Potential covalent drugs targeting the main protease of the SARS-CoV-2 coronavirus

Identification of prospective covalent inhibitors for SARS-CoV-2 main protease using structure-based approach

The rapid global spread of SARS-CoV-2 has recently caused havoc and forced the world into a state of the pandemic causing respiratory, gastrointestinal, hepatic, and neurologic diseases. It persistently, through mutation, develops into new variants of the virus that have appeared over time. As main protease (Mpro) is involved in proteolysis of two overlapping polyproteins pp1a and pp1ab to produce 16 non-structural proteins having a paramount factor in the virus replication that have a cysteine-histidine catalytic dyad. A computational approach, guiding a covalent docking as it offers higher potency, long duration of action and decreased drug resistance advantages over the conventional docking of the ligands on a catalytic dyad, is applied for SARS-CoV-2 main protease (Mpro) in this manuscript to divulge better molecules. Mpro active site contains Cys145 residue which act as a nucleophile and can donate its electron to an electrophilic molecule by interacting covalently. Furthermore, the ligand-protein complexes are allowed to simulate their dynamic studies to look into their time-based interaction stability and also, a parallel study of ADME properties for the hit molecules is also performed. Important insights from the studies revealed that the interactions are persistent and molecules may be considered for further optimization in clinical investigation.Communicated by Ramaswamy H. Sarma.

In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Main Protease among a PubChem Database of Avian Infectious Bronchitis Virus 3CLPro Inhibitors

Remarkable structural homologies between the main proteases of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the avian infectious bronchitis virus (IBV) were revealed by comparative amino-acid sequence and 3D structural alignment. Assessing whether reported IBV 3CLPro inhibitors could also interact with SARS-CoV-2 has been undertaken in silico using a PubChem BioAssay database of 388 compounds active on the avian infectious bronchitis virus 3C-like protease. Docking studies of this database on the SARS-CoV-2 protease resulted in the identification of four covalent inhibitors targeting the catalytic cysteine residue and five non-covalent inhibitors for which the binding was further investigated by molecular dynamics (MD) simulations. Predictive ADMET calculations on the nine compounds suggest promising pharmacokinetic properties.

Computational and Enzymatic Studies of Sartans in SARS-CoV-2 Spike RBD-ACE2 Binding: The Role of Tetrazole and Perspectives as Antihypertensive and COVID-19 Therapeutics

This study is an extension of current research into a novel class of synthetic antihypertensive drugs referred to as "bisartans", which are bis-alkylated imidazole derivatives bearing two symmetric anionic biphenyltetrazoles. Research to date indicates that bisartans are superior to commercially available hypertension drugs, since the former undergo stronger docking to angiotensin-converting enzyme 2 (ACE2). ACE2 is the key receptor involved in SARS-CoV-2 entry, thus initiating COVID-19 infection and in regulating levels of vasoactive peptides such as angiotensin II and beneficial heptapeptides A(1-7) and Alamandine in the renin-angiotensin system (RAS). In previous studies using in vivo rabbit-iliac arterial models, we showed that Na⁺ or K⁺ salts of selected Bisartans initiate a potent dose-response inhibition of vasoconstriction. Furthermore, computational studies revealed that bisartans undergo stable binding to the vital interfacial region between ACE2 and the SARS-CoV-2 "receptor binding domain" (i.e., the viral RBD). Thus, bisartan homologs are expected to interfere with SARS-CoV-2 infection and/or suppress disease expression in humans. The primary goal of this study was to investigate the role of tetrazole in binding and the network of amino acids of SARS-CoV-2 Spike RBD-ACE2 complex involved in interactions with sartans. This study would, furthermore, allow the expansion of the synthetic space to create a diverse suite of new bisartans in conjunction with detailed computational and in vitro antiviral studies. A critical role for tetrazole was uncovered in this study, shedding light on the vital importance of this group in the binding of sartans and bisartans to the ACE2/Spike complex. The in silico data predicting an interaction of tetrazole-containing sartans with ACE2 were experimentally validated by the results of surface plasmon resonance (SPR) analyses performed with a recombinant human ACE2 protein.

SCARdock: A Web Server and Manually Curated Resource for Discovering Covalent Ligands

BACKGROUND

Covalent drugs have been intentionally discarded historically due to the concern of off-target side effects, but the past decade has seen a fast resurgence of the discovery of covalent drugs. Compared to noncovalent ligands, covalent ligands might have better biochemical efficiency, lower patient burden, less dosing frequency, less drug resistance, and improved target specificity.

RESULTS

Previously, we proposed the steric-clashes alleviating receptor (SCAR) strategy for screening and repurposing covalent inhibitors. To help the discovery of covalent ligands targeting protein targets, we have developed a web server dedicated to providing the SCARdock protocol to general users. Along with this server, we presented three high-quality data sets for the discovery of covalent ligands: a manually curated data set containing 954 high-quality complex structures of covalent ligands and proteins, a manually curated data set of 68 experimentally confirmed covalent warheads targeting 11 different residues, and a prefiltered, classified, and ready-to-use data set of 690,018 entries of purchasable virtual compounds containing these experimentally verified warheads.

CONCLUSIONS

The SCARdock server and the accompanied data sets would be of great value to the discovery of covalent ligands and are available freely at http://www.liugroup.site/scardock/ or https://scardock.com.

WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19

The global coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV) has led to a huge health and economic crises. However, the research required to develop new drugs and vaccines is very expensive in terms of labor, money, and time. Owing to recent advances in data science, drug-repositioning technologies have become one of the most promising strategies available for developing effective treatment options. Using the previously reported human drug virus database (HDVD), we proposed a model to predict possible drug regimens based on a weighted reconstruction-based linear label propagation algorithm (WLLP). For the drug–virus association matrix, we used the weighted K-nearest known neighbors method for preprocessing and label propagation of the network based on the linear neighborhood similarity of drugs and viruses to obtain the final prediction results. In the framework of 10 times 10-fold cross-validated area under the receiver operating characteristic (ROC) curve (AUC), WLLP exhibited excellent performance with an AUC of 0.8828 ± 0.0037 and an area under the precision-recall curve of 0.5277 ± 0.0053, outperforming the other four models used for comparison. We also predicted effective drug regimens against SARS-CoV-2, and this case study showed that WLLP can be used to suggest potential drugs for the treatment of COVID-19.

