the rational design of IL-based drugs, drug carriers, and selective inhibitors as a therapeutic for SARS-CoV-2.

In Silico Identification and Analysis of Potentially Bioactive Antiviral Phytochemicals against SARS-CoV-2: A Molecular Docking and Dynamics Simulation Approach

SARS-CoV-2, a deadly coronavirus sparked COVID-19 pandemic around the globe. With an increased mutation rate, this infectious agent is highly transmissible inducing an escalated rate of infections and death everywhere. Hence, the discovery of a viable antiviral therapy option is urgent. Computational approaches have offered a revolutionary framework to identify novel antimicrobial treatment regimens and allow a quicker, cost-effective, and productive conversion into the health center by evaluating preliminary and safety investigations. The primary purpose of this research was to find plausible plant-derived antiviral small molecules to halt the viral entrance into individuals by clogging the adherence of Spike protein with human ACE2 receptor and to suppress their genome replication by obstructing the activity of Nsp3 (Nonstructural protein 3) and 3CLpro (main protease). An in-house library of 1163 phytochemicals were selected from the NPASS and PubChem databases for downstream analysis. Preliminary analysis with SwissADME and pkCSM revealed 149 finest small molecules from the large dataset. Virtual screening using the molecular docking scoring and the MM-GBSA data analysis revealed that three candidate ligands CHEMBL503 (Lovastatin), CHEMBL490355 (Sulfuretin), and CHEMBL4216332 (Grayanoside A) successfully formed docked complex within the active site of human ACE2 receptor, Nsp3, and 3CLpro, respectively. Dual method molecular dynamics (MD) simulation and post-MD MM-GBSA further confirmed efficient binding and stable interaction between the ligands and target proteins. Furthermore, biological activity spectra and molecular target analysis revealed that all three preselected phytochemicals were biologically active and safe for human use. Throughout the adopted methodology, all three therapeutic candidates significantly outperformed the control drugs (Molnupiravir and Paxlovid). Finally, our research implies that these SARS-CoV-2 protein antagonists might be viable therapeutic options. At the same time, enough wet lab evaluations would be needed to ensure the therapeutic potency of the recommended drug candidates for SARS-CoV-2.

Computational Screening of Approved Drugs for Inhibition of the Antibiotic Resistance Gene mecA in Methicillin-Resistant Staphylococcus aureus (MRSA) Strains

Antibiotic resistance is a critical problem that results in a high morbidity and mortality rate. The process of discovering new chemotherapy and antibiotics is challenging, expensive, and time-consuming, with only a few getting approved for clinical use. Therefore, screening already-approved drugs to combat pathogens such as bacteria that cause serious infections in humans and animals is highly encouraged. In this work, we aim to identify approved antibiotics that can inhibit the mecA antibiotic resistance gene found in methicillin-resistant Staphylococcus aureus (MRSA) strains. The MecA protein sequence was utilized to perform a BLAST search against a drug database containing 4302 approved drugs. The results revealed that 50 medications, including known antibiotics for other bacterial strains, targeted the mecA antibiotic resistance gene. In addition, a structural similarity approach was employed to identify existing antibiotics for S. aureus, followed by molecular docking. The results of the docking experiment indicated that six drugs had a high binding affinity to the mecA antibiotic resistance gene. Furthermore, using the structural similarity strategy, it was discovered that afamelanotide, an approved drug with unclear antibiotic activity, had a strong binding affinity to the MRSA-MecA protein. These findings suggest that certain already-approved drugs have potential in chemotherapy against drug-resistant pathogenic bacteria, such as MRSA.

Identifying SARS-CoV-2 Drugs Binding to the Spike Fatty Acid Binding Pocket Using In Silico Docking and Molecular Dynamics

Drugs against novel targets are needed to treat COVID-19 patients, especially as SARS-CoV-2 is capable of rapid mutation. Structure-based de novo drug design and repurposing of drugs and natural products is a rational approach to discovering potentially effective therapies. These in silico simulations can quickly identify existing drugs with known safety profiles that can be repurposed for COVID-19 treatment. Here, we employ the newly identified spike protein free fatty acid binding pocket structure to identify repurposing candidates as potential SARS-CoV-2 therapies. Using a validated docking and molecular dynamics protocol effective at identifying repurposing candidates inhibiting other SARS-CoV-2 molecular targets, this study provides novel insights into the SARS-CoV-2 spike protein and its potential regulation by endogenous hormones and drugs. Some of the predicted repurposing candidates have already been demonstrated experimentally to inhibit SARS-CoV-2 activity, but most of the candidate drugs have yet to be tested for activity against the virus. We also elucidated a rationale for the effects of steroid and sex hormones and some vitamins on SARS-CoV-2 infection and COVID-19 recovery.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

