- 1.1 Introduction
- 1.1.1 What is Drug Repurposing?
- 1.1.2 Why Drug Repurposing?
- 1.1.3 Why Drug Repurposing for COVID-19?
- 1.1.4 Why In Silico Approaches for Drug Repurposing?
- 1.2 Understanding SARS-CoV-2 from an In Silico Perspective
- 1.2.1 Structural Proteins of SARS-CoV-2
- 1.2.2 Non-structural and Accessory Proteins of SARS-CoV-2
- 1.2.3 Structure of SARS-CoV-2 Proteins
- 1.3 Structure-based Approaches for Drug Repurposing
- 1.3.1 Docking Studies in the Main Protease (Mpro/3CLpro)
- 1.3.2 Docking Studies in RNA-dependent RNA Polymerase
- 1.3.3 Docking Studies in Papain-like Protease
- 1.3.4 Docking Studies in the Nucleocapsid Protein (N-protein)
- 1.3.5 Docking Studies in the Spike Glycoprotein (S-protein)
- 1.3.6 Docking Studies in NSP1
- 1.3.7 Docking Studies in NSP13/Helicase
- 1.3.8 Docking Studies in NSP15/Endonucleases
- 1.3.9 Docking Studies in NSP16
- 1.3.10 Docking in Main Protease and Spike Glycoprotein
- 1.3.11 Docking in Multiple Structural Proteins
- 1.3.12 Docking in Proteases
- 1.3.13 Docking in Multiple Targets
- 1.3.14 Discussion and Consensus Screening Protocol from the Reviewed Literature
- 1.4 Ligand-based Approaches for Drug Repurposing
- 1.4.1 QSAR-based Approaches
- 1.4.2 Pharmacophore-based Approaches
- 1.5 Other Approaches for Drug Repurposing
- 1.5.1 Machine Learning-based Methods
- 1.5.2 Pharmacology-based Network Analysis Methods
- 1.6 Understanding Human Targets in COVID-19 From an In Silico Perspective
- 1.6.1 Host Proteins Involved in the SARS-CoV-2 Life Cycle
- 1.6.2 Host Response to SARS-CoV-2 Infection
- 1.6.3 Structural Information of Human Proteins in COVID-19
- 1.7 Structure-based Approaches for Drug Repurposing Using Human Proteins
- 1.7.1 Docking Studies in Angiotensin Converting Enzyme-2
- 1.7.2 Docking Studies in Transmembrane Protease, Serine 2 (TMPRSS2)
- 1.7.3 Docking Studies in Glucose-Regulated Protein 78 (GRP78)
- 1.7.4 Docking Studies in Furin
- 1.7.5 Docking Studies in ARDS Targets
- 1.8 Summary of Hits from Reviewed Literature
- 1.9 Concluding Remarks
- 1.10 Executive Summary
- Author Contributions
- References
CHAPTER 1: In Silico Approaches for Drug Repurposing for SARS-CoV-2 Infection
-
Published:27 Apr 2022
-
Special Collection: 2022 ebook collection
S. A. Kulkarni and K. Ingale, in The Coronavirus Pandemic and the Future Volume 2, ed. M. D. Waters, A. Dhawan, T. Marrs, D. Anderson, S. Warren, C. L. Hughes, ... C. L. Hughes, The Royal Society of Chemistry, 2022, pp. 1-80.
Download citation file:
This chapter discusses drug repurposing efforts carried out globally for coronavirus disease 2019 (COVID-19) using in silico methods. The concept of drug repurposing, information required for in silico approaches and its importance in the light of the current pandemic is highlighted along with regulatory considerations. The chapter focuses on other potential targets for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as well as human ones for developing therapeutics for COVID-19. It categorizes in silico methods according to structure-based, ligand-based and other approaches. The structure-based approaches are further categorized based on individual targets, for which a summary of some important research reports is provided. The studies targeting the main protease of SARS-CoV-2 outnumber the research on other viral proteins. Although it is known that modulating human targets involved in COVID-19 could be important and beneficial, not much emphasis has been given to computational research on these targets. The main purpose of the in silico research is to provide clues to experimentalists to select drugs for target-wise screening or propose mechanisms of action for selected drugs. The present review assesses the in silico predictions provided in each report based on available experimental data for inhibition of the specific target(s) or inhibition of SARS-CoV-2 cellular infection. Finally, a discussion is provided on the areas of improvement for in silico approaches.
1.1 Introduction
The coronavirus disease 2019 (COVID-19) pandemic that the world is facing currently is a rare and once-in-a-lifetime situation. The fast spread of the virus worldwise has been alarming, and the number of infected people and deaths has been increasing since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection originated in December 2019 in Wuhan, China. Currently, significant efforts are being made to find treatments as well as restrict its spread. In an emergency like this, the usual entire drug discovery effort for finding a new drug is not feasible (it takes approximately 10–12 years to identify a new drug and get it approved by the regulatory authorities). In view of this, scientific efforts have been focused on a drug-repurposing approach to identifying treatment for COVID-19. Both antiviral drugs that have been approved for the treatment of SARS-CoV-2 infection (remdesivir and favipiravir) have emerged from drug repurposing.
1.1.1 What is Drug Repurposing?
Drug repurposing is investigating the use of an existing drug to treat an entirely different disease, and is carried out using two pathways:
through known target(s) of the drug where new information shows that this target is also involved in new disease etiology;
through identification of new target(s) for the drug by which it alleviates the new disease.
In the case of COVID-19, both approaches are valid. Some drugs have been proposed to work through known targets of SARS-CoV-2 (e.g. remdesivir is an inhibitor of RNA-dependent RNA polymerase), while a few drugs have been identified to work through entirely new targets (e.g. dipyridamole as an inhibitor of the main protease of SARS-CoV-2).
1.1.2 Why Drug Repurposing?
Repurposing opportunities exist because drugs modulate multiple biological targets, designated as on- and off-targets, that are involved in multiple biological processes. Based on drug promiscuity data it is believed that every drug has a potential to bind to approximately 6–7.5 targets.1 As drug discovery pipelines are focused on a disease of interest, the therapeutic application in other areas is likely to be missed. These drugs can either be approved and marketed compounds used daily in clinical settings, or drugs that have been shelved (where molecules failed in clinical trials or for which projects have been discontinued for various reasons). Thus, drug repurposing can be defined as expanding the scope of approved drugs and reviving the shelved ones.
As the safety, tolerability, pharmacokinetics, pharmacodynamics and clinical information in humans is known for repurposed drugs, the existing dose and formulation, if suitable, can be directly investigated in clinical study for the treatment of a new disease. If a higher dose or frequency of dosing is required, it can be investigated in clinical study if a known therapeutic window allows it; otherwise more safety data are required. Similarly, if a new route of administration is required, relevant bridging toxicity information suitable for the new route of administration should be generated and all phases of clinical studies are required.
Dr Christopher Austin (Director, National Center for Advancing Translational Sciences at the United States National Institutes of Health) has remarked that “Drug repurposing seems tantalizingly simple. Conservatively, there are 6500 human diseases that have no regulatory-approved treatments whatsoever. At the current rate of progress, it will be 2000 years before every human disease is treatable. What percentage of those 6500 currently untreatable diseases is ameliorable, to some degree, by a drug you can get at [your local pharmacy]? Shame on us if we can't figure out a way to make these available to patients suffering from disabling and lethal diseases. This is an eminently solvable problem.”2
The world has already seen the success of drug repurposing in terms of remdesivir, a drug for the Ebola virus which has been approved and used for the treatment of SARS-CoV-2 infection.
1.1.2.1 Regulatory Considerations
The regulatory pathway to develop a repurposed drug is less elaborate, since existing safety, pharmacokinetics and toxicity information generated for original indication can be used, and only the required bridging studies need be performed to establish safety of the drug in new disease. However, clinical trials are required to establish efficacy in a new disease before filing a new drug application. The United States Food and Drug Administration (FDA) offers 3 years of data exclusivity for a new use of a previously marketed drug, independent of applicable patents.
1.1.3 Why Drug Repurposing for COVID-19?
With a new infection such as SARS-CoV-2, finding a repurposed drug is the most viable option, since new discovery will take significant time, and by the time a drug would be approved for treatment, substantial human loss would already have taken place, and the disease may not remain relevant. In addition, repurposing has enormous advantages over novel drugs in terms of faster commencement of clinical trials, as the drug may be available off the shelf. If found effective in well-designed clinical studies, the repurposed drug would benefit from:
available bulk active pharmaceutical ingredient and formulation manufacturing processes and immediate accessibility to patients;
lower cost compared to new drug discovery, as safety data for the drug candidate are available, and only efficacy in the new disease is to be established;
shorter time to market;
existing awareness among physicians about the drug, its side-effects, contraindications and drug–drug interactions, based on which it can be prescribed, keeping in view the pre-existing conditions of the patient.
At present there are approximately 269 repurposed drugs in clinical investigation for COVID-19, many of which have been identified using in silico methods. The present review analyses and reports methods that are useful in exploring such repurposing opportunities.
1.1.4 Why In Silico Approaches for Drug Repurposing?
In general, several approaches for drug repurposing, including in silico methods, have been used by various researchers. Some of the experiment-based drug repurposing approaches are mentioned here. However, these are only representative and by no means exhaustive.
1.1.4.1 Gene Profiling
Genetic expression profiling has been used by several groups in the context of drug repurposing. The mRNA expression data from patients with various diseases is compared with similar data on mRNA expressed in vitro by a set of drugs. The expression data are then analysed in terms of signatures and drugs that have the opposite signature to the disease are identified. This approach showed that the histamine H2 antagonist cimetidine would be effective in lung cancer, and the antiepileptic topiramate would be effective in inflammatory bowel disease.3 Although this approach may be useful in cases of acute respiratory distress syndrome (ARDS) in COVID-19, it has not been reported yet.
1.1.4.2 In Vitro Screening
An in vitro screen is routinely utilized for drug repurposing.4 In one study, a primary organotypic spinal cord slice culture was used to screen FDA-approved compounds. The results identified interesting and previously unknown effects for ceftriaxone and harmine as upregulators of the glutamate transporter GLT-1. GLT-1 in glia had previously been reported to be associated with the development of amyotrophic lateral sclerosis.5
Cell-based high-throughput screening of 4910 drug-like compounds in four prostate cancer and two non-malignant prostate cell lines identified disulfiram, a drug used for alcohol abuse, as a selective antineoplastic agent.6 A zebrafish model used to evaluate medications for tobacco dependence found that apomorphine and topiramate modified nicotine-induced and ethanol-induced behavior.7
This standard approach has been used for screening drugs against SARS-CoV-2 infection and some of its targets8–10 (also see ref. 173).
1.1.4.3 Phenotypic Screening
This approach identifies compounds that show disease-relevant effects in model systems without prior knowledge of the target(s) affected. A discovery platform for screening known drug-like compounds using a battery of in vivo assays spanning a broad range of therapeutic areas have been explored.11 This approach could identify new indication for the failed drug MLR1023 from Pfizer as a potential new antidiabetic agent via insulin sensitization.12
1.1.4.4 Binding Assays for Target Interactions
Proteomic techniques such as affinity chromatography and mass spectrometry have been used to identify binding partners for an increasing number of drugs to identify their targets for drug repurposing opportunities.13
1.1.4.5 Cellular Thermal Shift Assay
This has been used to predict thermal stabilization of target proteins by drug-like ligands that possess the appropriate cellular affinity.14 In vitro competition binding assay has been used to evaluate 38 kinase inhibitors against a panel of 317 distinct human protein kinases which exhaustively identified a total of 3175 binding interactions. Sorafenib and dasatinib showed higher affinity to secondary kinase targets than their known primary target, potentially limiting their use in patient populations.15
1.1.4.6 The In Silico Effort
Among several experimental approaches, only phenotype screening and in vitro screening against the virus have been used for drug repurposing for COVID-19, since reliable target-based assays for high-throughput screening were not available initially for SARS-CoV-2. Furthermore, exhaustive screening of all drugs with all targets associated with COVID-19 is an enormous task requiring huge resources. Using in silico methods, such exhaustive screening is indeed feasible, but has rarely been reported by research groups. We have carried out one such effort using in-house reverse virtual high-throughput screening technology on 21 viral and human targets. This has yielded a shortlist of 42 candidates, of which four were tested for SARS-CoV-2 inhibition and found to be active; one of them is undergoing a phase 3 clinical trial in India.