Application of Nanotechnology in COVID-19 Infection: Findings and Limitations

There is an urgent need to address the global mortality of the COVID-19 pandemic, as it reached 6.3 million as of July 2022. As such, the experts recommended the mass diagnosis of SARS-CoV-2 infection at an early stage using nanotechnology-based sensitive diagnostic approaches. The development of nanobiosensors for Point-of-Care (POC) sampling of COVID-19 could ensure mass detection without the need for sophisticated laboratories or expert personnel. The use of Artificial Intelligence (AI) techniques for POC detection was also proposed. In addition, the utilization of various antiviral nanomaterials such as Silver Nanoparticles (AgNPs) for the development of masks for personal protection mitigates viral transmission. Nowadays, nano-assisted vaccines have been approved for emergency use, but their safety and effectiveness in the mutant strain of the SARS-CoV-2 virus remain challenging. Methodology: Updated literature was sourced from various research indexing databases such as PubMed, SCOPUS, Science Direct, Research Gate and Google Scholars. Result: We presented the concept of novel nanotechnology researched discovery, including nano-devices, electrochemical biosensing, nano-assisted vaccine, and nanomedicines, for use in recent times, which could be a formidable step for future management of COVID-19.

Recent computational drug repositioning strategies against SARS-CoV-2

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We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data.

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We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19.

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Integrating different levels of data may improve the success rate for drug repositioning.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

Molecular modeling of potent novel sulfonamide derivatives as non-peptide small molecule anti-COVID 19 agents

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19. The Sulfonamides groups have been widely introduced in several drugs, especially for their antibacterial activities and generally prescribed for respiratory infections. On the other hand, imidazole groups have the multipotency to act as drugs, including antiviral activity. We have used a structure-based drug design approach to design some imidazole derivatives of sulfonamide, which can efficiently bind to the active site of SARS-CoV-2 main protease and thus may have the potential to inhibit its proteases activity. We conducted molecular docking and molecular dynamics simulation to observe the stability and flexibility of inhibitor complexes. We have checked ADMET (absorption, distribution, metabolism, excretion and toxicity) and drug-likeness rules to scrutinize toxicity and then designed the most potent compound based on computational chemistry. Our small predicted molecule non-peptide protease inhibitors could provide a useful model in the further search for novel compounds since it has many advantages over peptidic drugs, like lower side effects, toxicity and less chance of drug resistance. Further, we confirmed the stability of our inhibitor-complex and interaction profile through the Molecular dynamics simulation study. Our small predicted moleculeCommunicated by Ramaswamy H. Sarma.

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning

The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout India since the first case in India was reported on 30 January 2020. The official report says that totally 4, 11,773 cases are positive, 2, 28,307 recovered, and the country reported 12,948 deaths as of 21 June 2020. Vaccination is the only way to prevent the spreading of COVID-19 disease. Due to various reasons, there is vaccine hesitancy across many people. Hence, the Indian government has the solution to avoid the spread of the disease by instructing their citizens to maintain social distancing, wearing masks, avoiding crowds, and cleaning your hands. Moreover, lots of poverty cases are reported due to social distancing, and hence, both the center government and the respective state governments decide to issue relief funds to all its citizens. The government is unable to maintain social distancing during the relief schemes as the population is huge and available support staffs are less. In this paper, the proposed algorithm makes use of graph theory to schedule the timing of the relief funds so that with the available support staff, the government would able to implement its relief scheme while maintaining social distancing. Furthermore, we have used LSTM deep learning model to predict the spread rate and analyze the daily positive COVID cases.

Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach

Small molecules such as 9,10-dihydrophenanthrene derivatives have remarkable activity toward inhibition of SARS-CoV-2 3CLpro and COVID-19 proliferation, which show a strong correlation between their structures and bioactivity. Therefore, these small compounds could be suitable for clinical pharmaceutical use against COVID-19. The objective of this study was to remodel the structures of 9,10-dihydrophenanthrene derivatives to achieve a powerful biological activity against 3CLpro and favorable pharmacokinetic properties for drug design and discovery. Therefore, by the use of bioinformatics techniques, we developed robust 3D-QSAR models that are capable of describing the structure-activity relationship for 46 molecules based on 9,10-dihydrophenanthrene derivatives using CoMFA/SE (R 2 = 0.97, Q 2 = 0.81, R 2 pred = 0.95, c R 2 p = 0.71) and CoMSIA/SEHDA (R 2 = 0.94, Q 2 = 0.76, R 2 pred = 0.91, c R 2 p = 0.65) techniques. Accordingly, 96 lead compounds were generated based on a template molecule that showed the highest observed activity in vitro (T40, pIC50 = 5.81) and predicted their activities and bioavailability in silico. The rational screening outputs of 3D-QSAR, Molecular docking, ADMET, and MM-GBSA led to the identification of 9 novel modeled molecules as potent noncovalent drugs against SARS-CoV-2-3CLpro. Finally, by molecular dynamics simulations, the stability and structural dynamics of 3CLpro free and complex (PDB code: 6LU7) were discussed in the presence of samples of 9,10-dihydrophenanthrene derivative in an aqueous environment. Overall, the retrosynthesis of the proposed drug compounds in this study and the evaluation of their bioactivity in vitro and in vivo may be interesting for designing and discovering a new drug effective against COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02004-z.

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.