Effect of an Inhibitor on the ACE2-Receptor-Binding Domain of SARS-CoV-2

The recent outbreak of COVID-19 infection started in Wuhan, China, and spread across China and beyond. Since the WHO declared COVID-19 a pandemic (March 11, 2020), three vaccines and only one antiviral drug (remdesivir) have been approved (Oct 22, 2020) by the FDA. The coronavirus enters human epithelial cells by the binding of the densely glycosylated fusion spike protein (S protein) to a receptor (angiotensin-converting enzyme 2, ACE2) on the host cell surface. Therefore, inhibiting the viral entry is a promising treatment pathway for preventing or ameliorating the effects of COVID-19 infection. In the current work, we have used all-atom molecular dynamics (MD) simulations to investigate the influence of the MLN-4760 inhibitor on the conformational properties of ACE2 and its interaction with the receptor-binding domain (RBD) of SARS-CoV-2. We have found that the presence of an inhibitor tends to completely/partially open the ACE2 receptor where the two subdomains (I and II) move away from each other, while the absence results in partial or complete closure. The current study increases our understanding of ACE inhibition by MLN-4760 and how it modulates the conformational properties of ACE2.

Protein degradation: a novel computational approach to design protein degrader probes for main protease of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has afflicted many lives and led to approvals of drugs and vaccines for emergency use. Even though vaccines have emerged, the high mortality of COVID-19 and its insurgent proliferation throughout the masses commands an innovative therapeutic proposition for the treatment. Targeted protein degradation has been applied to various disease domains and we propose that it could be incredibly beneficial to tackle the current pandemic. In this study, we have attempted to furnish insights on the design of suitable PROTACs for the main protease (M^pro) of SARS-CoV-2, a protein that is considered to be an essential target for viral replication. We have employed protein-protein docking to predict the possible complementarity between a cereblon E3 ligase and M^pro of SARS-CoV-2, and estimate possible linker length. Molecular Dynamic simulation and analysis on generated ternary complexes demonstrated stable interactions that suggested that designed PROTAC has a potential to cause degradation. The superior characteristics rendered by PROTACS led us to propose them as possibly the next-generation antiviral drugs for SARS-CoV-2.

Integrated docking and enhanced sampling-based selection of repurposing drugs for SARS-CoV-2 by targeting host dependent factors

Since the onset of global pandemic, the most focused research currently in progress is the development of potential drug candidates and clinical trials of existing FDA approved drugs for other relevant diseases, in order to repurpose them for the COVID-19. At the same time, several high throughput screenings of drugs have been reported to inhibit the viral components during the early course of infection but with little proven efficacies. Here, we investigate the drug repurposing strategies to counteract the coronavirus infection which involves several potential targetable host proteins involved in viral replication and disease progression. We report the high throughput analysis of literature-derived repurposing drug candidates that can be used to target the genetic regulators known to interact with viral proteins based on experimental and interactome studies. In this work we have performed integrated molecular docking followed by molecular dynamics (MD) simulations and free energy calculations through an expedite in silico process where the number of screened candidates reduces sequentially at every step based on physicochemical interactions. We elucidate that in addition to the pre-clinical and FDA approved drugs that targets specific regulatory proteins, a range of chemical compounds (Nafamostat, Chloramphenicol, Ponatinib) binds to the other gene transcription and translation regulatory proteins with higher affinity and may harbour potential for therapeutic uses. There is a rapid growing interest in the development of combination therapy for COVID-19 to target multiple enzymes/pathways. Our in silico approach would be useful in generating leads for experimental screening for rapid drug repurposing against SARS-CoV-2 interacting host proteins.Communicated by Ramaswamy H. Sarma.

Broad Tricyclic Ring Inhibitors Block SARS-CoV-2 Spike Function Required for Viral Entry

The entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into host cells requires binding of the viral spike glycoprotein to the angiotensin-converting enzyme 2 (ACE2) receptor, which triggers subsequent conformational changes to facilitate viral and cellular fusion at the plasma membrane or following endocytosis. Here, we experimentally identified selective and broad inhibitors of SARS-CoV-2 entry that share a tricyclic ring (or similar) structure. The inhibitory effect was restricted to early steps during infection and the entry inhibitors interacted with the receptor binding domain of the SARS-CoV-2 spike but did not significantly interfere with receptor (ACE2) binding. Instead, some of these compounds induced conformational changes or affected spike assembly and blocked SARS-CoV-2 spike cell-cell fusion activity. The broad inhibitors define a highly conserved binding pocket that is present on the spikes of SARS-CoV-1, SARS-CoV-2, and all circulating SARS-CoV-2 variants tested and block SARS-CoV spike activity required for mediating viral entry. These compounds provide new insights into the SARS-CoV-2 spike topography, as well as into critical steps on the entry pathway, and can serve as lead candidates for the development of broad-range entry inhibitors against SARS-CoVs.