The discussion hereafter will focus on in silico efforts for drug repurposing reported on SARS-CoV-2 and human targets involved in COVID-19. The in silico approaches have been categorized as structure-based, ligand-based and other approaches. Details of the methods of these approaches and how they have been used for drug repurposing have been compiled recently.16 The present review summarizes some major in silico drug repurposing efforts reported in the literature at 31 August 2020. The review only covers different in silico approaches used for repurposing of already approved drugs. The screening of databases other than those of approved drugs, such as ZINC, phytochemicals, polyphenols, dietary supplements or any other compound collection is not covered in this review. Furthermore, comments are made by the authors on the utility and success of reported computational efforts based on existing experimental results. The consensus screening methodology emerging from various research articles is presented and future directions are also discussed.
1.2 Understanding SARS-CoV-2 from an In Silico Perspective
Coronaviruses are a family of single-stranded RNA viruses with a relatively large size. The viral membrane encapsulating the RNA is studded with spike glycoprotein which gives the coronaviruses their crown-like appearance (Figure 1.1).
SARS-CoV-2 is a member of the coronavirus family containing a 29.9 kb RNA messenger, encoding 13 open reading frames, as shown in Figure 1.2.
This genome encodes several structural and nonstructural proteins, with 28 proteins in all (https://viralzone.expasy.org/9076). Structural proteins are important for virion assembly as well as for causing infection. The four different types of structural proteins are N (nucleocapsid), S (spike), E (envelope) and M (membrane). The N-protein (419 aa) encapsidates the viral genome, while the other structural proteins S, E and M comprise the surrounding lipid bilayer envelope. The M- (222 aa) and E- (75 aa) proteins are required for virus morphogenesis, assembly and budding, whereas the S-glycoprotein (1273 aa) is a fusion viral protein comprising two subunits (S1 and S2). The S1 subunit comprises a signal peptide, N-terminal domain (NTD) and receptor-binding domain (RBD) of 253 aa, and the S2 unit comprises 608 aa. The orf1ab gene constitutes the major portion (two-thirds) of the genome, and encodes for 16 non-structural proteins (NSPs): NSP1 to NSP16. Furthermore, there are eight accessory proteins in the genome of SARS-CoV-2: Orf3a, Orf6, Orf7a, Orf7b, Orf8, Orf9b, Orf10 and Orf14.
1.2.1 Structural Proteins of SARS-CoV-2
1.2.1.1 Surface Spike Protein (S-protein)
The SARS-CoV-2 spike glycoprotein (S-protein) mediates viral entry into the host cells by binding to surface angiotensin-converting enzyme (ACE)-2 receptors. The S1 and S2 subunits of the S-protein are responsible for SARS-CoV-2 binding to host cell surface receptors and mediating virus–cell and cell–cell membrane fusion, respectively. The entry of SARS-CoV-2 into cells is dependent on cell–cell fusion mediated by cleavage of the S-protein by the host protease furin at the S1/S2 site. The activation of S-protein by TMPRSS2 in human lung cells is dependent on the furin-mediated pre-cleavage of the S-protein at the S1/S2 site.17 Along with the spike structural integrity, this cleavage activation by furin, cathepsin L, neuropilin-1 and TMPRSS2 plays a key role in the invasion and virulence of SARS-CoV-2. Spike protein is a vital component of the virus, as it also plays a key role in neutralizing host antibodies and the T-cell response, thus compromising the host's immunity.18
The S1 domain is marked as a major antigen on the surface of the coronavirus and comprises the NTD and the C-terminal domain. The head region of S1 comprises the RBD, which is responsible for recognizing the host cell receptor (ACE-2). The membrane fusion between the viral particles and the host receptor is initiated by interactions between key residues of the RDB and ACE-2. Interacting residues between viral RBD and host ACE-2 receptors are identified to be LEU455, PHE486, GLN493, SER494 and ASN501.19 Another report identified LYS455, PHE456, ALA475, PHE486, PHE490 and GLN493 residues to be involved in the spike RBD and ACE-2 binding.20
1.2.1.2 Nucleocapsid Protein (N-protein)
The nucleocapsid (N)-protein packages the positive-strand viral genome RNA into a helical ribo-nucleocapsid and plays a fundamental role during virion assembly through its interactions with the viral genome and membrane (M)-protein. N-protein is crucial in enhancing the efficiency of sub-genomic viral RNA transcription and viral replication. It regulates cellular processes such as cytoskeleton reorganization or host cell apoptosis and the cell cycle. In addition, N-protein can elicit the protective immune response of the host, due to highly immunogenic phosphoprotein.
N-protein is used as a diagnostic tool, due to its conserved amino acid sequence and strong immunogenicity. Quantification of virus in a test sample can be done by quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR) followed by immunofluorescence microscopy to determine N-protein expression 48 h post-infection. The N terminal RNA-binding domain (NTD), SER/ARG (SR)-rich central linker region and C-terminal dimerization domain (CTD) are the three domains of N-protein. The central linker is the chief phosphorylation site, while the NTD and CTD are involved in executing the binding and oligomerization of RNA.21 A reported structural analysis shows that the NTD rich in basic amino acids is a fist-shaped structure, while flexibility is provided by the loops of β-sheets in the core region.21
1.2.1.3 Envelope Protein (E-protein)
The envelope (E)-protein is the smallest of the SARS-CoV-2 structural proteins, with a 8.4–12 kDa size. E-protein comprises of two distinct domains; the charged cytoplasmic tail, and the hydrophobic domain. It is an integral membrane protein embedded in the envelope, but also localized in the endoplasmic reticulum (ER), golgi and ER–golgi apparatus intermediate compartment (ERGIC), once a host cell has been infected. The pentamer formed functions as an ion channel, hence the name E-channel or viroporin17 .
The viral morphogenesis requires E-protein to play a special role, especially during viral assembly and viral progress. The E-protein also assists in increasing viral titer and mature progenies. Studies have shown that by co-ordinating with other intracellular proteins, the E-protein modulates their activity. The SARS-CoV-2 E-protein can also act as a virulence factor.
1.2.1.4 Membrane Protein (M-protein)
The membrane (M)-protein provides the framework for the virion particle. The M-protein spans an envelope thrice, comprising a long C terminal and short N-terminal. M-protein, along with other structural proteins, forms a special assembly. M-protein is also involved in aiding protein–protein interaction and maintaining viral intracellular homeostasis. This protein is glycosylated in the Golgi apparatus. This glycosylation of the M-protein is important for the virion to fuse into the cell and to make protein antigenic. The M-protein plays a key role in stimulating virions in the cell. The virus N-protein also forms a complex by binding to genomic RNA and the M-protein elicits the production and formation of interacting virions in the ERGIC with this complex.
1.2.2 Non-structural and Accessory Proteins of SARS-CoV-2
The non-structural and accessory proteins play vital roles in several functions such as viral transcription, replication, etc. described in Table 1.1.
Function of non-structural and accessory proteins of SARS-CoV-2a
Protein (AA) . | Other names . | Function . |
---|---|---|
NSP1 (180) | Induces host mRNA (leader protein) cleavage, induces specifically host mRNA degradation and inhibits IFN-1 production | |
NSP2 (638) | Binds to host proteins prohibitin 1 and 2 | |
NSP3a (1945) | Papain-like proteinase | Releases NSPs 1, 2 and 3 and antagonizes the host's innate immunity |
NSP4 (500) | Membrane rearrangement | |
NSP5a (306) | 3C-like proteinase (3CLpro) or main protease (Mpro) | Cleaves at 11 sites of NSP polyprotein, directly mediates the maturation of NSPs |
NSP6 (290) | Generates autophagosomes | |
NSP7 (83) | Dimerizes with NSP8, co-factor of NSP12 | |
NSP8 (198) | Stimulates NSP12 | |
NSP9 (113) | Binds to helicase, RNA binding protein | |
NSP10 (139) | Co-factor for activation of NSP14 and NSP16 replicative enzymes | |
NSP11 (13) | Unknown | |
NSP12a (932) | RNA-dependent RNA polymerase (RdRp) | Copies viral RNA from an RNA template |
NSP13 (601) | Helicase | Unwinds duplex RNA, 5′-capping of RNA, antagonizez IFN-1 |
NSP14 (527) | 5′-capping of RNA (3′ to 5′ exonuclease, guanine N7-methyltransferase), antagonizes IFN-1 | |
NSP15 (346) | EndoRNAse/endoribonuclease | Degrades RNA to evade host defense, antagonizes IFN-1 |
NSP16 (298) | 2′-O-ribose-methyltransferase | 5′-cap RNA, methylation (adenine) |
Orf3a (275) | Activates the NLRP3 inflammasome, virus replication, pathogenesis | |
Orf6 (61) | Blocks the expression of STAT1-activated genes inhibiting antiviral activity | |
Orf7a (121) | Involved in blockage of cell cycle progression at G0/G1 phase | |
Orf7b (43) | Essential for Golgi compartment localization | |
Orf8 (121) | Disrupts IFN-1 signaling | |
Orf9b (97) | Inhibits IFN-1 production | |
Orf10 (38) | Encodes a functional protein | |
Orf14 (73) | May play a role in host–virus interaction. |
Protein (AA) . | Other names . | Function . |
---|---|---|
NSP1 (180) | Induces host mRNA (leader protein) cleavage, induces specifically host mRNA degradation and inhibits IFN-1 production | |
NSP2 (638) | Binds to host proteins prohibitin 1 and 2 | |
NSP3a (1945) | Papain-like proteinase | Releases NSPs 1, 2 and 3 and antagonizes the host's innate immunity |
NSP4 (500) | Membrane rearrangement | |
NSP5a (306) | 3C-like proteinase (3CLpro) or main protease (Mpro) | Cleaves at 11 sites of NSP polyprotein, directly mediates the maturation of NSPs |
NSP6 (290) | Generates autophagosomes | |
NSP7 (83) | Dimerizes with NSP8, co-factor of NSP12 | |
NSP8 (198) | Stimulates NSP12 | |
NSP9 (113) | Binds to helicase, RNA binding protein | |
NSP10 (139) | Co-factor for activation of NSP14 and NSP16 replicative enzymes | |
NSP11 (13) | Unknown | |
NSP12a (932) | RNA-dependent RNA polymerase (RdRp) | Copies viral RNA from an RNA template |
NSP13 (601) | Helicase | Unwinds duplex RNA, 5′-capping of RNA, antagonizez IFN-1 |
NSP14 (527) | 5′-capping of RNA (3′ to 5′ exonuclease, guanine N7-methyltransferase), antagonizes IFN-1 | |
NSP15 (346) | EndoRNAse/endoribonuclease | Degrades RNA to evade host defense, antagonizes IFN-1 |
NSP16 (298) | 2′-O-ribose-methyltransferase | 5′-cap RNA, methylation (adenine) |
Orf3a (275) | Activates the NLRP3 inflammasome, virus replication, pathogenesis | |
Orf6 (61) | Blocks the expression of STAT1-activated genes inhibiting antiviral activity | |
Orf7a (121) | Involved in blockage of cell cycle progression at G0/G1 phase | |
Orf7b (43) | Essential for Golgi compartment localization | |
Orf8 (121) | Disrupts IFN-1 signaling | |
Orf9b (97) | Inhibits IFN-1 production | |
Orf10 (38) | Encodes a functional protein | |
Orf14 (73) | May play a role in host–virus interaction. |
AA: number of amino acid residues in the protein; IFN: interferon.
The structural and non-structural as well as accessory viral proteins are considered as drug targets, and in silico effort on these largely depends on available information in terms of three-dimensional (3D) structures of these proteins and drug/ligand binding to these targets.
1.2.3 Structure of SARS-CoV-2 Proteins
The 3D structure of several SARS-CoV-2 proteins have been solved and are available in the Protein Data Bank (PDB) from www.rcsb.org using X-ray, nuclear magnetic resonance (NMR) imaging or electron microscopy. Currently there are 380 structures reported in the PDB, and Table 1.2 provides structural information of viral targets.