The spike glycoprotein of SARS-CoV-2: A review of how mutations of spike glycoproteins have driven the emergence of variants with high transmissibility and immune escape

Late in 2019, SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) emerged, causing an unknown type of pneumonia today called coronaviruses disease 2019 (COVID-19). COVID-19 is still an ongoing global outbreak that has claimed and threatened many lives worldwide. Along with the fastest vaccine developed in history to fight SARS-CoV-2 came a critical problem, SARS-CoV-2. These new variants are a result of the accumulation of mutations in the sequence and structure of spike (S) glycoprotein, which is by far the most critical protein for SARS-CoV-2 to recognize cells and escape the immune system, in addition to playing a role in SARS-CoV-2 infection, pathogenicity, transmission, and evolution. In this review, we discuss mutation of S protein and how these mutations have led to new variants that are usually more transmissible and can thus mitigate the immunity produced by vaccination. Here, analysis of S protein sequences and structures from variants point out the mutations among them, how they emerge, and the behavior of S protein from each variant. This review brings details in an understandable way about how the variants of SARS-CoV-2 are a result of mutations in S protein, making them more transmissible and even more aggressive than their relatives.

A review on drug repurposing in COVID-19: from antiviral drugs to herbal alternatives

BACKGROUND

COVID-19 is an illness caused by severe acute respiratory syndrome coronavirus 2. Due to its rapid spread, in March 2020 the World Health Organization (WHO) declared pandemic. Since the outbreak of pandemic many governments, scientists, and institutions started to work on new vaccines and finding of new and repurposing drugs. Drug repurposing is an excellent option for discovery of already used drugs, effective against COVID-19, lowering the cost of production, and shortening the period of delivery, especially when preclinical safety studies have already been performed. There are many approved drugs that showed significant results against COVID-19, like ivermectin and hydrochloroquine, including alternative treatment options against COVID-19, utilizing herbal medicine.

SHORT CONCLUSION

This article summarized 11 repurposing drugs, their positive and negative health implications, along with traditional herbal alternatives, that harvest strong potential in efficient treatments options against COVID-19, with small or no significant side effects. Out of 11 repurposing drugs, four drugs are in status of emergency approval, most of them being in phase IV clinical trials. The first repurposing drug approved for clinical usage is remdesivir, whereas chloroquine and hydrochloroquine approval for emergency use was revoked by FDA for COVID-19 treatment in June 2020.

Recent advances in developing small-molecule inhibitors against SARS-CoV-2

The COVID-19 pandemic caused by the novel SARS-CoV-2 virus has caused havoc across the entire world. Even though several COVID-19 vaccines are currently in distribution worldwide, with others in the pipeline, treatment modalities lag behind. Accordingly, researchers have been working hard to understand the nature of the virus, its mutant strains, and the pathogenesis of the disease in order to uncover possible drug targets and effective therapeutic agents. As the research continues, we now know the genome structure, epidemiological and clinical features, and pathogenic mechanism of SARS-CoV-2. Here, we summarized the potential therapeutic targets involved in the life cycle of the virus. On the basis of these targets, small-molecule prophylactic and therapeutic agents have been or are being developed for prevention and treatment of SARS-CoV-2 infection.

Exploring nature's bounty: identification of Withania somnifera as a promising source of therapeutic agents against COVID-19 by virtual screening and in silico evaluation

Coronaviruses are etiological agents of extreme human and animal infection resulting in abnormalities primarily in the respiratory tract. Presently, there is no defined COVID-19 intervention and clinical trials of prospective therapeutic agents are still in the nascent stage. Withania somnifera (L.) Dunal (WS), is an important medicinal plant in Ayurveda. The present study aimed to evaluate the antiviral potential of selected WS phytoconstituents against the novel SARS-CoV-2 target proteins and human ACE2 receptor using in silico methods. Most of the phytoconstituents displayed good absorption and transport kinetics and were also found to display no associated mutagenic or adverse effect(s). Molecular docking analyses revealed that most of the WS phytoconstituents exhibited potent binding to human ACE2 receptor, SAR-CoV and SARS-CoV-2 spike glycoproteins as well as the two main SARS-CoV-2 proteases. Most of the phytoconstituents were predicted to undergo Phase-I metabolism prior to excretion. All phytoconstituents had favorable bioactivity scores with respect to various receptor proteins and target enzymes. SAR analysis revealed that the number of oxygen atoms in the withanolide backbone and structural rearrangements were crucial for effective binding. Molecular simulation analyses of SARS-CoV-2 spike protein and papain-like protease with Withanolides A and B, respectively, displayed a stability profile at 300 K and constant RMSDs of protein side chains and Cα atoms throughout the simulation run time. In a nutshell, WS phytoconstituents warrant further investigations in vitro and in vivo to unravel their molecular mechanism(s) and modes of action for their future development as novel antiviral agents against COVID-19.

DrugDevCovid19: An Atlas of Anti-COVID-19 Compounds Derived by Computer-Aided Drug Design

Since the outbreak of SARS-CoV-2, numerous compounds against COVID-19 have been derived by computer-aided drug design (CADD) studies. They are valuable resources for the development of COVID-19 therapeutics. In this work, we reviewed these studies and analyzed 779 compounds against 16 target proteins from 181 CADD publications. We performed unified docking simulations and neck-to-neck comparison with the solved co-crystal structures. We computed their chemical features and classified these compounds, aiming to provide insights for subsequent drug design. Through detailed analyses, we recommended a batch of compounds that are worth further study. Moreover, we organized all the abundant data and constructed a freely available database, DrugDevCovid19, to facilitate the development of COVID-19 therapeutics.

DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations

Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug–disease associations (DDAs) can improve the prediction accuracy of the DDAs.

Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug–disease associations, including the prediction of as yet unknown DDAs in the input.

Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug–disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug–disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug–disease associations.