An in-silico investigation of potential natural polyphenols for the targeting of COVID main protease inhibitor

The deadliest recent pandemic outbreak of COVID-19 disease has severely damaged the socio-economic health of the people globally. Due to unavailability of any effective vaccine or treatment the human beings are still struggling to overcome the pandemic condition. In an attempt to discover anti-COVID molecule, we used in-silico approach and reported 160 natural polyphenols to identify the most promising druggable HITs that can further used for drug discovery process. The co-crystallized structure COVID protease enzyme (PDB id 6LU7) was used. HTVS, MD simulation, binding energy calculations and in-silico ADME calculation were done and analyzed. Depending upon the scores three compounds galangin, nalsudaldain and rhamnezine were identified and the docking score were found to be -7.704, -6.51, -4.212 respectively. These docked complexes were further subjected to MD simulation runs over a 100 ns time and the RMSD and RMSF values were determined. The RMSD values of three compounds were found to be 2.9 Å, 7.6 Å & 9.5 Å respectively and the lowest RMSF values suggested the steady stability of ligand-protein complexes. The binding free energies (ΔG) of compounds with protein were found to be -49.8, -56.45, -62.87 kJ/mole. Moreover, in-silico ADME calculations indicated the drug likeliness properties of these molecules. By considering all these in-silico results the identified HITs would be the most probable anti-COVID drug molecules that can be further taken in wet lab and can act as lead for development of newer inhibitor of COVID-19 main protease enzyme.

Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies

Background

Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19.

Methods

A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure.

Results

A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2.

Conclusion

The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.

Molecular dynamics studies reveal structural and functional features of the SARS-CoV-2 spike protein

The SARS-CoV-2 virus is responsible for the COVID-19 pandemic the world experience since 2019. The protein responsible for the first steps of cell invasion, the spike protein, has probably received the most attention in light of its central role during infection. Computational approaches are among the tools employed by the scientific community in the enormous effort to study this new affliction. One of these methods, namely molecular dynamics (MD), has been used to characterize the function of the spike protein at the atomic level and unveil its structural features from a dynamic perspective. In this review, we focus on these main findings, including spike protein flexibility, rare S protein conformational changes, cryptic epitopes, the role of glycans, drug repurposing, and the effect of spike protein variants.

DNA aptamers inhibit SARS-CoV-2 spike-protein binding to hACE2 by an RBD- independent or dependent approach

Objective: Nobody knows when the COVID-19 pandemic will end or when and where the next coronavirus will outbreak. Therefore, it is still necessary to develop SARS-CoV-2 inhibitors for different variants or even the new coronavirus. Since SARS-CoV-2 uses its surface spike-protein to recognize hACE2, mediating its entry into cells, ligands that can specifically recognize the spike-protein have the potential to prevent infection. Methods: We have recently discovered DNA aptamers against the S2-domain of the WT spike-protein by exploiting the selection process called SELEX. After optimization, among all candidates, the aptamer S2A2C1 has the shortest sequence and the best binding affinity toward the S2-protein. More importantly, the S2A2C1 aptamer does not bind to the RBD of the spike-protein, but it efficiently blocks the spike-protein/hACE2 interaction, suggesting an RBD-independent inhibition approach. To further improve its performance, we conjugated the S2A2C1 aptamer with a reported anti-RBD aptamer, S1B6C3, using various linkers and constructed hetero-bivalent fusion aptamers. Binding affinities of mono and fusion aptamers against the spike-proteins were measured. The inhibition efficacies of mono and fusion aptamers to prevent the hACE2/spike-protein interaction were determined using ELISA. Results: Anti-spike-protein aptamers, including S2A2C1 and S1B6C3-A5-S2A2C1, maintained high binding affinity toward the WT, Delta, and Omicron spike-proteins and high inhibition efficacies to prevent them from binding to hACE2, rendering them well-suited as diagnostic and therapeutic molecular tools to target SARS-CoV-2 and its variants. Conclusions: Overall, we discovered the anti-S2 aptamer, S2A2C1, which inhibits the hACE2/spike-protein interaction via an RBD-independent approach. The anti-S2 and anti-RBD aptamers were conjugated to obtain the fusion aptamer, S1B6C3-A5-S2A2C1, which recognizes the spike-protein by an RBD-dependent approach. Our strategies, which discovered aptamer inhibitors targeting the highly conserved S2-protein, as well as the design of fusion aptamers, can be used to target new coronaviruses as they emerge.

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.

In Silico Screening of Novel TMPRSS2 Inhibitors for Treatment of COVID-19

COVID-19, a pandemic caused by the virus SARS-CoV-2, has spread globally, necessitating the search for antiviral compounds. Transmembrane protease serine 2 (TMPRSS2) is a cell surface protease that plays an essential role in SARS-CoV-2 infection. Therefore, researchers are searching for TMPRSS2 inhibitors that can be used for the treatment of COVID-19. As such, in this study, based on the crystal structure, we targeted the active site of TMPRSS2 for virtual screening of compounds in the FDA database. Then, we screened lumacaftor and ergotamine, which showed strong binding ability, using 100 ns molecular dynamics simulations to study the stability of the protein–ligand binding process, the flexibility of amino acid residues, and the formation of hydrogen bonds. Subsequently, we calculated the binding free energy of the protein–ligand complex by the MM-PBSA method. The results show that lumacaftor and ergotamine interact with residues around the TMPRSS2 active site, and reached equilibrium in the 100 ns molecular dynamics simulations. We think that lumacaftor and ergotamine, which we screened through in silico studies, can effectively inhibit the activity of TMPRSS2. Our findings provide a basis for subsequent in vitro experiments, having important implications for the development of effective anti-COVID-19 drugs.