Protein structures of SARS-CoV-2 available in the PDB
Macromolecule name . | Number of structures in PDB . | Resolution range . | Number of ligands . |
---|---|---|---|
Spike glycoprotein (S-protein) | 75 | 1.5 to 6.8 Å | 1 |
Nucleocapsid protein (N-protein) | 10 | 1.361 to 2.7 Å | 0 |
NSP1 | 11 | 2.6 to 3.2 Å | 0 |
NSP3a/papain-like proteinase (PLpro) | 24 | 0.95 to 3.18 Å | 3 |
NSP5/Main protease (Mpro) | 172 | 1.25 to 2.35 Å | 151 |
NSP7 | 15 | 1.5 to 3.7 Å | 0 |
NSP8 | 15 | 1.5 to 3.7 Å | 0 |
NSP9 | 3 | 2.0 to 2.95 Å | 0 |
NSP10 | 13 | 1.8 to 2.55 Å | 3 |
NSP12a/RNA-directed RNA polymerase (RdRp) | 10 | 2.5 to 3.7 Å | 0 |
NSP13 | 2 | 1.94 to 3.5 Å | 1 |
NSP15/uridylate-specific endoribonuclease | 7 | 1.82 to 2.35 Å | 3 |
NSP16/2′-O-methyltransferase | 20 | 1.8 to 2.4 Å | 16 |
ORF3a | 1 | 2.9 Å | 0 |
ORF7a | 1 | 2.9 Å | 0 |
ORF8 | 1 | 2.04 Å | 0 |
ORF9b | 1 | 1.95 Å | 1 |
Macromolecule name . | Number of structures in PDB . | Resolution range . | Number of ligands . |
---|---|---|---|
Spike glycoprotein (S-protein) | 75 | 1.5 to 6.8 Å | 1 |
Nucleocapsid protein (N-protein) | 10 | 1.361 to 2.7 Å | 0 |
NSP1 | 11 | 2.6 to 3.2 Å | 0 |
NSP3a/papain-like proteinase (PLpro) | 24 | 0.95 to 3.18 Å | 3 |
NSP5/Main protease (Mpro) | 172 | 1.25 to 2.35 Å | 151 |
NSP7 | 15 | 1.5 to 3.7 Å | 0 |
NSP8 | 15 | 1.5 to 3.7 Å | 0 |
NSP9 | 3 | 2.0 to 2.95 Å | 0 |
NSP10 | 13 | 1.8 to 2.55 Å | 3 |
NSP12a/RNA-directed RNA polymerase (RdRp) | 10 | 2.5 to 3.7 Å | 0 |
NSP13 | 2 | 1.94 to 3.5 Å | 1 |
NSP15/uridylate-specific endoribonuclease | 7 | 1.82 to 2.35 Å | 3 |
NSP16/2′-O-methyltransferase | 20 | 1.8 to 2.4 Å | 16 |
ORF3a | 1 | 2.9 Å | 0 |
ORF7a | 1 | 2.9 Å | 0 |
ORF8 | 1 | 2.04 Å | 0 |
ORF9b | 1 | 1.95 Å | 1 |
The target-wise structure data currently available in the PDB are provided in Table 1.3.
Details of protein structures of SARS-CoV-2 available in the PDBa
PDB . | Resolution (Å) . | Target Name . | Ligand ID . | Cavity . | PDB ID . | Resolution (Å) . | Target Name . | Ligand ID . | Cavity . |
---|---|---|---|---|---|---|---|---|---|
6WQ3 | 2.10 | NSP16 | SAH | SAM binding site | 6ZME | 3.00 | NSP1 | ||
6WRZ | 2.25 | NSP16 | SAH | 6ZLW | 2.60 | NSP1 | |||
6WVN | 2.00 | NSP16 | SAM | 6ZM7 | 2.70 | NSP1 | |||
6XKM | 2.25 | NSP16 | SAM | 6ZMT | 3.00 | NSP1 | |||
6W61 | 2.00 | NSP16 | SAM | 6ZN5 | 3.20 | NSP1 | |||
6WJT | 2.00 | NSP16 | SAH | 6ZMI | 2.60 | NSP1 | |||
6WKS | 1.80 | NSP16 | SAM | 6ZMO | 3.10 | NSP1 | |||
6W4H | 1.80 | NSP16 | SAM | 6ZOJ | 2.80 | NSP1 | |||
6W75 | 1.95 | NSP16 | SAM | 6ZP4 | 2.90 | NSP1 | |||
6WKQ | 1.98 | NSP16 | SFG | 6ZOK | 2.80 | NSP1 | |||
6YZ1 | 2.40 | NSP16 | SFG | 6ZON | 3.00 | NSP1 | |||
7BQ7 | 2.37 | NSP16 | SAM | 6WQ3 | 2.10 | NSP10 | |||
5REG | 1.67 | Mpro | LWA | Allosteric cavity 1 | 6WRZ | 2.25 | NSP10 | ||
5RF8 | 1.44 | Mpro | SFY | 6WVN | 2.00 | NSP10 | |||
5RE7 | 1.79 | Mpro | T0S | Allosteric cavity 2 | 6XKM | 2.25 | NSP10 | ||
5RE8 | 1.81 | Mpro | T0V | 6W4H | 1.80 | NSP10 | |||
5RF4 | 1.61 | Mpro | T5Y | 6W61 | 2.00 | NSP10 | |||
5RF9 | 1.43 | Mpro | S7D | 6W75 | 1.95 | NSP10 | |||
5RFD | 1.41 | Mpro | T6J | 6WJT | 2.00 | NSP10 | |||
5RGJ | 1.34 | Mpro | U0S | 6WKS | 1.80 | NSP10 | |||
6XB1 | 1.8 | Mpro | NEN | Allosteric cavity 3 | 6WKQ | 1.98 | NSP10 | ||
6XB2 | 2.1 | Mpro | NEN | 6YZ1 | 2.40 | NSP10 | |||
5REC | 1.73 | Mpro | T1J | Allosteric cavity 4 | 6ZCT | 2.55 | NSP10 | ||
5REE | 1.77 | Mpro | T1M | 7BQ7 | 2.37 | NSP10 | |||
5RGS | 1.72 | Mpro | S7V | 6WQD | 1.95 | NSP7 | |||
5RE5 | 2.07 | Mpro | T0J | Allosteric cavity 5 | 6WTC | 1.85 | NSP7 | ||
5RE6 | 1.87 | Mpro | O0S | 6XEZ | 3.50 | NSP7 | |||
5REA | 1.63 | Mpro | JGP | Allosteric cavity 6 | 6XIP | 1.50 | NSP7 | ||
5RFB | 1.48 | Mpro | K3S | 6XQB | 3.40 | NSP7 | |||
5RFC | 1.4 | Mpro | K1Y | 6WIQ | 2.85 | NSP7 | |||
5RGG | 2.26 | Mpro | NZD | 7C2K | 2.93 | NSP7 | |||
6YVF | 1.6 | Mpro | A82 | 7BTF | 2.95 | NSP7 | |||
6WNP | 1.44 | Mpro | U5G | Active site | 7BV1 | 2.80 | NSP7 | ||
5R7Y | 1.65 | Mpro | JFM | 7BV2 | 2.50 | NSP7 | |||
5R7Z | 1.59 | Mpro | HWH | 7BW4 | 3.70 | NSP7 | |||
5R80 | 1.93 | Mpro | RZG | 7BZF | 3.26 | NSP7 | |||
5R81 | 1.95 | Mpro | RZJ | 6YYT | 2.90 | NSP7 | |||
5R82 | 1.31 | Mpro | RZS | 6M5I | 2.49 | NSP7 | |||
5R83 | 1.58 | Mpro | K0G | 6M71 | 2.90 | NSP7 | |||
5R84 | 1.83 | Mpro | GWS | 6WQD | 1.95 | NSP8 (C-terminal) | |||
5RE4 | 1.88 | Mpro | SZY | 6WTC | 1.85 | NSP8 (C-terminal) | |||
5RE9 | 1.72 | Mpro | LPZ | 6XEZ | 3.50 | NSP8 | |||
5REB | 1.68 | Mpro | T0Y | 6XIP | 1.50 | NSP8 (C-terminal) | |||
5REF | 1.61 | Mpro | 6SU | 6XQB | 3.40 | NSP8 | |||
5REH | 1.8 | Mpro | AWP | 6WIQ | 2.85 | NSP8 (C-terminal) | |||
5REJ | 1.72 | Mpro | T1V | 7C2K | 2.93 | NSP8 | |||
5REK | 1.74 | Mpro | T1Y | 7BTF | 2.95 | NSP8 | |||
5REL | 1.62 | Mpro | T2G | 7BV1 | 2.80 | NSP8 | |||
5REM | 1.96 | Mpro | T2J | 7BV2 | 2.50 | NSP8 | |||
5REN | 2.15 | Mpro | T2V | 7BW4 | 3.70 | NSP8 | |||
5REO | 1.88 | Mpro | T2Y | 7BZF | 3.26 | NSP8 | |||
5REP | 1.81 | Mpro | T3G | 6YYT | 2.90 | NSP8 | |||
5RER | 1.88 | Mpro | T3J | 6M5I | 2.49 | NSP8 | |||
5RES | 1.65 | Mpro | T3V | 6M71 | 2.90 | NSP8 | |||
5RET | 1.68 | Mpro | T47 | 6WXD | 2.00 | NSP9 | |||
5REU | 1.69 | Mpro | T4D | 6W4B | 2.95 | NSP9 | |||
5REV | 1.6 | Mpro | T4J | 6WC1 | 2.40 | NSP9 | |||
5REW | 1.55 | Mpro | T4M | 6M3M | 2.70 | Nucleocapsid (N-terminal) | |||
5REX | 2.07 | Mpro | T4V | 6VYO | 1.70 | Nucleocapsid (N-terminal) | |||
5REY | 1.96 | Mpro | T4Y | 6WJI | 2.05 | Nucleocapsid (C-terminal) | |||
5REZ | 1.79 | Mpro | T54 | 6WKP | 2.67 | Nucleocapsid (N-terminal) | |||
5RF0 | 1.65 | Mpro | T5D | 6WZO | 1.42 | Nucleocapsid (N-terminal) | |||
5RF1 | 1.73 | Mpro | T5G | 6WZQ | 1.45 | Nucleocapsid (N-terminal) | |||
5RF2 | 1.53 | Mpro | HVB | 6YUN | 1.44 | Nucleocapsid (C-terminal) | |||
5RF3 | 1.5 | Mpro | T5V | 6ZCO | 1.36 | Nucleocapsid (C-terminal) | |||
5RF6 | 1.45 | Mpro | NTG | 7C22 | 2.00 | Nucleocapsid (C-terminal) | |||
5RF7 | 1.54 | Mpro | T67 | 6XDC | 2.90 | ORF3a | |||
5RFA | 1.52 | Mpro | JGY | 6W37 | 2.90 | ORF7a | |||
5RFE | 1.46 | Mpro | JGG | 6Z4U | 1.95 | ORF9b | |||
5RFF | 1.78 | Mpro | T6M | 6WOJ | 2.20 | NSP3 (ADRP) | APR | ADP-ribose binding site | |
5RFG | 2.32 | Mpro | T6V | 6W02 | 1.50 | NSP3 (ADRP) | APR | ||
5RFH | 1.58 | Mpro | T6Y | 6W6Y | 1.45 | NSP3 (ADRP) | AMP | ||
5RFI | 1.69 | Mpro | T71 | 6WCF | 1.06 | NSP3 (ADRP) | |||
5RFJ | 1.8 | Mpro | T7A | 6WEN | 1.35 | NSP3 (ADRP) | |||
5RFK | 1.75 | Mpro | T7D | 6WEY | 0.95 | NSP3 (ADRP) | |||
5RFL | 1.64 | Mpro | T7G | 6YWK | 2.20 | NSP3 (ADRP) | |||
5RFM | 2.06 | Mpro | T7J | 6YWM | 2.16 | NSP3 (ADRP) | |||
5RFN | 1.8 | Mpro | T7P | 6VXS | 2.03 | NSP3 (ADRP) | |||
5RFO | 1.83 | Mpro | T7S | 6YWL | 2.50 | NSP3 (ADRP) | |||
5RFP | 2.03 | Mpro | T7V | 6WZU | 1.79 | NSP3 (PLpro) | |||
5RFQ | 1.76 | Mpro | T7Y | 6WRH | 1.60 | NSP3 (PLpro) | |||
5RFR | 1.71 | Mpro | T81 | 6XA9 | 2.90 | NSP3 (PLpro) | |||
5RFS | 1.7 | Mpro | T84 | 6XAA | 2.70 | NSP3 (PLpro) | |||
5RFT | 1.58 | Mpro | T8A | 6XG3 | 2.48 | NSP3 (PLpro) | |||
5RFU | 1.53 | Mpro | T8D | 6YVA | 3.18 | NSP3 (PLpro) | |||
5RFV | 1.48 | Mpro | T8J | 6W9C | 2.70 | NSP3 (PLpro) | |||
5RFW | 1.43 | Mpro | T8M | 6WUU | 2.79 | NSP3 (PLpro) | VIR250 | Catalytic site | |
5RFX | 1.55 | Mpro | T8P | 6WX4 | 1.65 | NSP3 (PLpro) | VIR251 | ||
5RFY | 1.9 | Mpro | T8S | 7JIR | 2.09 | NSP3 (PLpro) | TTT | PLpro active site | |
5RFZ | 1.68 | Mpro | T8V | 7JIT | 1.95 | NSP3 (PLpro) | Y95 | ||
5RG0 | 1.72 | Mpro | T8Y | 7JIV | 2.05 | NSP3 (PLpro) | VBY | ||
5RG1 | 1.65 | Mpro | T9J | 7JIW | 2.30 | NSP3 (PLpro) | VBY | ||
5RG2 | 1.63 | Mpro | T9M | 7JN2 | 1.93 | NSP3 (PLpro) | Y41 | ||
5RG3 | 1.58 | Mpro | T9P | 6M71 | 2.90 | RdRp | |||