Computational approaches for drug repositioning and repurposing to combat SARS-CoV-2 infection

Drug repositioning (also referred to as drug repurposing) is the method of exploring novel therapeutic indications for Food and Drug Administration-approved clinically implemented drugs. The unique strategy of drug repositioning is used to boost the drug development process since drug discovery is an expensive, arduous, cumbersome, and high-risk procedure. Recently, several pharmaceutical firms have used the drug repositioning technique in their drug discovery and development programs to develop new medications based on the identification of new therapeutic targets. This technique is extremely effective, saves time, is comparatively economical, and has a low chance of failure. Developing appropriate treatment measures to inhibit the spread of Coronavirus disease-2019 (COVID-19) is currently a top priority. As a result, several studies were conducted to build novel therapeutic molecules using diverse strategies of drug repurposing to discover drug candidates against COVID-19 infection that can act as substantial inhibitors against virus particles. By implementing virtual screening of drug libraries, it is possible to identify potential drugs through drug repurposing. A molecular docking approach and calculation of binding free energy are used to estimate binding affinity and drug–receptor interactions. Drug-repurposing methodologies can be divided into three categories: target-oriented, drug-oriented, and disease-oriented, based on the gathered data about the various physicochemical, pharmacokinetic and pharmacological features of a drug candidate. Using computational methods such as homology modeling and molecular similarity, this methodology aids in determining the binding interaction of drug molecules with the target protein of the virus. In this book chapter, we explore a typical set of currently utilized computational techniques for identifying repurposable drug molecules for COVID-19, as well as their supporting databases. We also assess promising drugs anticipated by computational approaches to drugs currently being evaluated in clinical trials. Moreover, we also examine the takeaways from the evaluated research efforts, such as how to competently combine bioinformatics tools with experimental work and suggest a fully integrated drug-repurposing approach to combat the deadly COVID-19 infection.

Rapid screening and identification of viral pathogens in metagenomic data

BACKGROUND

Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified.

RESULTS

In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references).

CONCLUSIONS

Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).

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.

Investigating Fungi-Derived Bioactive Molecules as Inhibitor of the SARS Coronavirus Papain Like Protease: Computational Based Study

Due to the rapid growth of the COVID-19 pandemic and its outcomes, developing a remedy to fight the predicament is critical. So far, it has infected more than 214,468,601 million people and caused the death of 4,470,969 million people according to the August 27, 2021, World Health Organization's (WHO) report. Several studies have been published on both computational and wet-lab approaches to develop antivirals for COVID-19, although there has been no success yet. However, the wet-lab approach is laborious, expensive, and time-consuming, and computational techniques have screened the activity of bioactive compounds from different sources with less effort and cost. For this investigation, we screened the binding affinity of fungi-derived bioactive molecules toward the SARS coronavirus papain-like protease (PLpro) by using computational approaches. Studies showed that protease inhibitors can be very effective in controlling virus-induced infections. Additionally, fungi represent a vast source of bioactive molecules, which could be potentially used for antiviral therapy. Fifty fungi-derived bioactive compounds were investigated concerning SARS-CoV-2 PLpro by using Auto Dock 4.2.1, Gromacs 2018. 2, ADMET, Swiss-ADME, FAF-Drugs 4.023, pKCSM, and UCLA-DOE server. From the list of the screened bioactive compounds, Dihydroaltersolanol C, Anthraquinone, Nigbeauvin A, and Catechin were selected with the Auto-Dock results of -8.68, -7.52, -10.46, and -10.58 Kcal/mol, respectively, based on their binding affinity compared to the reference drug. We presented the drug likeliness, toxicity, carcinogenicity, and mutagenicity of all compounds using ADMET analysis. They interacted with the amino acid residues, Gly163, Trp106, Ser111, Asp164, and Cys270, through hydrogen bonds. The root-mean-square deviation (RMSD), root-mean-square fluctuations (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg) values revealed a stable interaction. From the overall analyses, we can conclude that Dihydroaltersolanol C, Anthraquinone, Nigbeauvin A, and Catechin are classified as promising candidates for PLpro, thus potentially useful in developing a medicine for COVID-19.

Artificial Intelligence for COVID-19: A Systematic Review

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Recent progress in the development of potential drugs against SARS-CoV-2

SARS-CoV-2, a newly emerged and highly pathogenic coronavirus, is identified as the causal agent of Coronavirus Disease (2019) (COVID-19) in the late December 2019, in China. The virus has rapidly spread nationwide and spilled over to the other countries around the globe, resulting in more than 120 million infections and 2.6 million deaths until the time of this review. Unfortunately, there are still no specific drugs available against this disease, and it is very necessary to call upon more scientists to work together to stop a further spread. Hence, the recent progress in the development of drugs may help scientific community quickly understand current research status and further develop new effective drugs. Herein, we summarize the cellular entry and replication process of this virus and discuss the recent development of potential viral based drugs that target bio-macromolecules in different stages of the viral life cycle, especially S protein, 3CLPro, PLPro, RdRp and helicase.

Drug repurposing studies targeting SARS-CoV-2: an ensemble docking approach on drug target 3C-like protease (3CLpro)

The COVID-19 pandemic has been responsible for several deaths worldwide. The causative agent behind this disease is the Severe Acute Respiratory Syndrome - novel Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 belongs to the category of RNA viruses. The main protease, responsible for the cleavage of the viral polyprotein is considered as one of the hot targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for targeting the main protease of SARS-CoV, which caused SARS in the year 2002-2003. Hence, drug repurposing approach may prove to be useful in targeting the main protease of SARS-CoV-2. The high-resolution crystal structure of the main protease of SARS-CoV-2 (PDB ID: 6LU7) was used as the target. The Food and Drug Administration approved and SWEETLEAD database of drug molecules were screened. The apo form of the main protease was simulated for a cumulative of 150 ns and 10 μs open-source simulation data was used, to obtain conformations for ensemble docking. The representative structures for docking were selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble docking approach for the main protease helped in exploring the conformational variation in the drug-binding site of the main protease leading to the efficient binding of more relevant drug molecules. The drugs obtained as top hits from the ensemble docking possessed anti-bacterial and anti-viral properties. This in silico ensemble docking approach would support the identification of potential candidates for repurposing against COVID-19.Communicated by Ramaswamy H. Sarma.