Maturation of the SARS-CoV-2 virus is regulated by dimerization of its main protease

SARS-CoV-2 main protease (M^pro) involved in COVID-19 is required for maturation of the virus and infection of host cells. The key question is how to block the activity of M^pro. By combining atomistic simulations with machine learning, we found that the enzyme regulates its own activity by a collective allosteric mechanism that involves dimerization and binding of a single substrate. At the core of the collective mechanism is the coupling between the catalytic site residues, H41 and C145, which direct the activity of M^pro dimer, and two salt bridges formed between R4 and E290 at the dimer interface. If these salt bridges are mutated, the activity of M^pro is blocked. The results suggest that dimerization of main proteases is a general mechanism to foster coronavirus proliferation, and propose a robust drug-based strategy that does not depend on the frequently mutating spike proteins at the viral envelope used to develop vaccines.

Venetoclax Decreases the Expression of the Spike Protein through Amino Acids Q493 and S494 in SARS-CoV-2

The new coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has been reported and spread globally. There is an urgent need to take urgent measures to treat and prevent further infection of this virus. Here, we use virtual drug screening to establish pharmacophore groups and analyze the ACE2 binding site of the spike protein with the ZINC drug database and DrugBank database by molecular docking and molecular dynamics simulations. Screening results showed that Venetoclax, a treatment drug for chronic lymphocytic leukemia, has a potential ability to bind to the spike protein of SARS-CoV-2. In addition, our in vitro study found that Venetoclax degraded the expression of the spike protein of SARS-CoV-2 through amino acids Q493 and S494 and blocked the interaction with the ACE2 receptor. Our results suggest that Venetoclax is a candidate for clinical prevention and treatment and deserves further research.

DeepVir: Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning-Application to COVID-19

This study formulates antiviral repositioning as a matrix completion problem wherein the antiviral drugs are along the rows and the viruses are along the columns. The input matrix is partially filled, with ones in positions where the antiviral drug has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) are encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion (GRMC) problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results of our curated RNA drug-virus association data set show that the proposed approach excels over state-of-the-art GRMC techniques. When applied to in silico prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under trials for the same.

Predicting global diet-disease relationships at the atomic level: a COVID-19 case study

Over the past few months, numerous studies harnessed in silico methods such as molecular docking to evaluate food compounds for inhibitory activity against coronavirus infection and replication. These studies capitalize on the efficiency of computational methods to quickly guide subsequent research and examine diet-disease relationships, and their sudden widespread utility may signal new opportunities for future antiviral and bioactive food research. Using Coronavirus Disease 2019 (COVID-19) research as a case study, we herein provide an overview of findings from studies using molecular docking to study food compounds as potential inhibitors of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), explore considerations for the critical interpretation of study findings, and discuss how these studies help shape larger conversations of diet and disease.

In Silico Multi-Target Approach Revealed Potential Lead Compounds as Scaffold for the Synthesis of Chemical Analogues Targeting SARS-CoV-2

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), an infectious disease that spreads rapidly in humans. In March 2020, the World Health Organization (WHO) declared a COVID-19 pandemic. Identifying a multi-target-directed ligand approach would open up new opportunities for drug discovery to combat COVID-19. The aim of this work was to perform a virtual screening of an exclusive chemical library of about 1700 molecules containing both pharmacologically active compounds and synthetic intermediates to propose potential protein inhibitors for use against SARS-CoV-2. In silico analysis showed that our compounds triggered an interaction network with key residues of the SARS-CoV-2 spike protein (S-protein), blocking trimer formation and interaction with the human receptor hACE2, as well as with the main 3C-like protease (3CLpro), inhibiting their biological function. Our data may represent a step forward in the search for potential new chemotherapeutic agents for the treatment of COVID-19.