5RGH | 1.7 | Mpro | U0M | 6XEZ | 3.50 | RdRp | |||
5RGI | 1.57 | Mpro | U0P | 6XQB | 3.40 | RdRp | |||
5RGK | 1.43 | Mpro | U0V | 6YYT | 2.90 | RdRp | |||
5RGL | 1.76 | Mpro | U0Y | 7BTF | 2.95 | RdRp | |||
5RGM | 2.04 | Mpro | U1D | 7BV1 | 2.80 | RdRp | |||
5RGN | 1.86 | Mpro | U1A | 7BV2 | 2.50 | RdRp | F86 | Catalytic active site | |
5RGO | 1.74 | Mpro | U1G | 7BW4 | 3.70 | RdRp | |||
5RGP | 2.07 | Mpro | U1M | 7BZF | 3.26 | RdRp | |||
5RGQ | 2.15 | Mpro | U1V | 7C2K | 2.93 | RdRp | |||
5RGT | 2.22 | Mpro | UHS | 6LVN | 2.47 | Spike (HR domain) | |||
5RGU | 2.1 | Mpro | UGD | 6LXT | 2.90 | Spike (S2) | |||
5RGV | 1.82 | Mpro | UGG | 6LZG | 2.50 | Spike (RBD) | |||
5RGW | 1.43 | Mpro | UGM | 6M0J | 2.45 | Spike (RBD) | |||
5RGX | 1.69 | Mpro | UGP | 6M17 | 2.90 | Spike (RBD) | |||
5RGY | 1.97 | Mpro | UGS | 6M1V | 1.50 | Spike (S2) | |||
5RGZ | 1.52 | Mpro | UH1 | 6VSB | 3.46 | Spike | |||
5RH0 | 1.91 | Mpro | UH4 | 6VW1 | 2.68 | Spike (RBD) | |||
5RH1 | 1.96 | Mpro | UGV | 6VXX | 2.80 | Spike | |||
5RH2 | 1.82 | Mpro | UH7 | 6VYB | 3.20 | Spike | |||
5RH3 | 1.69 | Mpro | UHA | 6W41 | 3.08 | Spike (S1) | |||
5RH5 | 1.72 | Mpro | UHV | 6WPS | 3.10 | Spike | |||
5RH6 | 1.6 | Mpro | UHY | 6WPT | 3.70 | Spike | |||
5RH7 | 1.71 | Mpro | UJ1 | 6X29 | 2.70 | Spike | |||
5RH8 | 1.81 | Mpro | UHM | 6X2A | 3.30 | Spike | |||
5RH9 | 1.91 | Mpro | UJ4 | 6X2B | 3.60 | Spike | |||
5RHA | 1.51 | Mpro | T8M | 6X2C | 3.20 | Spike | |||
5RHB | 1.43 | Mpro | USD | 6X6P | 3.22 | Spike | |||
5RHC | 1.58 | Mpro | USA | 6X79 | 2.90 | Spike | |||
5RHD | 1.57 | Mpro | US7 | 6XC2 | 3.11 | Spike (S1) | |||
5RHE | 1.56 | Mpro | UPD | 6XC3 | 2.69 | Spike (S1) | |||
5RHF | 1.76 | Mpro | UPJ | 6XC4 | 2.34 | Spike (S1) | |||
6LU7 | 2.16 | Mpro | N3 | 6XC7 | 2.88 | Spike (S1) | |||
6LZE | 1.5 | Mpro | FHR | 6XCM | 3.42 | Spike | |||
6M0K | 1.5 | Mpro | FJC | 6XCN | 3.66 | Spike | |||
6M2N | 2.19 | Mpro | 3WL | 6XDG | 3.90 | Spike (RBD) | |||
6W63 | 2.1 | Mpro | X77 | 6XE1 | 2.75 | Spike (S1) | |||
6W9Q | 2.05 | Mpro | PO4 | 6XEY | 3.25 | Spike | |||
6WTJ | 1.9 | Mpro | K36 | 6XKL | 3.21 | Spike | |||
6WTK | 2 | Mpro | UED | 6XLU | 2.40 | Spike | |||
6WTT | 2.15 | Mpro | K36 | 6XM0 | 2.70 | Spike | |||
6WTT | 2.15 | Mpro | K36 | 6XM3 | 2.90 | Spike | |||
6XCH | 2.2 | Mpro | Leupeptin | 6XM4 | 2.90 | Spike | |||
6XHM | 1.4 | Mpro | V2M | 6XM5 | 3.10 | Spike | |||
6XMK | 1.7 | Mpro | QYS | 6XR8 | 2.90 | Spike | |||
6XOA | 2.1 | Mpro | EDO | 6XRA | 3.00 | Spike | |||
6XQS | 1.9 | Mpro | SV6 | 6XS6 | 3.70 | Spike | |||
6XQT | 2.3 | Mpro | NNA | 6YLA | 2.42 | Spike (RBD) | |||
6XQU | 2.2 | Mpro | U5G | 6YM0 | 4.36 | Spike (RBD) | |||
6XR3 | 1.45 | Mpro | V7G | 6YOR | 3.30 | Spike (S1) | |||
6Y2F | 1.95 | Mpro | O6K | 6YZ5 | 1.80 | Spike (RBD) | |||
6Y2G | 2.2 | Mpro | O6K | 6YZ7 | 3.30 | Spike (RBD) | |||
6YNQ | 1.8 | Mpro | P6N | 6Z2M | 2.71 | Spike (RBD) | |||
6YT8 | 2.05 | Mpro | PK8 | 6Z43 | 3.30 | Spike | |||
6YZ6 | 1.7 | Mpro | Leupeptin | 6Z97 | 3.40 | Spike | |||
6ZRT | 2.1 | Mpro | SV6 | 6ZBP | 1.85 | Spike (RBD) | |||
6ZRU | 2.1 | Mpro | U5G | 6ZCZ | 2.65 | Spike (RBD) | |||
7BQY | 1.7 | Mpro | N3 | 6ZDG | 4.70 | Spike (ectodomain) | |||
7BRP | 1.8 | Mpro | HU5 | 6ZDH | 3.70 | Spike | |||
7BRR | 1.4 | Mpro | K36 | 6ZER | 3.80 | Spike (RBD) | |||
7BUY | 1.6 | Mpro | JRY | 6ZFO | 4.40 | Spike (ectodomain) | |||
7C7P | 1.74 | Mpro | SV6 | 6ZGE | 2.60 | Spike | |||
7C8R | 2.3 | Mpro | TG3 | 6ZGG | 3.80 | Spike | |||
7C8T | 2.05 | Mpro | NOL | 6ZGH | 6.80 | Spike | |||
7C8U | 2.35 | Mpro | K36 | 6ZGI | 2.90 | Spike | |||
7COM | 2.25 | Mpro | U5G | 6ZHD | 3.70 | Spike | |||
5RED | 1.47 | Mpro | JJG | Allosteric cavity 8 | 6ZOW | 3.00 | Spike | ||
5REI | 1.82 | Mpro | T1S | 6ZOX | 3.00 | Spike | |||
5RF5 | 1.74 | Mpro | HV2 | 6ZOY | 3.10 | Spike | |||
5RGR | 1.41 | Mpro | K1G | 6ZOZ | 3.50 | Spike | |||
5RH4 | 1.34 | Mpro | UHG | 6ZP0 | 3.00 | Spike | |||
6WQF | 2.3 | Mpro | 6ZP1 | 3.30 | Spike | ||||
6WTM | 1.85 | Mpro | 6ZP2 | 3.10 | Spike | ||||
6XB0 | 1.8 | Mpro | 6ZP5 | 3.10 | Spike | ||||
6XHU | 1.8 | Mpro | 6ZP7 | 3.30 | Spike | ||||
6XKF | 1.8 | Mpro | 6ZWV | 3.50 | Spike | ||||
6XKH | 1.28 | Mpro | 7BWJ | 2.85 | Spike (RBD) | ||||
6M03 | 2 | Mpro | 7BYR | 3.84 | Spike | ||||
6M2Q | 1.7 | Mpro | 7BZ5 | 1.84 | Spike (RBD) | ||||
5R8T | 1.27 | Mpro | 7C01 | 2.88 | Spike (S1) | ||||
6XA4 | 1.65 | Mpro | UAW241 | Surface | 7C2L | 3.10 | Spike | ||
6XBG | 1.45 | Mpro | UAW246 | 7C8V | 2.15 | Spike (RBD) | |||
6XBH | 1.6 | Mpro | UAW247 | 7C8W | 2.77 | Spike (RBD) | |||
6XBI | 1.7 | Mpro | UAW248 | 7CAH | 3.90 | Spike (RBD) | |||
6XFN | 1.7 | Mpro | UAW243 | 7CAN | 2.94 | Spike (RBD) | |||
6Y2E | 1.75 | Mpro | 6VWW | 2.20 | NSP15 | ||||
6Y84 | 1.39 | Mpro | 6W01 | 1.90 | NSP15 | ||||
6YB7 | 1.25 | Mpro | 6X1B | 1.97 | NSP15 | ||||
7BRO | 2 | Mpro | 6XDH | 2.35 | NSP15 | ||||
7JFQ | 1.55 | Mpro | 6WLC | 1.82 | NSP15 | U5P | NendoU active site | ||
6XEZ | 3.50 | NSP13 | 6WXC | 1.85 | NSP15 | CMU | |||
6ZSL | 1.94 | NSP13 | 6X4I | 1.85 | NSP15 | U3P | |||
7JTL | 2.04 | ORF8 |
PDB . | Resolution (Å) . | Target Name . | Ligand ID . | Cavity . | PDB ID . | Resolution (Å) . | Target Name . | Ligand ID . | Cavity . |
---|---|---|---|---|---|---|---|---|---|
6WQ3 | 2.10 | NSP16 | SAH | SAM binding site | 6ZME | 3.00 | NSP1 | ||
6WRZ | 2.25 | NSP16 | SAH | 6ZLW | 2.60 | NSP1 | |||
6WVN | 2.00 | NSP16 | SAM | 6ZM7 | 2.70 | NSP1 | |||
6XKM | 2.25 | NSP16 | SAM | 6ZMT | 3.00 | NSP1 | |||
6W61 | 2.00 | NSP16 | SAM | 6ZN5 | 3.20 | NSP1 | |||
6WJT | 2.00 | NSP16 | SAH | 6ZMI | 2.60 | NSP1 | |||
6WKS | 1.80 | NSP16 | SAM | 6ZMO | 3.10 | NSP1 | |||
6W4H | 1.80 | NSP16 | SAM | 6ZOJ | 2.80 | NSP1 | |||
6W75 | 1.95 | NSP16 | SAM | 6ZP4 | 2.90 | NSP1 | |||
6WKQ | 1.98 | NSP16 | SFG | 6ZOK | 2.80 | NSP1 | |||
6YZ1 | 2.40 | NSP16 | SFG | 6ZON | 3.00 | NSP1 | |||
7BQ7 | 2.37 | NSP16 | SAM | 6WQ3 | 2.10 | NSP10 | |||
5REG | 1.67 | Mpro | LWA | Allosteric cavity 1 | 6WRZ | 2.25 | NSP10 | ||
5RF8 | 1.44 | Mpro | SFY | 6WVN | 2.00 | NSP10 | |||
5RE7 | 1.79 | Mpro | T0S | Allosteric cavity 2 | 6XKM | 2.25 | NSP10 | ||
5RE8 | 1.81 | Mpro | T0V | 6W4H | 1.80 | NSP10 | |||
5RF4 | 1.61 | Mpro | T5Y | 6W61 | 2.00 | NSP10 | |||
5RF9 | 1.43 | Mpro | S7D | 6W75 | 1.95 | NSP10 | |||
5RFD | 1.41 | Mpro | T6J | 6WJT | 2.00 | NSP10 | |||
5RGJ | 1.34 | Mpro | U0S | 6WKS | 1.80 | NSP10 | |||
6XB1 | 1.8 | Mpro | NEN | Allosteric cavity 3 | 6WKQ | 1.98 | NSP10 | ||
6XB2 | 2.1 | Mpro | NEN | 6YZ1 | 2.40 | NSP10 | |||
5REC | 1.73 | Mpro | T1J | Allosteric cavity 4 | 6ZCT | 2.55 | NSP10 | ||
5REE | 1.77 | Mpro | T1M | 7BQ7 | 2.37 | NSP10 | |||
5RGS | 1.72 | Mpro | S7V | 6WQD | 1.95 | NSP7 | |||
5RE5 | 2.07 | Mpro | T0J | Allosteric cavity 5 | 6WTC | 1.85 | NSP7 | ||
5RE6 | 1.87 | Mpro | O0S | 6XEZ | 3.50 | NSP7 | |||
5REA | 1.63 | Mpro | JGP | Allosteric cavity 6 | 6XIP | 1.50 | NSP7 | ||
5RFB | 1.48 | Mpro | K3S | 6XQB | 3.40 | NSP7 | |||
5RFC | 1.4 | Mpro | K1Y | 6WIQ | 2.85 | NSP7 | |||
5RGG | 2.26 | Mpro | NZD | 7C2K | 2.93 | NSP7 | |||
6YVF | 1.6 | Mpro | A82 | 7BTF | 2.95 | NSP7 | |||
6WNP | 1.44 | Mpro | U5G | Active site | 7BV1 | 2.80 | NSP7 | ||
5R7Y | 1.65 | Mpro | JFM | 7BV2 | 2.50 | NSP7 | |||
5R7Z | 1.59 | Mpro | HWH | 7BW4 | 3.70 | NSP7 | |||
5R80 | 1.93 | Mpro | RZG | 7BZF | 3.26 | NSP7 | |||
5R81 | 1.95 | Mpro | RZJ | 6YYT | 2.90 | NSP7 | |||
5R82 | 1.31 | Mpro | RZS | 6M5I | 2.49 | NSP7 | |||
5R83 | 1.58 | Mpro | K0G | 6M71 | 2.90 | NSP7 | |||
5R84 | 1.83 | Mpro | GWS | 6WQD | 1.95 | NSP8 (C-terminal) | |||
5RE4 | 1.88 | Mpro | SZY | 6WTC | 1.85 | NSP8 (C-terminal) | |||
5RE9 | 1.72 | Mpro | LPZ | 6XEZ | 3.50 | NSP8 | |||