Binding of boswellic acids to functional proteins of the SARS-CoV-2 virus: Bioinformatic studies

Boswellic acids (BAs) have been shown to possess antiviral activity. Using bioinformatic methods, it was tested whether or not acetyl‐11‐keto‐β‐boswellic acid (AKBA), 11‐keto‐β‐boswellic acid (KBA), β‐boswellic acid (BBA), and the phosphorylated active metabolite of Remdesivir® (RGS‐P3) bind to functional proteins of SARS‐CoV‐2, that is, the replicase polyprotein P0DTD1, the spike glycoprotein P0DTC2, and the nucleoprotein P0DTC9. Using P0DTD1, AKBA and KBA showed micromolar binding affinity to the RNA‐dependent RNA polymerase (RdRp) and to the main proteinase complex M^pro. Phosphorylated BAs even bond in the nanomolar range. Due to their positive and negative charges, BAs and RGS‐P3 bond to corresponding negative and positive areas of the protein. BAs and RGS‐P3 docked in the tunnel‐like cavity of RdRp. BAs also docked into the elongated surface rim of viral M^pro. In both cases, binding occurred with active site amino acids in the lower micromolecular to upper nanomolar range. KBA, BBA, and RGS‐P3 also bond to P0DTC2 and P0DTC9. The binding energies for BAs were in the range of −5.8 to −6.3 kcal/mol. RGS‐P3 and BAs occluded the centrally located pore of the donut‐like protein structure of P0DTC9 and, in the case of P0DTC2, RGS‐P3 and BAs impacted the double‐wing‐like protein structure. The data of this bioinformatics study clearly show that BAs bind to three functional proteins of the SARS‐CoV‐2 virus responsible for adhesion and replication, as does RGS‐P3, a drug on the market to treat this disease. The binding effectiveness of BAs can be increased through phosphate esterification. Whether or not BAs are druggable against the SARS‐CoV‐2 disease remains to be established.

An overview of potential inhibitors targeting non-structural proteins 3 (PLpro and Mac1) and 5 (3CLpro/Mpro) of SARS-CoV-2

There is an urgent need to develop effective treatments for coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid spread of SARS-CoV-2 has resulted in a global pandemic that has not only affected the daily lives of individuals but also had a significant impact on the global economy and public health. Although extensive research has been conducted to identify inhibitors targeting SARS-CoV-2, there are still no effective treatment strategies to combat COVID-19. SARS-CoV-2 comprises two important proteolytic enzymes, namely, the papain-like proteinase, located within non-structural protein 3 (nsp3), and nsp5, both of which cleave large replicase polypeptides into multiple fragments that are required for viral replication. Moreover, a domain within nsp3, known as the macrodomain (Mac1), also plays an important role in viral replication. Inhibition of their functions should be able to significantly interfere with the replication cycle of the virus, and therefore these key proteins may serve as potential therapeutic targets. The functions of the above viral targets and their corresponding inhibitors have been summarized in the current review. This review provides comprehensive updates of nsp3 and nsp5 inhibitor development and would help advance the discovery of novel anti-viral therapeutics against SARS-CoV-2.

Phytochemicals from Leucas zeylanica Targeting Main Protease of SARS-CoV-2: Chemical Profiles, Molecular Docking, and Molecular Dynamics Simulations

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a contemporary coronavirus, has impacted global economic activity and has a high transmission rate. As a result of the virus's severe medical effects, developing effective vaccinations is vital. Plant-derived metabolites have been discovered as potential SARS-CoV-2 inhibitors. The SARS-CoV-2 main protease (Mpro) is a target for therapeutic research because of its highly conserved protein sequence. Gas chromatography-mass spectrometry (GC-MS) and molecular docking were used to screen 34 compounds identified from Leucas zeylanica for potential inhibitory activity against the SARS-CoV-2 Mpro. In addition, prime molecular mechanics-generalized Born surface area (MM-GBSA) was used to screen the compound dataset using a molecular dynamics simulation. From molecular docking analysis, 26 compounds were capable of interaction with the SARS-CoV-2 Mpro, while three compounds, namely 11-oxa-dispiro[4.0.4.1]undecan-1-ol (-5.755 kcal/mol), azetidin-2-one 3,3-dimethyl-4-(1-aminoethyl) (-5.39 kcal/mol), and lorazepam, 2TMS derivative (-5.246 kcal/mol), exhibited the highest docking scores. These three ligands were assessed by MM-GBSA, which revealed that they bind with the necessary Mpro amino acids in the catalytic groove to cause protein inhibition, including Ser144, Cys145, and His41. The molecular dynamics simulation confirmed the complex rigidity and stability of the docked ligand-Mpro complexes based on the analysis of mean radical variations, root-mean-square fluctuations, solvent-accessible surface area, radius of gyration, and hydrogen bond formation. The study of the postmolecular dynamics confirmation also confirmed that lorazepam, 11-oxa-dispiro[4.0.4.1]undecan-1-ol, and azetidin-2-one-3, 3-dimethyl-4-(1-aminoethyl) interact with similar Mpro binding pockets. The results of our computerized drug design approach may assist in the fight against SARS-CoV-2.

Nature-derived hit, lead, and drug-like small molecules: Current status and future aspects against key target proteins of Coronaviruses

BACKGROUND

COVID-19 pandemic, the most unprecedented event of the year 2020, has brought millions of scientists worldwide in a single platform to fight against it. Though several drugs are now in the clinical trial, few vaccines available on the market already but the lack of an effect of those is making the situation worse.

AIM OF THE STUDY

In this review, we demonstrated comprehensive data of natural antiviral products showing activities against different proteins of Human Coronaviruses (HCoV) that are responsible for its pathogenesis. Furthermore, we categorized the compounds into the hit, lead, and drug based on the IC50/EC50 value, drug-likeness, and lead-likeness test to portray their potentiality to be a drug. We also demonstrated the present status of our screened antiviral compounds with respect to clinical trials and reported the lead compounds that can be promoted to clinical trial against COVID-19.

METHODS

A systematic search strategy was employed focusing on Natural Products (NPs) with proven activity (in vitro, in vivo, or in silico) against human coronaviruses, in general, and data were gathered from databases like PubMed, Web of Science, Google Scholar, SciVerse, and Scopus. Information regarding clinical trials retrieved from the Clinical Trial database.

RESULTS

Total "245" natural compounds were identified initially from the literature study. Among them, Glycyrrhizin, Caffeic acid, Curcumin is in phase 3, and Tetrandrine, Cyclosporine, Tacrolimus, Everolimus are in phase 4 clinical trial. Except for Glycyrrhizin, all compounds showed activity against COVID-19.