Accelerating the discovery of the beyond rule of five compounds that have high affinities toward SARS-CoV-2 spike RBD

The battle against SARS-CoV-2 coronavirus is the focal point for the global pandemic that has affected millions of lives worldwide. The need for effective and selective therapeutics for the treatment of the disease caused by SARS-CoV-2 is critical. Herein, we performed a hierarchical computational approach incorporating molecular docking studies, molecular dynamics simulations, absolute binding energy calculations, and steered molecular dynamics simulations for the discovery of potential compounds with high affinity towards SARS-CoV-2 spike RBD. By leveraging ZINC15 database, a total of 1282 in-clinical and FDA approved drugs were filtered out from nearly 0.5 million protomers of relatively large compounds (MW > 500, and LogP ≤ 5). Our results depict plausible mechanistic aspects related to the blockage of SARS-CoV-2 spike RBD by the top hits discovered. We found that the most promising candidates, namely, ZINC95628821, ZINC95617623, ZINC3979524, and ZINC261494658, strongly bind to the spike RBD and interfere with the human ACE2 receptor. These findings accelerate the rational design of selective inhibitors targeting the spike RBD protein of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Development and evaluation of peptidomimetic compounds against SARS-CoV-2 spike protein: an in silico and in vitro study

Background: Coronavirus disease 2019 (COVID‐19) as global pandemic disease has been adversely affecting public health and social life with considerable loss of human life worldwide. Therefore, there is an urgent need for developing novel therapeutics to combat COVID‐19. The causative agent of COVID‐19 is SARS‐CoV‐2 which targets human angiotensin converting enzyme 2 (ACE2) as cellular receptor via its spike (S) protein. In this context, interfering with the binding of SARS‐CoV‐2 S protein to target molecules could provide a promising strategy to find novel therapeutic agents against SARS‐CoV‐2. The purpose of the current study was to identify potential peptidomimetics against S protein with a combination of structure‐based virtual screening methods and in vitro assays. Methods: The candidates were inspected in terms of ADME properties, drug‐likeness, as well as toxicity profiles. Additionally, molecular docking and dynamics simulations were performed to predict binding of the studied ligands to spike protein. Results: Biological evaluation of the compounds revealed that PM2 molecule exhibits some antiviral activity. Conclusion: In summary, this study highlights the importance of combining in silico and in vitro techniques in order to identify antiviral compound to tackle COVID‐19 and presents a new scaffold that may be structurally optimized for improved antiviral activity.

Clinically available/under trial drugs and vaccines for treatment of SARS-COV-2

Prior 2019 to work date entire world is seriously influenced by an appalling illness called COVID sickness [Coronavirus disease-2019 (COVID-19)] which is brought about by another strain of coronavirus known as severe acute respiratory syndrome-Coronavirus-2. This pandemic was first seen in the Hubei area in Wuhan city of China. To date above 170 million individuals have been influenced by this infection and more than 3 million individuals died. The race of finding specific therapeutic drugs and efficacious vaccine candidates is still going on to tackle the pandemic-associated morbidities. This chapter discussed different clinically accessible medications (remdesivir, hydroxychloroquine, azithromycin, etc.) and immunizations (mRNA-1273, Sputanik, Pfizer, etc.) which are either in use or under trial for the treatment of COVID-19.

Atazanavir Is a Competitive Inhibitor of SARS-CoV-2 Mpro, Impairing Variants Replication In Vitro and In Vivo

Atazanavir (ATV) has already been considered as a potential repurposing drug to 2019 coronavirus disease (COVID-19); however, there are controversial reports on its mechanism of action and effectiveness as anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Through the pre-clinical chain of experiments: enzymatic, molecular docking, cell-based and in vivo assays, it is demonstrated here that both SARS-CoV-2 B.1 lineage and variant of concern gamma are susceptible to this antiretroviral. Enzymatic assays and molecular docking calculations showed that SARS-CoV-2 main protease (M^pro) was inhibited by ATV, with Morrison’s inhibitory constant (K_i) 1.5-fold higher than GC376 (a positive control) dependent of the catalytic water (H₂O_cat) content. ATV was a competitive inhibitor, increasing the M^pro’s Michaelis–Menten (K_m) more than sixfold. Cell-based assays indicated that different lineages of SARS-CoV-2 is susceptible to ATV. Using oral administration of ATV in mice to reach plasmatic exposure similar to humans, transgenic mice expression in human angiotensin converting enzyme 2 (K18-hACE2) were partially protected against lethal challenge with SARS-CoV-2 gamma. Moreover, less cell death and inflammation were observed in the lung from infected and treated mice. Our studies may contribute to a better comprehension of the M^pro/ATV interaction, which could pave the way to the development of specific inhibitors of this viral protease.

Effects of simeprevir on the replication of SARS-CoV-2 in vitro and in transgenic hACE2 mice

In a bid to contain the current COVID-19 (coronavirus disease 2019) pandemic, various countermeasures have been applied. To date, however, there is a lack of an effective drug for the treatment of COVID-19. Through molecular modelling studies, simeprevir, a protease inhibitor approved for the management of hepatitis C virus infection, has been predicted as a potential antiviral against SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the causative agent of COVID-19. Here we assessed the efficacy of simeprevir against SARS-CoV-2 both in vitro in Vero E6 cells and in vivo in a human angiotensin-converting enzyme 2 (hACE2) transgenic mouse model. The results showed that simeprevir could inhibit SARS-CoV-2 replication in Vero E6 cells with a half-maximal effective concentration (EC₅₀) of 1.41 ± 0.12 μM. In a transgenic hACE2 mouse model of SARS-CoV-2 infection, intraperitoneal administration of simeprevir at 10 mg/kg/day for 3 consecutive days failed to suppress viral replication. These findings collectively imply that simeprevir does not inhibit SARS-CoV-2 in vivo and therefore do not support its application as a treatment against COVID-19 at a dosage of 10 mg/kg/day.