5REB | 1.68 | Mpro | T0Y | 6XIP | 1.50 | NSP8 (C-terminal) | |||
5REF | 1.61 | Mpro | 6SU | 6XQB | 3.40 | NSP8 | |||
5REH | 1.8 | Mpro | AWP | 6WIQ | 2.85 | NSP8 (C-terminal) | |||
5REJ | 1.72 | Mpro | T1V | 7C2K | 2.93 | NSP8 | |||
5REK | 1.74 | Mpro | T1Y | 7BTF | 2.95 | NSP8 | |||
5REL | 1.62 | Mpro | T2G | 7BV1 | 2.80 | NSP8 | |||
5REM | 1.96 | Mpro | T2J | 7BV2 | 2.50 | NSP8 | |||
5REN | 2.15 | Mpro | T2V | 7BW4 | 3.70 | NSP8 | |||
5REO | 1.88 | Mpro | T2Y | 7BZF | 3.26 | NSP8 | |||
5REP | 1.81 | Mpro | T3G | 6YYT | 2.90 | NSP8 | |||
5RER | 1.88 | Mpro | T3J | 6M5I | 2.49 | NSP8 | |||
5RES | 1.65 | Mpro | T3V | 6M71 | 2.90 | NSP8 | |||
5RET | 1.68 | Mpro | T47 | 6WXD | 2.00 | NSP9 | |||
5REU | 1.69 | Mpro | T4D | 6W4B | 2.95 | NSP9 | |||
5REV | 1.6 | Mpro | T4J | 6WC1 | 2.40 | NSP9 | |||
5REW | 1.55 | Mpro | T4M | 6M3M | 2.70 | Nucleocapsid (N-terminal) | |||
5REX | 2.07 | Mpro | T4V | 6VYO | 1.70 | Nucleocapsid (N-terminal) | |||
5REY | 1.96 | Mpro | T4Y | 6WJI | 2.05 | Nucleocapsid (C-terminal) | |||
5REZ | 1.79 | Mpro | T54 | 6WKP | 2.67 | Nucleocapsid (N-terminal) | |||
5RF0 | 1.65 | Mpro | T5D | 6WZO | 1.42 | Nucleocapsid (N-terminal) | |||
5RF1 | 1.73 | Mpro | T5G | 6WZQ | 1.45 | Nucleocapsid (N-terminal) | |||
5RF2 | 1.53 | Mpro | HVB | 6YUN | 1.44 | Nucleocapsid (C-terminal) | |||
5RF3 | 1.5 | Mpro | T5V | 6ZCO | 1.36 | Nucleocapsid (C-terminal) | |||
5RF6 | 1.45 | Mpro | NTG | 7C22 | 2.00 | Nucleocapsid (C-terminal) | |||
5RF7 | 1.54 | Mpro | T67 | 6XDC | 2.90 | ORF3a | |||
5RFA | 1.52 | Mpro | JGY | 6W37 | 2.90 | ORF7a | |||
5RFE | 1.46 | Mpro | JGG | 6Z4U | 1.95 | ORF9b | |||
5RFF | 1.78 | Mpro | T6M | 6WOJ | 2.20 | NSP3 (ADRP) | APR | ADP-ribose binding site | |
5RFG | 2.32 | Mpro | T6V | 6W02 | 1.50 | NSP3 (ADRP) | APR | ||
5RFH | 1.58 | Mpro | T6Y | 6W6Y | 1.45 | NSP3 (ADRP) | AMP | ||
5RFI | 1.69 | Mpro | T71 | 6WCF | 1.06 | NSP3 (ADRP) | |||
5RFJ | 1.8 | Mpro | T7A | 6WEN | 1.35 | NSP3 (ADRP) | |||
5RFK | 1.75 | Mpro | T7D | 6WEY | 0.95 | NSP3 (ADRP) | |||
5RFL | 1.64 | Mpro | T7G | 6YWK | 2.20 | NSP3 (ADRP) | |||
5RFM | 2.06 | Mpro | T7J | 6YWM | 2.16 | NSP3 (ADRP) | |||
5RFN | 1.8 | Mpro | T7P | 6VXS | 2.03 | NSP3 (ADRP) | |||
5RFO | 1.83 | Mpro | T7S | 6YWL | 2.50 | NSP3 (ADRP) | |||
5RFP | 2.03 | Mpro | T7V | 6WZU | 1.79 | NSP3 (PLpro) | |||
5RFQ | 1.76 | Mpro | T7Y | 6WRH | 1.60 | NSP3 (PLpro) | |||
5RFR | 1.71 | Mpro | T81 | 6XA9 | 2.90 | NSP3 (PLpro) | |||
5RFS | 1.7 | Mpro | T84 | 6XAA | 2.70 | NSP3 (PLpro) | |||
5RFT | 1.58 | Mpro | T8A | 6XG3 | 2.48 | NSP3 (PLpro) | |||
5RFU | 1.53 | Mpro | T8D | 6YVA | 3.18 | NSP3 (PLpro) | |||
5RFV | 1.48 | Mpro | T8J | 6W9C | 2.70 | NSP3 (PLpro) | |||
5RFW | 1.43 | Mpro | T8M | 6WUU | 2.79 | NSP3 (PLpro) | VIR250 | Catalytic site | |
5RFX | 1.55 | Mpro | T8P | 6WX4 | 1.65 | NSP3 (PLpro) | VIR251 | ||
5RFY | 1.9 | Mpro | T8S | 7JIR | 2.09 | NSP3 (PLpro) | TTT | PLpro active site | |
5RFZ | 1.68 | Mpro | T8V | 7JIT | 1.95 | NSP3 (PLpro) | Y95 | ||
5RG0 | 1.72 | Mpro | T8Y | 7JIV | 2.05 | NSP3 (PLpro) | VBY | ||
5RG1 | 1.65 | Mpro | T9J | 7JIW | 2.30 | NSP3 (PLpro) | VBY | ||
5RG2 | 1.63 | Mpro | T9M | 7JN2 | 1.93 | NSP3 (PLpro) | Y41 | ||
5RG3 | 1.58 | Mpro | T9P | 6M71 | 2.90 | RdRp | |||
5RGH | 1.7 | Mpro | U0M | 6XEZ | 3.50 | RdRp | |||
5RGI | 1.57 | Mpro | U0P | 6XQB | 3.40 | RdRp | |||
5RGK | 1.43 | Mpro | U0V | 6YYT | 2.90 | RdRp | |||
5RGL | 1.76 | Mpro | U0Y | 7BTF | 2.95 | RdRp | |||
5RGM | 2.04 | Mpro | U1D | 7BV1 | 2.80 | RdRp | |||
5RGN | 1.86 | Mpro | U1A | 7BV2 | 2.50 | RdRp | F86 | Catalytic active site | |
5RGO | 1.74 | Mpro | U1G | 7BW4 | 3.70 | RdRp | |||
5RGP | 2.07 | Mpro | U1M | 7BZF | 3.26 | RdRp | |||
5RGQ | 2.15 | Mpro | U1V | 7C2K | 2.93 | RdRp | |||
5RGT | 2.22 | Mpro | UHS | 6LVN | 2.47 | Spike (HR domain) | |||
5RGU | 2.1 | Mpro | UGD | 6LXT | 2.90 | Spike (S2) | |||
5RGV | 1.82 | Mpro | UGG | 6LZG | 2.50 | Spike (RBD) | |||
5RGW | 1.43 | Mpro | UGM | 6M0J | 2.45 | Spike (RBD) | |||
5RGX | 1.69 | Mpro | UGP | 6M17 | 2.90 | Spike (RBD) | |||
5RGY | 1.97 | Mpro | UGS | 6M1V | 1.50 | Spike (S2) | |||
5RGZ | 1.52 | Mpro | UH1 | 6VSB | 3.46 | Spike | |||
5RH0 | 1.91 | Mpro | UH4 | 6VW1 | 2.68 | Spike (RBD) | |||
5RH1 | 1.96 | Mpro | UGV | 6VXX | 2.80 | Spike | |||
5RH2 | 1.82 | Mpro | UH7 | 6VYB | 3.20 | Spike | |||
5RH3 | 1.69 | Mpro | UHA | 6W41 | 3.08 | Spike (S1) | |||
5RH5 | 1.72 | Mpro | UHV | 6WPS | 3.10 | Spike | |||
5RH6 | 1.6 | Mpro | UHY | 6WPT | 3.70 | Spike | |||
5RH7 | 1.71 | Mpro | UJ1 | 6X29 | 2.70 | Spike | |||
5RH8 | 1.81 | Mpro | UHM | 6X2A | 3.30 | Spike | |||
5RH9 | 1.91 | Mpro | UJ4 | 6X2B | 3.60 | Spike | |||
5RHA | 1.51 | Mpro | T8M | 6X2C | 3.20 | Spike | |||
5RHB | 1.43 | Mpro | USD | 6X6P | 3.22 | Spike | |||
5RHC | 1.58 | Mpro | USA | 6X79 | 2.90 | Spike | |||
5RHD | 1.57 | Mpro | US7 | 6XC2 | 3.11 | Spike (S1) | |||
5RHE | 1.56 | Mpro | UPD | 6XC3 | 2.69 | Spike (S1) | |||
5RHF | 1.76 | Mpro | UPJ | 6XC4 | 2.34 | Spike (S1) | |||
6LU7 | 2.16 | Mpro | N3 | 6XC7 | 2.88 | Spike (S1) | |||
6LZE | 1.5 | Mpro | FHR | 6XCM | 3.42 | Spike | |||
6M0K | 1.5 | Mpro | FJC | 6XCN | 3.66 | Spike | |||
6M2N | 2.19 | Mpro | 3WL | 6XDG | 3.90 | Spike (RBD) | |||
6W63 | 2.1 | Mpro | X77 | 6XE1 | 2.75 | Spike (S1) | |||
6W9Q | 2.05 | Mpro | PO4 | 6XEY | 3.25 | Spike | |||
6WTJ | 1.9 | Mpro | K36 | 6XKL | 3.21 | Spike | |||
6WTK | 2 | Mpro | UED | 6XLU | 2.40 | Spike | |||
6WTT | 2.15 | Mpro | K36 | 6XM0 | 2.70 | Spike | |||
6WTT | 2.15 | Mpro | K36 | 6XM3 | 2.90 | Spike | |||
6XCH | 2.2 | Mpro | Leupeptin | 6XM4 | 2.90 | Spike | |||
6XHM | 1.4 | Mpro | V2M | 6XM5 | 3.10 | Spike | |||
6XMK | 1.7 | Mpro | QYS | 6XR8 | 2.90 | Spike | |||
6XOA | 2.1 | Mpro | EDO | 6XRA | 3.00 | Spike | |||
6XQS | 1.9 | Mpro | SV6 | 6XS6 | 3.70 | Spike | |||
6XQT | 2.3 | Mpro | NNA | 6YLA | 2.42 | Spike (RBD) | |||
6XQU | 2.2 | Mpro | U5G | 6YM0 | 4.36 | Spike (RBD) | |||
6XR3 | 1.45 | Mpro | V7G | 6YOR | 3.30 | Spike (S1) | |||
6Y2F | 1.95 | Mpro | O6K | 6YZ5 | 1.80 | Spike (RBD) | |||
6Y2G | 2.2 | Mpro | O6K | 6YZ7 | 3.30 | Spike (RBD) | |||
6YNQ | 1.8 | Mpro | P6N | 6Z2M | 2.71 | Spike (RBD) | |||
6YT8 | 2.05 | Mpro | PK8 | 6Z43 | 3.30 | Spike | |||
6YZ6 | 1.7 | Mpro | Leupeptin | 6Z97 | 3.40 | Spike | |||
6ZRT | 2.1 | Mpro | SV6 | 6ZBP | 1.85 | Spike (RBD) | |||
6ZRU | 2.1 | Mpro | U5G | 6ZCZ | 2.65 | Spike (RBD) | |||
7BQY | 1.7 | Mpro | N3 | 6ZDG | 4.70 | Spike (ectodomain) | |||
7BRP | 1.8 | Mpro | HU5 | 6ZDH | 3.70 | Spike | |||
7BRR | 1.4 | Mpro | K36 | 6ZER | 3.80 | Spike (RBD) | |||
7BUY | 1.6 | Mpro | JRY | 6ZFO | 4.40 | Spike (ectodomain) | |||
7C7P | 1.74 | Mpro | SV6 | 6ZGE | 2.60 | Spike | |||
7C8R | 2.3 | Mpro | TG3 | 6ZGG | 3.80 | Spike | |||
7C8T | 2.05 | Mpro | NOL | 6ZGH | 6.80 | Spike | |||
7C8U | 2.35 | Mpro | K36 | 6ZGI | 2.90 | Spike | |||
7COM | 2.25 | Mpro | U5G | 6ZHD | 3.70 | Spike | |||
5RED | 1.47 | Mpro | JJG | Allosteric cavity 8 | 6ZOW | 3.00 | Spike | ||
5REI | 1.82 | Mpro | T1S | 6ZOX | 3.00 | Spike | |||
5RF5 | 1.74 | Mpro | HV2 | 6ZOY | 3.10 | Spike | |||
5RGR | 1.41 | Mpro | K1G | 6ZOZ | 3.50 | Spike | |||
5RH4 | 1.34 | Mpro | UHG | 6ZP0 | 3.00 | Spike | |||
6WQF | 2.3 | Mpro | 6ZP1 | 3.30 | Spike | ||||
6WTM | 1.85 | Mpro | 6ZP2 | 3.10 | Spike | ||||
6XB0 | 1.8 | Mpro | 6ZP5 | 3.10 | Spike | ||||
6XHU | 1.8 | Mpro | 6ZP7 | 3.30 | Spike | ||||
6XKF | 1.8 | Mpro | 6ZWV | 3.50 | Spike | ||||
6XKH | 1.28 | Mpro | 7BWJ | 2.85 | Spike (RBD) | ||||
6M03 | 2 | Mpro | 7BYR | 3.84 | Spike | ||||
6M2Q | 1.7 | Mpro | 7BZ5 | 1.84 | Spike (RBD) | ||||
5R8T | 1.27 | Mpro | 7C01 | 2.88 | Spike (S1) | ||||
6XA4 | 1.65 | Mpro | UAW241 | Surface | 7C2L | 3.10 | Spike | ||
6XBG | 1.45 | Mpro | UAW246 | 7C8V | 2.15 | Spike (RBD) | |||