CONCLUSIONS

In summary, our demonstrated specific small molecules with lead and drug-like capabilities clarified their position in the drug discovery pipeline and proposed their future research against COVID-19.

Targeting SARS-COV-2 non-structural protein 16: a virtual drug repurposing study

Non-Structural Protein 16 (nsp-16), a viral RNA methyltransferase (MTase), is one of the highly viable targets for drug discovery of coronaviruses including SARS-CoV-2. In this study, drug discovery of SARS-CoV-2 nsp-16 has been performed by a virtual drug repurposing approach. First, drug shape-based screening (among FDA approved drugs) with a known template of MTase inhibitor, sinefungin was done and best compounds with high similarity scores were selected. In addition to the selected compounds, 4 nucleoside analogs of anti-viral (Raltgravir, Maraviroc and Favipiravir) and anti-inflammatory (Prednisolone) drugs were selected for further investigations. Then, binding energies and interaction modes were found by molecular docking approaches and compouds with lower energy were selected for further investigation. After that, Molecular dynamics (MD) simulation was carried to test the potential selected compounds in a realistic environment. The results showed that Raltegravir and Maraviroc among other compounds can bind strongly to the active site of the protein compared to sinefungin, and can be potential candidates to inhibit NSP-16. Also, the MD simulation results suggested that the Maraviroc and Raltegravir are more effective drug candidates than Sinefungin for inhibiting the enzyme. It is concluded that Raltegravir and Maraviroc which may be used in the treatment of COVID-19 after Invitro and invivo studies and clinical trial for final confirmation of drug effectiveness. Communicated by Ramaswamy H. Sarma.

Repurposing cefuroxime for treatment of COVID-19: a scoping review of in silico studies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus disease 19 (COVID-19), is a novel human Coronavirus that is responsible for about 300,000 deaths worldwide. To date, there is no confirmed treatment or vaccine prevention strategy against COVID-19. Due to the urgent need for effective treatment, drug repurposing is regarded as the immediate option. Potential drugs can often be identified via in silico drug screening experiments. Consequently, there has been an explosion of in silico experiments to find drug candidates or investigate anecdotal claims. One drug with several anecdotal accounts of benefit is Cefuroxime. The aim of this study was to identify and summarize in silico evidence for possible activity of Cefuroxime against SARS-CoV-2.To this end, we performed a scoping review of literature of in silico drug repurposing experiments for SARS-CoV-2 using PRISMA-ScR. We searched Medline, Embase, Scopus, Web of Knowledge, and Google Scholar for original studies published between 1st Feb, 2020 and 15th May, 2020 that screened drug libraries, and identified Cefuroxime as a top-ranked potential inhibitor drug against SARS-CoV-2 proteins. Six studies were identified. These studies reported Cefuroxime as a potential inhibitor of 3 key SARS-CoV-2 proteins; main protease, RNA dependent RNA polymerase, and ACE2-Spike complex. We provided a summary of the methodology and findings of the identified studies. Our scoping review identified significant in silico evidence that Cefuroxime may be a potential multi-target inhibitor of SARS-CoV-2. Further in vitro and in vivo studies are required to evaluate the potential of Cefuroxime for COVID-19.Communicated by Ramaswamy H. Sarma.

Inhibition of the 3CL Protease and SARS-CoV-2 Replication by Dalcetrapib

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) 3CL protease is a promising target for inhibition of viral replication by interaction with a cysteine residue (Cys145) at its catalytic site. Dalcetrapib exerts its lipid-modulating effect by binding covalently to cysteine 13 of a cholesteryl ester transfer protein. Because 12 free cysteine residues are present in the 3CL protease, we investigated the potential of dalcetrapib to inhibit 3CL protease activity and SARS-CoV-2 replication. Molecular docking investigations suggested that dalcetrapib-thiol binds to the catalytic site of the 3CL protease with a delta G value of -8.5 kcal/mol. Dalcetrapib inhibited both 3CL protease activity in vitro and viral replication in Vero E6 cells with IC₅₀ values of 14.4 ± 3.3 μM and an EC₅₀ of 17.5 ± 3.5 μM (mean ± SD). Near-complete inhibition of protease activity persisted despite 1000-fold dilution after ultrafiltration with a nominal dalcetrapib-thiol concentration of approximately 100 times below the IC₅₀ of 14.4 μM, suggesting stable protease-drug interaction. The inhibitory effect of dalcetrapib on the SARS-CoV-2 3CL protease and viral replication warrants its clinical evaluation for the treatment of COVID-19.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Molecular Docking and Virtual Screening Based Prediction of Drugs for COVID-19

AIMS

To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications.

BACKGROUND

SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally.

OBJECTIVE

To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs.

METHODS

A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol.

RESULTS

Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications.

CONCLUSION

Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.

Human cell receptors: potential drug targets to combat COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease 2019 (COVID-19). The World Health Organization (WHO) has announced that COVID-19 is a pandemic having a higher spread rate rather than the mortality. Identification of a potential approach or therapy against COVID-19 is still under consideration. Therefore, it is essential to have an insight into SARS-CoV-2, its interacting partner, and domains for an effective treatment. The present study is divided into three main categories, including SARS-CoV-2 prominent receptor and its expression levels, other interacting partners, and their binding domains. The first section focuses primarily on coronaviruses' general aspects (SARS-CoV-2, SARS-CoV, and the Middle East Respiratory Syndrome Coronaviruses (MERS-CoV)) their structures, similarities, and mode of infections. The second section discusses the host receptors which includes the human targets of coronaviruses like dipeptidyl peptidase 4 (DPP4), CD147, CD209L, Angiotensin-Converting Enzyme 2 (ACE2), and other miscellaneous targets (type-II transmembrane serine proteases (TTSPs), furin, trypsin, cathepsins, thermolysin, elastase, phosphatidylinositol 3-phosphate 5-kinase, two-pore segment channel, and epithelium sodium channel C-α subunit). The human cell receptor, ACE2 plays an essential role in the Renin-Angiotensin system (RAS) pathway and COVID-19. Thus, this section also discusses the ACE2 expression and risk of COVID-19 infectivity in various organs and tissues such as the liver, lungs, intestine, heart, and reproductive system in the human body. Absence of ACE2 protein expression in immune cells could be used for limiting the SARS-CoV-2 infection. The third section covers the current available approaches for COVID-19 treatment. Overall, this review focuses on the critical role of human cell receptors involved in coronavirus pathogenesis, which would likely be used in designing target-specific drugs to combat COVID-19.