Proteomics advances towards developing SARS-CoV-2 therapeutics using in silico drug repurposing approaches

Standing amidst the COVID-19 pandemic, we have faced major medical and economic crisis in recent times which remains to be an unresolved issue till date. Although the scientific community has made significant progress towards diagnosis and understanding the disease; however, effective therapeutics are still lacking. Several omics-based studies, especially proteomics and interactomics, have contributed significantly in terms of identifying biomarker panels that can potentially be used for the disease prognosis. This has also paved the way to identify the targets for drug repurposing as a therapeutic alternative. US Food and Drug Administration (FDA) has set in motion more than 500 drug development programs on an emergency basis, most of them are focusing on repurposed drugs. Remdesivir is one such success of a robust and quick drug repurposing approach. The advancements in omics-based technologies has allowed to explore altered host proteins, which were earlier restricted to only SARS-CoV-2 protein signatures. In this article, we have reviewed major contributions of proteomics and interactomics techniques towards identifying therapeutic targets for COVID-19. Furthermore, in-silico molecular docking approaches to streamline potential drug candidates are also discussed.

Network analytics for drug repurposing in COVID-19

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein–protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.

Active site-specific quantum tunneling of hACE2 receptor to assess its complexing poses with selective bioactive compounds in co-suppressing SARS-CoV-2 influx and subsequent cardiac injury

OBJECTIVE

This research aims to study the target specificity of selective bioactive compounds in complexing with the human angiotensin-converting enzyme (hACE2) receptor to impede the severe acute respiratory syndrome coronavirus 2 influx mechanism resulting in cardiac injury and depending on the receptor's active site properties and quantum tunneling.

MATERIALS AND METHODS

A library of 120 phytochemical ligands was prepared, from which 5 were selected considering their absorption, distribution, metabolism, and excretion (ADMET) and quantitative structure-activity relationship (QSAR) profiles. The protein active sites and belonging quantum tunnels were defined to conduct supramolecular docking of the aforementioned ligands. The hydrogen bond formation and hydrophobic interactions between the ligand-receptor complexes were studied following the molecular docking steps. A comprehensive molecular dynamic simulation (MDS) was conducted for each of the ligand-receptor complexes to figure out the values - root mean square deviation (RMSD) (Å), root mean square fluctuation (RMSF) (Å), H-bonds, Cα, solvent accessible surface area (SASA) (Å2), molecular surface area (MolSA) (Å2), Rg (nm), and polar surface area (PSA) (Å). Finally, computational programming and algorithms were used to interpret the dynamic simulation outputs into their graphical quantitative forms.

RESULTS

ADMET and QSAR profiles revealed that the most active candidates from the library to be used were apigenin, isovitexin, piperolactam A, and quercetin as test ligands, whereas serpentine as the control. Based on the binding affinities of supramolecular docking and the parameters of molecular dynamic simulation, the strength of the test ligands can be classified as isovitexin > quercetin > piperolactam A > apigenin when complexed with the hACE2 receptor. Surprisingly, serpentine showed lower affinity (-8.6 kcal/mol) than that of isovitexin (-9.9 kcal/mol) and quercetin (-8.9 kcal/mol). The MDS analysis revealed all ligands except isovitexin having a value lower than 2.5 Ǻ. All the test ligands exhibited acceptable fluctuation ranges of RMSD (Å), RMSF (Å), H-bonds, Cα, SASA (Å2), MolSA (Å2), Rg (nm), and PSA (Å) values.

CONCLUSION

Considering each of the parameters of molecular optimization, docking, and dynamic simulation interventions, all of the test ligands can be suggested as potential targeted drugs in blocking the hACE2 receptor.

COVID-19 and RAAS inhibitors: is there a final conclusion?

Coronavirus disease 2019 (COVID-19), the first pandemic caused by a human infecting coronavirus, has drawn global attention from the first time it appeared in Wuhan city of China in late December 2019. Detection of the responsible viral pathogen, named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by WHO, and its possible pathogenesis lead to the forming of many hypotheses about the factors that may affect the patients' outcome. One of the SARS-CoV-2 infection concerns was the potential role of angiotensin-converting enzyme (ACE) inhibitors and angiotensin-receptor blockers (ARBs) in COVID-19 patients' morbidity and mortality. Studies demonstrated that because SARS-CoV-2 uses human ACE2 cell receptors as an entry receptor to invade the cells, there might be an association between antihypertensive drugs such as RAAS inhibitors (specifically ACEIs and ARBs) and the COVID-19 disease. Data are scarce and conflicting regarding ACEI or ARB consumption and how it influences disease outcomes, and a single conclusion has not been reached yet. According to the literature review in our article, the most evidentially supported theory about the use of RAAS inhibitors in COVID-19 is that these medications, including ACEI/ARB, are not associated with the increased risk of infection, disease severity, and patient prognosis. However, further studies are needed to support the hypothesis.