6XBH | 1.6 | Mpro | UAW247 | 7C8W | 2.77 | Spike (RBD) | |||
6XBI | 1.7 | Mpro | UAW248 | 7CAH | 3.90 | Spike (RBD) | |||
6XFN | 1.7 | Mpro | UAW243 | 7CAN | 2.94 | Spike (RBD) | |||
6Y2E | 1.75 | Mpro | 6VWW | 2.20 | NSP15 | ||||
6Y84 | 1.39 | Mpro | 6W01 | 1.90 | NSP15 | ||||
6YB7 | 1.25 | Mpro | 6X1B | 1.97 | NSP15 | ||||
7BRO | 2 | Mpro | 6XDH | 2.35 | NSP15 | ||||
7JFQ | 1.55 | Mpro | 6WLC | 1.82 | NSP15 | U5P | NendoU active site | ||
6XEZ | 3.50 | NSP13 | 6WXC | 1.85 | NSP15 | CMU | |||
6ZSL | 1.94 | NSP13 | 6X4I | 1.85 | NSP15 | U3P | |||
7JTL | 2.04 | ORF8 |
NSP16: 2′-O-methyltransferase; Mpro: main protease; 3CLpro: 3C-like proteinase; RdRp: RNA-directed RNA polymerase; NSP15: uridylate-specific endoribonuclease; PLpro: papain-like protease.
Some of the structures reported in Table 1.3 have co-crystallized ligands, which can provide information about active site of the enzyme or receptor along with key residues involved in the ligand binding. The common residues that bind to multiple ligands can provide clues for designing molecules that can target these residues. Furthermore, information on co-crystallized ligands for a target can be utilized to develop pharmacophore-based models for screening of drug databases.
1.3 Structure-based Approaches for Drug Repurposing
Structure-based approaches mainly involve molecular docking, molecular dynamics (MD) and free energy of binding calculations for drug–receptor complexes. As an effort to find repurposed drugs using in silico methods, some groups have shared their results in a database (https://covid.molssi.org/, https://shennongproject.ai/). Other groups have contributed in terms of providing a COVID-19 docking server where researchers can submit their compounds for in silico screening (http://ncov.schanglab.org.cn). A similar resource is the D3Targets-2019-nCoV server, where target prediction for drugs is also provided along with docking scores (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). The coronavirus antiviral research database (https://covdb.stanford.edu//) is another example which contains candidate antiviral compounds as well as molecular targets of SARS-CoV-2.
Several drug databases that are used for screening compounds against SARS-CoV-2 therapeutic targets for drug repurposing are listed in Table 1.4.
Databases used for virtual screening against SARS-CoV-2
Database . | Active link . | About . |
---|---|---|
FDA-Approved Drugs Database | https://www.accessdata.fda.gov/scripts/cder/daf/ | Includes information about drugs approved for human use in the United States |
DrugBank | http://www.drugbank.ca | Details of approved drugs (pharmaceuticals and chemicals) |
ZINC Database | http://zinc.docking.org/ | Compilation of commercially available chemical compounds provided by the Shoichet Laboratory, University of California, San Francisco (UCSF) |
ChEMBL | https://www.ebi.ac.uk/chembl/ | Database of small molecules, their interactions and effects on targets |
PubChem | https://pubchem.ncbi.nlm.nih.gov/ | Database of chemical compounds developed by the National Center for Biotechnology Information (NCBI) |
ChemIDPlus | https://chem.nlm.nih.gov/chemidplus/ | Database of chemical compounds and structures from the U.S. National Library of Medicine. |
SelleckChem | https://www.selleckchem.com/screening-libraries.html | Database of bioactive compounds including FDA-approved drugs and those that have completed phase 1 trials |
SWEETLEAD | https://simtk.org/projects/sweetlead | Database of approved medicines, illegal drugs and isolates from traditional medicinal herbs |
NCGC Pharmaceutical Collection | https://tripod.nih.gov/npc/ | Collection of approved and investigational drugs for high-throughput screening |
e-Drug3d | https://chemoinfo.ipmc.cnrs.fr/MOLDB/index.php | FDA-approved drugs and active metabolites |
DrugCentral | https://drugcentral.org/ | Resource for FDA approved drugs and drugs approved outside United States |
CureFFI database | https://www.cureffi.org/2013/10/04/list-of-fda-approved-drugs-and-cns-drugs-with-smiles/ | List of FDA-approved drugs and central nervous system drugs |
BindingDB | https://www.bindingdb.org/bind/index_original.jsp | Database of measured binding affinities |
SwissSimilarity | http://www.swisssimilarity.ch/ | Library of drugs and bioactive compounds |
Database . | Active link . | About . |
---|---|---|
FDA-Approved Drugs Database | https://www.accessdata.fda.gov/scripts/cder/daf/ | Includes information about drugs approved for human use in the United States |
DrugBank | http://www.drugbank.ca | Details of approved drugs (pharmaceuticals and chemicals) |
ZINC Database | http://zinc.docking.org/ | Compilation of commercially available chemical compounds provided by the Shoichet Laboratory, University of California, San Francisco (UCSF) |
ChEMBL | https://www.ebi.ac.uk/chembl/ | Database of small molecules, their interactions and effects on targets |
PubChem | https://pubchem.ncbi.nlm.nih.gov/ | Database of chemical compounds developed by the National Center for Biotechnology Information (NCBI) |
ChemIDPlus | https://chem.nlm.nih.gov/chemidplus/ | Database of chemical compounds and structures from the U.S. National Library of Medicine. |
SelleckChem | https://www.selleckchem.com/screening-libraries.html | Database of bioactive compounds including FDA-approved drugs and those that have completed phase 1 trials |
SWEETLEAD | https://simtk.org/projects/sweetlead | Database of approved medicines, illegal drugs and isolates from traditional medicinal herbs |
NCGC Pharmaceutical Collection | https://tripod.nih.gov/npc/ | Collection of approved and investigational drugs for high-throughput screening |
e-Drug3d | https://chemoinfo.ipmc.cnrs.fr/MOLDB/index.php | FDA-approved drugs and active metabolites |
DrugCentral | https://drugcentral.org/ | Resource for FDA approved drugs and drugs approved outside United States |
CureFFI database | https://www.cureffi.org/2013/10/04/list-of-fda-approved-drugs-and-cns-drugs-with-smiles/ | List of FDA-approved drugs and central nervous system drugs |
BindingDB | https://www.bindingdb.org/bind/index_original.jsp | Database of measured binding affinities |
SwissSimilarity | http://www.swisssimilarity.ch/ | Library of drugs and bioactive compounds |
The in silico structure-based drug repurposing studies for various SARS-CoV-2 targets that are reported in the literature are summarized in this section.
1.3.1 Docking Studies in the Main Protease (Mpro/3CLpro)
Among all SARS-CoV-2 targets, the main protease (Mpro) is the most studied target for in silico repurposing. An important reason for this is availability of structures (using X-ray, electron microscopy, NMR, etc.) with and without co-crystallized ligands. These structures have been utilized by several researchers to identify repurposing opportunities. There are some drugs for which median inhibitory concentration (IC50) in the Mpro assay has been reported,22–25 which are discussed herein. In addition, we refer to the cellular activity of drugs against SARS-CoV-2 infection reported in the literature8–10 and on the website https://ghddi-ailab.github.io/Targeting2019-nCoV/preclinical/. A summary of structure-based studies conducted so far is presented, based on crystal structure or models used, docking programs used, drugs and databases screened, MD program used, duration of MD simulation and free-energy estimation method used. There are 52 publications reporting in silico drug repurposing efforts specifically for the Mpro target, and their compilation using these parameters is provided in Table 1.5.