Computational Discovery of SARS-CoV-2 NSP 16 Drug Candidates Based on Pharmacophore Modeling and Molecular Dynamics Simulation

Non-Structural Protein 16 (NSP-16) is one of the most suitable targets for discovery of drugs for corona viruses including SARS-CoV-2. In this study, drug discovery of SARS-CoV-2 nsp-16 has been accomplished by pharmacophore-based virtual screening among some analogs (FDA approved drugs) and marine natural plants (MNP). The comparison of the binding energies and the inhibition constants was determined using molecular docking method. Three compounds including two FDA approved (Ibrutinib, Idelalisib) and one MNP (Kumusine) were selected for further investigation using the molecular dynamics simulations. The results indicated that Ibrutinib and Idelalisib are oral medications while Kumusine, with proper hydrophilic and solubility properties, is an appropriate candidate for nsp-16 inhibitor and can be effective to control COVID-19 disease.

Virtual screening of peptides with high affinity for SARS-CoV-2 main protease

The current pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 2,000,000 deaths worldwide. Currently, vaccine development and drug repurposing have been the main strategies to find a COVID-19 treatment. However, the development of new drugs could be the solution if the main strategies fail. Here, a virtual screening of pentapeptides was applied in order to identify peptides with high affinity to SARS-CoV-2 main protease (Mpro). Over 70,000 peptides were screened employing a genetic algorithm that uses a docking score as the fitness function. The algorithm was coupled with a RESTful API to persist data and avoid redundancy. The docking exhaustiveness was adapted to the number of peptides in each virtual screening step, where the higher the number of peptides, the lower the docking exhaustiveness. Two potential peptides were selected (HHYWH and HYWWT), which have higher affinity to Mpro than to human proteases. Albeit preliminary, the data presented here provide some basis for the rational design of peptide-based drugs to treat COVID-19.

Quantum biochemistry, molecular docking, and dynamics simulation revealed synthetic peptides induced conformational changes affecting the topology of the catalytic site of SARS-CoV-2 main protease

The recent outbreak caused by SARS-CoV-2 continues to threat and take many lives all over the world. The lack of an efficient pharmacological treatments are serious problems to be faced by scientists and medical staffs worldwide. In this work, an in silico approach based on the combination of molecular docking, dynamics simulations, and quantum biochemistry revealed that the synthetic peptides RcAlb-PepI, PepGAT, and PepKAA, strongly interact with the main protease (Mpro) a pivotal protein for SARS-CoV-2 replication. Although not binding to the proteolytic site of SARS-CoV-2 Mpro, RcAlb-PepI, PepGAT, and PepKAA interact with other protein domain and allosterically altered the protease topology. Indeed, such peptide-SARS-CoV-2 Mpro complexes provoked dramatic alterations in the three-dimensional structure of Mpro leading to area and volume shrinkage of the proteolytic site, which could affect the protease activity and thus the virus replication. Based on these findings, it is suggested that RcAlb-PepI, PepGAT, and PepKAA could interfere with SARS-CoV-2 Mpro role in vivo. Also, unlike other antiviral drugs, these peptides have no toxicity to human cells. This pioneering in silico investigation opens up opportunity for further in vivo research on these peptides, towards discovering new drugs and entirely new perspectives to treat COVID-19.Communicated by Ramaswamy H. Sarma.

Coronavirus Disease 2019: An Overview of the Complications and Management

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Since the first report of COVID-19 emerging in Wuhan, China, authorities in 216 countries and territories have reported about 47.3 million COVID-19 cases and 1.2 million deaths. The WHO guidelines for the management of COVID-19 are very limited to recommendations for managing symptoms and advice on careful management of pediatric patients, pregnant women, and patients with underlying comorbidities. There is no approved treatment for COVID-19 and guidelines vary between countries. In this review, first, a brief overview is provided on the basic knowledge about the virus, clinical features of the disease, and different diagnostic methods. Then, the relationship between COVID-19, various body systems, and other complications is discussed. Finallly, different management strategies are discussed, including those drawn on computational chemistry analyses, pre-clinical investigations, and clinical trials which involve pharmacological and non-pharmacological interventions. In conclusion, despite the recent approval of different vaccine candidates, more virological characteristics of SARS-CoV-2 are required to be explored, which may result in the discovery of more potential therapeutic targets leading to safer and more effective treatment to COVID-19.

Latest updates on SARS-CoV-2 genomic characterization, drug, and vaccine development; a comprehensive bioinformatics review

Amid the COVID-19 outbreak, several bioinformatic analyses have been conducted on SARS-CoV-2 virus genome. Numerous studies rushed to fill the gap about this novel virus. Comparison with other related sequences, structural predictions of the produced proteins, determination of variations in amino acid residues and depiction of possible drug and vaccine targets have been the focus of scientific research from the beginning of this year. In addition to discussing the viral taxonomy, clinical features, life cycle, and genome organization, this review will focus on the recent updates in genome and viral proteins characterization and potential therapeutic and vaccine candidates developed so far. Comparative studies with related genomes and proteins provide understanding for the viral molecular mechanisms and suggest targets for therapeutics and vaccinology trials to stop the escalation of this new virus. This pandemic, with its resulting social and economic afflictions, will definitely have significant marks on our lives in the following years.

A review on drug repurposing applicable to COVID-19

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

Binding ability of arginine, citrulline, N-acetyl citrulline and thiocitrulline with SARS COV-2 main protease using molecular docking studies

In this article, the binding abilities of arginine, citrulline, N-acetyl citrulline and thiocitrulline on the active sites of SARS-COV-2 protease have been investigated using in-silico studies. All the above ligands bind selectively and preferentially to Cys-145 active site and also to other amino acids surrounding to it in the main protease. Of which arginine forms less number of weaker bonds compared to the other ligands, it by itself is a precursor for the formation of citrulline analogues with in the cell. Major advantage of using the above ligands is that in addition to its preferential binding, they have the ability to increase the immunity by assisting NO generation. Our results show that N-acetyl citrulline, citrulline, thiocitrulline and arginine may be used as a supplement during the treatment of SARS-COV-2.

Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Herbal Medicine in Fighting Against COVID-19: New Battle with an Old Weapon

World population has been suffering due to the outbreak of present pandemic situation of COVID-19. The disease has become life-threatening in a very short time with touching on most of the citizenry and economic systems globally. The novel virus, SARS-CoV-2 has been known as the causative agent of COVID-19. The SARS-CoV-2 is single stranded RNA virus having ~30 kb genomic components which are 70% identical to SARS-CoV. The main process of pathophysiology of COVID-19 has been associated with the interaction of a novel coronavirus with host cell receptor, angiotensin-converting enzyme-2 (ACE 2) by fusion. Therapeutic agents having serine protease inhibitors and ACE-2 blockers may be explored for the treatment by inhibiting the viral target such as Mpro, RdRp, PLpro and helicase. Herbal medicine has a wide array chemical entity with potential health benefits including antiviral activity which may be explored as alternative treatment of COVID-19. The herbal bioactives like catechins, andrographolide, hesperidin, biorobin, scutellarein, silvestrol, shikonin, tryptanthrin, vitexin quercetin, myricetin, caffeic acid, psoralidin, luteolin etc have showed potential inhibitory effect against SARS-CoV-2. Recent research reports indicate that the various plant secondary metabolites have shown the potential antiviral activities. The present review article highlights on the recent information on the mechanism of actions and applications of herbal medicine in the treatment of COVID-19.

Evaluation of the effects of chlorhexidine and several flavonoids as antiviral purposes on SARS-CoV-2 main protease: molecular docking, molecular dynamics simulation studies

The recent outbreak of COVID-19 caused by a new human coronavirus called SARS-CoV-2, is continually causing worldwide human infections and deaths.The main protease (3CLpro), which plays a critical role in the life cycle of the virus, makes it an attractive target for the development of antiviral agents effective against coronaviruses (CoVs).Currently, there is no specific viral protein targeted therapeutics.Therefore, there is a need to investigate an alternative therapy which will prevent the spread of the infection, by focusing on the transmission of the virus.Chlorhexidine (CHX) and flavonoids agents have shown that they have a viral inactivation effect against enveloped viruses, and thus facilitate the struggle against oral transmission.Especially, some flavonoids have very strong antiviral activity in SARS-CoV and MERS-CoV main protease.This study was conducted to evaluate the CHX and flavonoids compounds potential antiviral effects on SARS-CoV-2 main protease through virtual screening for the COVID-19 treatment by molecular docking method.According to the results of this study, CHX, Kaempferol-3-rutinoside, Rutin, Quercetin 3-beta-D-glucoside and Isobavachalcone exhibited the best binding affinity against this enzyme, and also these compounds showed significant inhibitory effects compared to the SARS-CoV-2 main protease crystal structure inhibitor (N3).Especially, these compounds mainly interact with His41, Cys145, His163, Met165, Glu166 and Thr190 in SARS-CoV-2 main protease binding site. Further, MD simulation analysis also confirmed that stability of these interactions between the enzyme and these five compounds.The current study provides to guide clinical trials for broad-spectrum CHX and bioactive flavonoids to reduce the viral load of the infection and possibly disease progression.Communicated by Ramaswamy H. Sarma.

Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding

A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments; however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19. A biomedical knowledge base was established via FastText, a word embedding technique, using PubMed literature from the past decade. Subsequently, a new knowledge base was created using recently published COVID-19 articles. Using this newly constructed knowledge base from the word embedding model, a list of anti-infective drugs and proteins of either human or coronavirus origin were inferred to be related, because they are located close to COVID-19 on the knowledge base. This study attempted to form a method to quickly infer related information about COVID-19 using the existing knowledge base, before sufficient knowledge about COVID-19 is accumulated. With COVID-19 not completely overcome, machine learning-based research in the PubMed literature will provide a broad guideline for researchers and pharmaceutical companies working on treatments for COVID-19.

Potential SARS-CoV-2 main protease inhibitors

The coronavirus disease 2019 (COVID-19) pandemic has prompted an urgent need for new treatment strategies. No target-specific drugs are currently available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but new drug candidates targeting the viral replication cycle are being explored. A prime target of drug-discovery efforts is the SARS-CoV-2 main protease (Mpro). The main proteases of different coronaviruses, including SARS-CoV-2, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), share a structurally conserved substrate-binding region that can be exploited to design new protease inhibitors. With the recent reporting of the X-ray crystal structure of the SARS-CoV-2 Mpro, studies to discover Mpro inhibitors using both virtual and in vitro screening are progressing rapidly. This review focusses on the recent developments in the search for small-molecule inhibitors targeting the SARS-CoV-2 Mpro.

Docking-based virtual screening studies aiming at the covalent inhibition of SARS-CoV-2 MPro by targeting the cysteine 145

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 which has infected millions of people worldwide. The main protease of SARS-CoV-2 (M^Pro) has been recognized as a key target for the development of antiviral compounds. Taking advantage of the X-ray crystal complex with reversible covalent inhibitors interacting with the catalytic cysteine 145 (Cys145), we explored flexible docking studies to select alternative compounds able to target this residue as covalent inhibitors. First, docking studies of three known electrophilic compounds led to results consistent with co-crystallized data validating the method for SARS-CoV-2 M^Pro covalent inhibition. Then, libraries of soft electrophiles (overall 41 757 compounds) were submitted to docking-based virtual screening resulting in the identification of 17 molecules having their electrophilic group close to the Cys145 residue. We also investigated flexible docking studies of a focused approved covalent drugs library including 32 compounds with various electrophilic functional groups. Among them, the calculations resulted in the identification of four compounds, namely dimethylfumarate, fosfomycin, ibrutinib and saxagliptin, able first, to bind to the active site of the protein and second, to form a covalent bond with the catalytic cysteine.