Advances in the computational landscape for repurposed drugs against COVID-19

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.

Genomics-guided targeting of stress granule proteins G3BP1/2 to inhibit SARS-CoV-2 propagation

SARS-CoV-2 nucleocapsid (N) protein undergoes RNA-induced phase separation (LLPS) and sequesters the host key stress granule (SG) proteins, Ras-GTPase-activating protein SH3-domain-binding protein 1 and 2 (G3BP1 and G3BP2) to inhibit SG formation. This will allow viral packaging and propagation in host cells. Based on a genomic-guided meta-analysis, here we identify upstream regulatory elements modulating the expression of G3BP1 and G3BP2 (collectively called G3BP1/2). Using this strategy, we have identified FOXA1, YY1, SYK, E2F-1, and TGFBR2 as activators and SIN3A, SRF, and AKT-1 as repressors of G3BP1/2 genes. Panels of the activators and repressors were then used to identify drugs that change their gene expression signatures. Two drugs, imatinib, and decitabine have been identified as putative modulators of G3BP1/2 genes and their regulators, suggesting their role as COVID-19 mitigation agents. Molecular docking analysis suggests that both drugs bind to G3BP1/2 with a much higher affinity than the SARS-CoV-2 N protein. This study reports imatinib and decitabine as candidate drugs against N protein and G3BP1/2 protein.

Therapeutic approaches for SARS-CoV-2 infection

Therapeutic approaches to COVID-19 treatment require appropriate inhibitors to target crucial proteins of SARS-CoV-2 replication machinery. It's been approximately 12 months since the pandemic started, yet no known specific drugs are available. However, research progresses with time in terms of high throughput virtual screening (HTVS) and rational design of repurposed, novel synthetic and natural products discovery by understanding the viral life cycle, immuno-pathological and clinical outcomes in patients based on host's nutritional, metabolic, and lifestyle status. Further, complementary and alternative medicine (CAM) approaches have also improved resiliency and immune responses. In this article, we summarize all the therapeutic antiviral strategies for COVID-19 drug discovery including computer aided virtual screening, repurposed drugs, immunomodulators, vaccines, plasma therapy, various adjunct therapies, and phage technology to unravel insightful mechanistic pathways of targeting SARS-CoV-2 and host's intrinsic, innate immunity at multiple checkpoints that aid in the containment of the disease.

In Silico Identification and Validation of Organic Triazole Based Ligands as Potential Inhibitory Drug Compounds of SARS-CoV-2 Main Protease

The SARS-CoV-2 virus is highly contagious to humans and has caused a pandemic of global proportions. Despite worldwide research efforts, efficient targeted therapies against the virus are still lacking. With the ready availability of the macromolecular structures of coronavirus and its known variants, the search for anti-SARS-CoV-2 therapeutics through in silico analysis has become a highly promising field of research. In this study, we investigate the inhibiting potentialities of triazole-based compounds against the SARS-CoV-2 main protease (Mpro). The SARS-CoV-2 main protease (Mpro) is known to play a prominent role in the processing of polyproteins that are translated from the viral RNA. Compounds were pre-screened from 171 candidates (collected from the DrugBank database). The results showed that four candidates (Bemcentinib, Bisoctrizole, PYIITM, and NIPFC) had high binding affinity values and had the potential to interrupt the main protease (Mpro) activities of the SARS-CoV-2 virus. The pharmacokinetic parameters of these candidates were assessed and through molecular dynamic (MD) simulation their stability, interaction, and conformation were analyzed. In summary, this study identified the most suitable compounds for targeting Mpro, and we recommend using these compounds as potential drug molecules against SARS-CoV-2 after follow up studies.

Dietary stigmastane-type saponins as promising dual-target directed inhibitors of SARS-CoV-2 proteases: a structure-based screening

Despite the development of COVID-19 vaccines, at present, there is still no approved antiviral drug against the pandemic. The SARS-CoV-2 3-chymotrypsin-like proteases (S-3CLpro) and papain-like protease (S-PLpro) are essential for the viral proliferation cycle, hence attractive drug targets. Plant-based dietary components that have been extensively reported for antiviral activities may serve as cheap sources of preventive nutraceuticals and/or antiviral drugs. A custom-made library of 176 phytochemicals from five West African antiviral culinary herbs was screened for potential dual-target-directed inhibitors of S-3CLpro and S-PLpro in silico. The docking analysis revealed fifteen steroidal saponins (FSS) from Vernonia amygdalina with the highest binding tendency for the active sites of S-3CLpro and S-PLpro. In an optimized docking analysis, the FSS were further docked against four equilibrated conformers of the S-3CLpro and S-PLpro. Three stigmastane-type steroidal saponins (vernonioside A2, vernonioside A4 and vernonioside D2) were revealed as the lead compounds. These compounds interacted with the catalytic residues of both S-3CLpro and S-PLpro, thereby exhibiting dual inhibitory potential against these SARS-CoV-2 cysteine proteases. The binding free energy calculations further corroborated the static and optimized docking analysis. The complexed proteases with these promising phytochemicals were stable during a full atomistic MD simulation while the phytochemicals exhibited favourable physicochemical and ADMET properties, hence, recommended as promising inhibitors of SARS-CoV-2 cysteine proteases.