Summary of Mpro studiesa
PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6M03 | DrugBank | AutoDock Vina | Glecaprevir, maraviroc | 26 | |||
6LU7 | Antivirals, statins | AutoDock Vina | Pitavastatin, rosuvastatin, fluvastatinb | 27 | |||
6LU7 | Thiol-reacting FDA-approved drugs | AutoDock Vina | Disulfiramc, captopril | 29 | |||
6LU7 | 19 in vitro tested or in trial for COVID-19 | AutoDock Vina | NAMD | 5 ns/125 ns | Nil | Entecavir, nelfinavir | 31 |
6LU7, 6M2N | Protease inhibitors | AutoDock | Telaprevir, boceprevir, lopinavir, edoxaban, ritonavir, argatroban, sitagliptin, inogatran. | 32 | |||
6LU7 | 7 HIV-1 protease inhibitors | VINA/VegaZZ | NAMD | 1 ns | Saquinavir | 33 | |
6LU7 | DrugBank | Glide | AMBER | 1 ns/100 ns | MM-PBSA-WSAS | Carfilzomib, eravacycline, valrubicin, lopinavir, elbasvir | 34 |
6Y2G | ChemBL | AutoDock Vina, RDOCK | Eszopiclone | 35 | |||
6LU7 | Antimalarial | Glide | GROMACS | 20 ns | Amodiaquine | 36 | |
5R7Y, 5R7Z, 5R80, 5R81, 5R82 | 61 antiviral drugs | Glide | Lopinavir, asunaprevir, remdesivir, indinavir, ritonavir, galidesivir | 37 | |||
6Y2G | 61 antiviral drugs | AutoDock | Artemisinin, ritonavir, darunavir, lopinavir, vitamin D, vitamin E | 38 | |||
6Y2F | 22 flavonoids, antimalarials, vitamins | AutoDock Vina | YASARA | 10 ns | Lopinavir–ritonavir, tipranavir, raltegravir | 39 | |
6M03, 6LU7, 6Y2F/6Y2G | 75 antiviral, anticancer, antimalarial | Glide | Desmond | 200 ns | MM-GBSA | Leupeptin, pepstatin A, nelfinavir, birinapant, lypressin, octreotide | 40 |
6Y84 | SelleckChem, DrugBank, Repurposing hub | SWISS-DOCK | Emetine, hesperidin, indinavir, itraconazole | 41 | |||
6LU7 | 33 antinematodes antiprotozoals, antifungals, natural products, antivirals | GOLD | YASARA | 100 ns | MM-PBSA | Simeprevir, ergotamine, bromocriptine, tadalafil | 42 |
HM | 1615 FDA-approved drugs from ZINC | iDock | GROMACS | 100 ns | Daunorubicin, ergotamine, ergoloid, bromocriptine, meclocycline, amrubicin, | 43 | |
6W63 | SuperDRUG2 | Glide | MM-GBSA | Binifibrate, macimorelin, bamifylline, rilmazafone, afatinib, ezetimibe | 44 | ||
6LU7 | OTAVA Chemicals (1017 compounds), Machine Learning SARS Targeted Library (1577 compounds) | GOLD | Apixaban | 45 | |||
6LU7 | 606 million ZINC | AutoDock Vina, AutoDock, Glide | Desmond | 20 ns | MM-GBSA | Apixaban, nelfinavir, glecaprevir, rivaroxaban, betrixaban, amprenavir | 46 |
6LU7 | SelleckChem | Glide | Ribavirin, telbivudine, aminosalicylate, pyrazinamide, vitamin B12, nicotinamide, temozolomide | 47 | |||
HM | 7173 DrugsLib | MTiOpenScreen, AutoDock Vina | Velpatasvir, ledipasvir | 48 | |||
6LU7 | 31 anti-HIV drugs | Glide | AMBER | 100 ns | MM-GBSA | Saquinavir | 49 |
6W63 | DrugBank | Glide | AMBER | 100 ns | MM-PBSA | Ritonavir, nelfinavir, leuprolide, valrubicin, teniposide | 50 |
6LU7 | 16 antiviral drugs | MOE | AMBER | 20 ns | MM (GB/PB) SA | Remdesivir, saquinavir, darunavir | 51 |
6LU7 | DrugBank | AutoDock | AMBER | 10 ns | MM-GBSA | Cobicistat, darunavir | 52 |
6LU7 | 10 000 approved, drugs in clinical trials and natural products | Glide, iFitDock | Disulfiram, cinanserin, tideglusib, shikonin, ebselen | 53 | |||
6LU7 | 55 antiviral, antimicrobial drugs | AutoDock | Pimozide, ebastine, bepridil | 54 | |||
6LU7 | DrugBank | AutoDock Vina | Mezlocillin, camazepam, spirapril, rolitetracycline, rescinnamine, tolvaptan, ciclesonide, raltegravir | 55 | |||
6W63 | 3981 approved drugs from FDA, EMA | DockThorAutoDock Vina | Niaprazine, tolazamide, gliclazide, glibenclamide, miconazole, econazole, sulconazole, tioconazole, efinaconazole, bedaquiline | 56 | |||
6LU7 | 1930 FDA-approved drugs and active metabolites | Glide, Fred, AutoDock Vina | Perampanel, carprofen, celecoxib, alprazolam, trovafloxacin, sarafloxacin, ethyl biscoumacetate | 57 | |||
6W63 | Natural products, protease inhibitors and FDA-approved drugs | Glide | 100 ns | MM-GBSA | Saquinavir, atazanavir, nelfinavir, lopinavir, indinavir, acarbose, octreotide, colistin | 58 | |
6LU7 | 232 drugs similar to efavirenz | AutoDock | NAMD | Simvastatin, lovastatin, oxacillin, podophyllotoxin, gefitinib | 59 | ||
6Y2F | ZINC, Spec database | Glide | GROMACS | 100 ns | MM-PBSA | Fortovase, cobicistat, cangrelor, lopromide | 60 |
6LU7 | SelleckChem, DrugBank | FlexX | Desmond | 100 ns | MM-GBSA | Birinapant, dynasore, mitoxantrone, leucovorin | 61 |
6LU7 | 23 antiviral drugs | AutoDock Vina | YASARA | 50 ns | MM-PBSA | Epirubicin, vapreotide, saquinavir | 62 |
6LU7 | 2683 SelleckChem | MOE, AutoDock Vina | Oxytetracycline, naringin, kanamycin, cefpiramide, salvianolic acid, teniposide, etoposide, doxorubicin | 63 | |||
6LU7 | 6 drugs in clinical trials | AutoDock Vina | Azithromycin, baricitinib, quinacrine, ruxolitinib | 64 | |||
6Y2F | Antiviral drugs and phytochemicals | AutoDock Vina | Simeprevir, ledipasvir, paritaprevir, glecaprevir, daclatasvir, atazanavir | 65 | |||
6LU7 | Approved drugs, Asinex BioDesign Library | Glide | Ritonavir, nelfinavir, saquinavir pralmorelin, iodixanol, iotrolan | 66 | |||
6LU7 | 33 approved protease inhibitors | AutoDock Vina | Paritaprevir, ciluprevir, simeprevir, deldeprevir, indinavir, saquinavir, faldaprevir, brecanavir, grazoprevir, lopinavir | 67 | |||
6LU7 | 1615 FDA-approved drugs from ZINC15 | AutoDock Vina | YASARA | 7 ns | Perampanel, conivaptan, sonidegib, azelastine, idelalisib, suvorexant, olaparib, ponatinib, loxapine, tolvaptan | 68 | |
6M03 | 129 antimalarial, antiparasitic, antibiotics, or antivirals and 992 ZINC Natural Products database | AutoDock Vina, AutoDock | Eprinomectin, fexofenadine, artefenomel, doramectin, betulinic acid, atovaquone, tetrandrine | 69 | |||
6YB7 | 19 antiviral and 10 natural ligands | Ritonavir | 70 | ||||
6LU7 | 4 tetracyclines | AutoDock Vina | Desmond | 100 ns | Doxycycline, tetracycline, demeclocycline, minocycline | 71 | |
6LU7 | 1930 FDA-approved drugs plus active metabolites | PLANTS | GROMACS | 100 ns | MM-PBSA | Triptorelin, nafarelin, icatibant, cobicistat, histrelin | 72 |
6LU7 | 5881 Drugs approved by FDA and anywhere in the world | AutoDock | GROMACS | 100 ns | MM-PBSA | Docetaxel, palbociclib, ergoloid, cabazitaxel, imatinib, alectinib, simeprevir, azelastine, plerixafor, dasabuvir, paclitaxel, clofazimine, rupatadine, pyronaridine, sulforidazine, thioproperazine, desmethylazelastine | 73 |
6LU7 | FDA-approved and SWEETLEAD | DOCK 6 | AMBER | 50 ns | Indinavir, ceftin, ivermectin, ceftiofur, cefazedone, amprenavir, neomycin, ceftazidime, ceftizoxime | 74 | |
6LU7 | 1 million ZINC | AutoDock Vina | GROMACS | 100 ns | Nilotinib, naldemedine, enasidenib, afatinib, ertapenem, ebselen, disulfiram, carmofur, cinanserin, shikonin, tideglusib | 75 | |
6LU7 | FDA-approved covalent binders | Covdock | GROMACS | 50 ns | Saquinavir, ritonavir, oseltamivir remdesivir, delavirdine, cefuroxime, prevacid | 76 | |
6LU7 | 124 antimicrobial drugs | Cdocker | GROMACS | 100 ns | Viomycin, bacampicillin | 77 | |
6Y2E | 124 antivirals and protease inhibitors | AutoDock Vina | Edoxaban, apixaban, betrixaban | 78 | |||
6Y2F | 8000 approved and experimental drugs | Glide | Fosamprenavir, darunavir, amprenavir, tipranavir, nicotinamide adenine dinucleotide (NAD) | 79 |
PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6M03 | DrugBank | AutoDock Vina | Glecaprevir, maraviroc | 26 | |||
6LU7 | Antivirals, statins | AutoDock Vina | Pitavastatin, rosuvastatin, fluvastatinb | 27 | |||
6LU7 | Thiol-reacting FDA-approved drugs | AutoDock Vina | Disulfiramc, captopril | 29 | |||
6LU7 | 19 in vitro tested or in trial for COVID-19 | AutoDock Vina | NAMD | 5 ns/125 ns | Nil | Entecavir, nelfinavir | 31 |
6LU7, 6M2N | Protease inhibitors | AutoDock | Telaprevir, boceprevir, lopinavir, edoxaban, ritonavir, argatroban, sitagliptin, inogatran. | 32 | |||
6LU7 | 7 HIV-1 protease inhibitors | VINA/VegaZZ | NAMD | 1 ns | Saquinavir | 33 | |
6LU7 | DrugBank | Glide | AMBER | 1 ns/100 ns | MM-PBSA-WSAS | Carfilzomib, eravacycline, valrubicin, lopinavir, elbasvir | 34 |
6Y2G | ChemBL | AutoDock Vina, RDOCK | Eszopiclone | 35 | |||
6LU7 | Antimalarial | Glide | GROMACS | 20 ns | Amodiaquine | 36 | |
5R7Y, 5R7Z, 5R80, 5R81, 5R82 | 61 antiviral drugs | Glide | Lopinavir, asunaprevir, remdesivir, indinavir, ritonavir, galidesivir | 37 | |||
6Y2G | 61 antiviral drugs | AutoDock | Artemisinin, ritonavir, darunavir, lopinavir, vitamin D, vitamin E | 38 | |||
6Y2F | 22 flavonoids, antimalarials, vitamins | AutoDock Vina | YASARA | 10 ns | Lopinavir–ritonavir, tipranavir, raltegravir | 39 | |
6M03, 6LU7, 6Y2F/6Y2G | 75 antiviral, anticancer, antimalarial | Glide | Desmond | 200 ns | MM-GBSA | Leupeptin, pepstatin A, nelfinavir, birinapant, lypressin, octreotide | 40 |
6Y84 | SelleckChem, DrugBank, Repurposing hub | SWISS-DOCK | Emetine, hesperidin, indinavir, itraconazole | 41 | |||
6LU7 | 33 antinematodes antiprotozoals, antifungals, natural products, antivirals | GOLD | YASARA | 100 ns | MM-PBSA | Simeprevir, ergotamine, bromocriptine, tadalafil | 42 |
HM | 1615 FDA-approved drugs from ZINC | iDock | GROMACS | 100 ns | Daunorubicin, ergotamine, ergoloid, bromocriptine, meclocycline, amrubicin, | 43 | |
6W63 | SuperDRUG2 | Glide | MM-GBSA | Binifibrate, macimorelin, bamifylline, rilmazafone, afatinib, ezetimibe | 44 | ||
6LU7 | OTAVA Chemicals (1017 compounds), Machine Learning SARS Targeted Library (1577 compounds) | GOLD | Apixaban | 45 | |||
6LU7 | 606 million ZINC | AutoDock Vina, AutoDock, Glide | Desmond | 20 ns | MM-GBSA | Apixaban, nelfinavir, glecaprevir, rivaroxaban, betrixaban, amprenavir | 46 |
6LU7 | SelleckChem | Glide | Ribavirin, telbivudine, aminosalicylate, pyrazinamide, vitamin B12, nicotinamide, temozolomide | 47 | |||
HM | 7173 DrugsLib | MTiOpenScreen, AutoDock Vina | Velpatasvir, ledipasvir | 48 | |||
6LU7 | 31 anti-HIV drugs | Glide | AMBER | 100 ns | MM-GBSA | Saquinavir | 49 |
6W63 | DrugBank | Glide | AMBER | 100 ns | MM-PBSA | Ritonavir, nelfinavir, leuprolide, valrubicin, teniposide | 50 |
6LU7 | 16 antiviral drugs | MOE | AMBER | 20 ns | MM (GB/PB) SA | Remdesivir, saquinavir, darunavir | 51 |
6LU7 | DrugBank | AutoDock | AMBER | 10 ns | MM-GBSA | Cobicistat, darunavir | 52 |
6LU7 | 10 000 approved, drugs in clinical trials and natural products | Glide, iFitDock | Disulfiram, cinanserin, tideglusib, shikonin, ebselen | 53 | |||
6LU7 | 55 antiviral, antimicrobial drugs | AutoDock | Pimozide, ebastine, bepridil | 54 | |||
6LU7 | DrugBank | AutoDock Vina | Mezlocillin, camazepam, spirapril, rolitetracycline, rescinnamine, tolvaptan, ciclesonide, raltegravir | 55 | |||
6W63 | 3981 approved drugs from FDA, EMA | DockThorAutoDock Vina | Niaprazine, tolazamide, gliclazide, glibenclamide, miconazole, econazole, sulconazole, tioconazole, efinaconazole, bedaquiline | 56 | |||
6LU7 | 1930 FDA-approved drugs and active metabolites | Glide, Fred, AutoDock Vina | Perampanel, carprofen, celecoxib, alprazolam, trovafloxacin, sarafloxacin, ethyl biscoumacetate | 57 | |||
6W63 | Natural products, protease inhibitors and FDA-approved drugs | Glide | 100 ns | MM-GBSA | Saquinavir, atazanavir, nelfinavir, lopinavir, indinavir, acarbose, octreotide, colistin | 58 | |