SARS-CoV-2 spike protein and RNA dependent RNA polymerase as targets for drug and vaccine development: A review

The present pandemic has posed a crisis to the economy of the world and the health sector. Therefore, the race to expand research to understand some good molecular targets for vaccine and therapeutic development for SARS-CoV-2 is inevitable. The newly discovered coronavirus 2019 (COVID-19) is a positive sense, single-stranded RNA, and enveloped virus, assigned to the beta CoV genus. The virus (SARS-CoV-2) is more infectious than the previously detected coronaviruses (MERS and SARS). Findings from many studies have revealed that S protein and RdRp are good targets for drug repositioning, novel therapeutic development (antibodies and small molecule drugs), and vaccine discovery. Therapeutics such as chloroquine, convalescent plasma, monoclonal antibodies, spike binding peptides, and small molecules could alter the ability of S protein to bind to the ACE-2 receptor, and drugs such as remdesivir (targeting SARS-CoV-2 RdRp), favipir, and emetine could prevent SASR-CoV-2 RNA synthesis. The novel vaccines such as mRNA1273 (Moderna), 3LNP-mRNAs (Pfizer/BioNTech), and ChAdOx1-S (University of Oxford/Astra Zeneca) targeting S protein have proven to be effective in combating the present pandemic. Further exploration of the potential of S protein and RdRp is crucial in fighting the present pandemic.

Fluoxetine hydrochloride loaded lipid polymer hybrid nanoparticles showed possible efficiency against SARS-CoV-2 infection

Up to date, there were no approved drugs against coronavirus (COVID-19) disease that dangerously affects global health and the economy. Repurposing the existing drugs would be a promising approach for COVID-19 management. The antidepressant drugs, selective serotonin reuptake inhibitors (SSRIs) class, have antiviral, anti-inflammatory, and anticoagulant effects, which makes them auspicious drugs for COVID 19 treatment. Therefore, this study aimed to predict the possible therapeutic activity of SSRIs against COVID-19. Firstly, molecular docking studies were performed to hypothesize the possible interaction of SSRIs to the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-COV-2) main protease. Secondly, the candidate drug was loaded in lipid polymer hybrid (LPH) nanoparticles to enhance its activity. The studied SSRIs were Fluoxetine hydrochloride (FH), Atomoxteine, Paroxetine, Nisoxteine, Repoxteine RR, and Repoxteine SS. Interestingly, FH could effectively bind with SARS-COV-2 main protease via hydrogen bond formation with low binding energy (-6.7 kcal/mol). Moreover, the optimization of FH-LPH formulation achieved 65.1 ± 2.7% encapsulation efficiency, 10.3 ± 0.4% loading efficiency, 98.5 ± 3.5 nm particle size, and -10.5 ± 0.45 mV zeta potential. Additionally, it improved cellular internalization in a time-dependent manner with good biocompatibility on Human lung fibroblast (CCD-19Lu) cells. Therefore, the study suggested the potential activity of FH-LPH nanoparticles against the COVID-19 pandemic.

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.

Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis

Viral epidemics and pandemics are considered public health emergencies. However, traditional and novel antiviral discovery approaches are unable to mitigate them in a timely manner. Notably, drug repurposing emerged as an alternative strategy to provide antiviral solutions in a timely and cost-effective manner. In the literature, many FDA-approved drugs have been repurposed to inhibit viruses, while a few among them have also entered clinical trials. Using experimental data, we identified repurposed drugs against 14 viruses responsible for causing epidemics and pandemics such as SARS-CoV-2, SARS, Middle East respiratory syndrome, influenza H1N1, Ebola, Zika, Nipah, chikungunya, and others. We developed a novel computational "drug-target-drug" approach that uses the drug-targets extracted for specific drugs, which are experimentally validated in vitro or in vivo for antiviral activity. Furthermore, these extracted drug-targets were used to fetch the novel FDA-approved drugs for each virus and prioritize them by calculating their confidence scores. Pathway analysis showed that the majority of the extracted targets are involved in cancer and signaling pathways. For SARS-CoV-2, our method identified 21 potential repurposed drugs, of which 7 (e.g., baricitinib, ramipril, chlorpromazine, enalaprilat, etc.) have already entered clinical trials. The prioritized drug candidates were further validated using a molecular docking approach. Therefore, we anticipate success during the experimental validation of our predicted FDA-approved repurposed drugs against 14 viruses. This study will assist the scientific community in hastening research aimed at the development of antiviral therapeutics.