6LU7 | 232 drugs similar to efavirenz | AutoDock | NAMD | Simvastatin, lovastatin, oxacillin, podophyllotoxin, gefitinib | 59 | ||
6Y2F | ZINC, Spec database | Glide | GROMACS | 100 ns | MM-PBSA | Fortovase, cobicistat, cangrelor, lopromide | 60 |
6LU7 | SelleckChem, DrugBank | FlexX | Desmond | 100 ns | MM-GBSA | Birinapant, dynasore, mitoxantrone, leucovorin | 61 |
6LU7 | 23 antiviral drugs | AutoDock Vina | YASARA | 50 ns | MM-PBSA | Epirubicin, vapreotide, saquinavir | 62 |
6LU7 | 2683 SelleckChem | MOE, AutoDock Vina | Oxytetracycline, naringin, kanamycin, cefpiramide, salvianolic acid, teniposide, etoposide, doxorubicin | 63 | |||
6LU7 | 6 drugs in clinical trials | AutoDock Vina | Azithromycin, baricitinib, quinacrine, ruxolitinib | 64 | |||
6Y2F | Antiviral drugs and phytochemicals | AutoDock Vina | Simeprevir, ledipasvir, paritaprevir, glecaprevir, daclatasvir, atazanavir | 65 | |||
6LU7 | Approved drugs, Asinex BioDesign Library | Glide | Ritonavir, nelfinavir, saquinavir pralmorelin, iodixanol, iotrolan | 66 | |||
6LU7 | 33 approved protease inhibitors | AutoDock Vina | Paritaprevir, ciluprevir, simeprevir, deldeprevir, indinavir, saquinavir, faldaprevir, brecanavir, grazoprevir, lopinavir | 67 | |||
6LU7 | 1615 FDA-approved drugs from ZINC15 | AutoDock Vina | YASARA | 7 ns | Perampanel, conivaptan, sonidegib, azelastine, idelalisib, suvorexant, olaparib, ponatinib, loxapine, tolvaptan | 68 | |
6M03 | 129 antimalarial, antiparasitic, antibiotics, or antivirals and 992 ZINC Natural Products database | AutoDock Vina, AutoDock | Eprinomectin, fexofenadine, artefenomel, doramectin, betulinic acid, atovaquone, tetrandrine | 69 | |||
6YB7 | 19 antiviral and 10 natural ligands | Ritonavir | 70 | ||||
6LU7 | 4 tetracyclines | AutoDock Vina | Desmond | 100 ns | Doxycycline, tetracycline, demeclocycline, minocycline | 71 | |
6LU7 | 1930 FDA-approved drugs plus active metabolites | PLANTS | GROMACS | 100 ns | MM-PBSA | Triptorelin, nafarelin, icatibant, cobicistat, histrelin | 72 |
6LU7 | 5881 Drugs approved by FDA and anywhere in the world | AutoDock | GROMACS | 100 ns | MM-PBSA | Docetaxel, palbociclib, ergoloid, cabazitaxel, imatinib, alectinib, simeprevir, azelastine, plerixafor, dasabuvir, paclitaxel, clofazimine, rupatadine, pyronaridine, sulforidazine, thioproperazine, desmethylazelastine | 73 |
6LU7 | FDA-approved and SWEETLEAD | DOCK 6 | AMBER | 50 ns | Indinavir, ceftin, ivermectin, ceftiofur, cefazedone, amprenavir, neomycin, ceftazidime, ceftizoxime | 74 | |
6LU7 | 1 million ZINC | AutoDock Vina | GROMACS | 100 ns | Nilotinib, naldemedine, enasidenib, afatinib, ertapenem, ebselen, disulfiram, carmofur, cinanserin, shikonin, tideglusib | 75 | |
6LU7 | FDA-approved covalent binders | Covdock | GROMACS | 50 ns | Saquinavir, ritonavir, oseltamivir remdesivir, delavirdine, cefuroxime, prevacid | 76 | |
6LU7 | 124 antimicrobial drugs | Cdocker | GROMACS | 100 ns | Viomycin, bacampicillin | 77 | |
6Y2E | 124 antivirals and protease inhibitors | AutoDock Vina | Edoxaban, apixaban, betrixaban | 78 | |||
6Y2F | 8000 approved and experimental drugs | Glide | Fosamprenavir, darunavir, amprenavir, tipranavir, nicotinamide adenine dinucleotide (NAD) | 79 |
1.3.1.1 Crystal Structures Used in Docking
At present there are 172 structures available in the PDB for Mpro and hence there is significant choice available for the researchers to use in the docking experiments. Overall, only 14 PDB structures (8% of those available) have been utilized by various research groups. However, from Table 1.5 it can be inferred that most of the research groups (∼62%) have used structure PDB ID 6LU7 (resolution of 2.16 Å) for docking and/or MD studies. This may be due to the availability of this structure in early February 2020 and that it is a co-crystallized structure with a covalent inhibitor N3 bound to CYS145. It is interesting to note that although another Mpro structure, PDB ID 7BQY complexed with N3 at better resolution (1.7 Å) was available, no in silico study utilized this structure, probably due to its later release (22 April 2020) in the Research Collaboratory for Structural Bioinformatics (RCSB) database. The other structures used in four studies each were PDB ID 6W63 (co-crystallized structure with non-covalent inhibitor X77, resolution 2.1 Å) and PDB ID 6Y2F (co-crystallized structure with α-ketoamide inhibitor, resolution 1.95 Å). The PDB structures 6M03 and 6Y2G were used in two studies each and the remainder were used in one study each. Only three studies have used multiple PDB structures for docking to understand the effect of receptor flexibility,32,37,40 while some studies have used multiple structures obtained from MD simulation of the apo Mpro structure.
Two studies have reported homology models for Mpro using different template structures.43,48 In a study by Jiménez-Alberto et al.,43 the Modeller program and several templates were used to generate various homology models and the best model was selected on the basis of Discrete Optimized Protein Energy score. The local structural quality was estimated using the QMEAN tool available in the SWISS-MODEL server. Since the crystal structures of Mpro became available soon after the model was developed, it was compared with 6LU7, whose b-factors agree with the developed model; therefore, the homology model constructed in this work seems to be valid. In contrast, Chen et al.48 performed homology modelling of the apo-enzyme structure of SARS-CoV-2 using SARS-CoV 3CLpro (PDB ID: 2DUC) as the template structure. The model post-energy minization was compared with the obtained crystal structure (PDB ID: 6LU7), having average root mean square deviation (RMSD) of Cα atomic positions (residues 1–300) is 1.2 Å for the A chain and 0.8 Å for the B chain. These RMSD values are acceptable for further structure-based studies using this model.
1.3.1.2 Methods or Programs Used in Docking
The docking programs or methods used for screening of drug/ligand databases in Mpro structure are provided in Table 1.6.
Docking methods or programs used for screening drugs/ligands in Mpro
Docking method/program . | Available web link . |
---|---|
AutoDock Vina | http://vina.scripps.edu/ |
Glide | https://www.schrodinger.com/glide |
AutoDock | http://autodock.scripps.edu/ |
GOLD | https://www.ccdc.cam.ac.uk/solutions/csd-discovery/components/gold/ |
Cdocker | https://www.3ds.com/ |
Covdock | https://www.schrodinger.com/covdock |
DOCK 6 | http://dock.compbio.ucsf.edu/DOCK_6/ |
FlexX | https://www.biosolveit.de/FlexX/ |
iDock | https://github.com/HongjianLi/idock |
MOE | https://www.chemcomp.com/ |
PLANTS | www.tcd.uni-konstanz.de |
SWISS-DOCK | http://www.swissdock.ch/ |
VegaZZ | www.ddl.unimi.it |
Docking method/program . | Available web link . |
---|---|
AutoDock Vina | http://vina.scripps.edu/ |
Glide | https://www.schrodinger.com/glide |
AutoDock | http://autodock.scripps.edu/ |
GOLD | https://www.ccdc.cam.ac.uk/solutions/csd-discovery/components/gold/ |
Cdocker | https://www.3ds.com/ |
Covdock | https://www.schrodinger.com/covdock |
DOCK 6 | http://dock.compbio.ucsf.edu/DOCK_6/ |
FlexX | https://www.biosolveit.de/FlexX/ |
iDock | https://github.com/HongjianLi/idock |
MOE | https://www.chemcomp.com/ |
PLANTS | www.tcd.uni-konstanz.de |
SWISS-DOCK | http://www.swissdock.ch/ |
VegaZZ | www.ddl.unimi.it |
The two most used docking software programs were AutoDock Vina and Glide, which were reported in 27% and 23% of the studies on Mpro. Eight studies (15%) reported results from multiple docking programs, whereas the AutoDock program was reported by 12% of studies. There is no single method that has shown excellent retrieval of repurposing opportunities when hits are analyzed based on available experimental results.
1.3.1.3 Ligands and Databases Used for Screening
Half of the reported studies used large drug database for screening of approved drugs, e.g. DrugBank, SelleckChem, ZINC, etc. The other studies screened smaller sets of drugs classified by different categories such as antivirals (23% of studies), antimalarials (6% of studies) and protease inhibitors (10% of studies).
1.3.1.4 Molecular Dynamics Programs Used
Only 45% of studies have used MD for Mpro apo structure or for understanding the stability of drug–receptor complexes. In these studies, the most used MD programs were GROMACS (http://www.gromacs.org/, 32% of studies) and AMBER (https://ambermd.org/, 24% of studies) followed by Desmond (https://www.schrodinger.com/desmond) and YASARA (http://www.yasara.org/), each reported in 16% of studies. The NAMD program (http://www.ks.uiuc.edu/Research/namd/) has been reported in three studies (12%).
1.3.1.5 Duration of MD Simulation
The MD simulation was performed for various time durations from 1 ns to 200 ns. Most studies (52%) reported a duration of 100 ns for MD simulation and 72% of studies reported a simulation time >50 ns. Thus, most studies have evaluated stability of receptors and drug–receptor complexes after evolving the system for a significant simulation time.
1.3.1.6 Free-energy Estimation Method Used
The free energies for the drug–receptor complexes were estimated in 60% of studies that performed MD simulations. The studies have used either molecular mechanics (MM)-generalized Born surface area (GBSA) or MM-Poisson–Boltzmann surface area (PBSA) methods for estimation of free energy. The free-energy estimates were made from the last few conformations of the simulation or from conformations from the last few nanoseconds of the simulation.
1.3.1.7 Analysis of Hit Drugs from Docking
From Table 1.5 it can be observed that several antivirals have appeared as hits in different studies: ritonavir (eight studies), saquinavir (seven studies), lopinavir (seven studies), nelfinavir (six studies) and simeprevir (four studies). The other drugs that are reported as hits in multiple studies are disulfiram, apixaban and cobicistat (three studies each). Furthermore, there are 11 studies which have reported screening in PDB ID 6LU7 using the AutoDock Vina program. Overall, 63 drugs have been identified as hits from these studies. Out of these studies, only disulfiram, nelfinavir, saquinavir and tolvaptan appear as promising in two studies. Thus, not many common hits were found in the studies using the combination of 6LU7 and AutoDock Vina. Furthermore, there are six studies that report hits from screening in PDB ID 6LU7 using the Glide program. From these studies, only saquinavir appears as a hit in two studies. Conversely, with docking by Glide in PDB ID 6W63, nelfinavir appears as a hit in two studies. The possible reason for not observing common hits could be due to the different databases screened in these studies as well as some differences in the preparation of proteins and ligands, grid used, centre and other parameters chosen.
1.3.1.8 Some Noteworthy Studies on Mpro
This section lists some selected studies that have reported:
correlation with experimental studies noteworthy for some drugs;
use of additional methods for screening of drug-like shape or pharmacophore-based methods;
novel methods used for screening;
important observations from the authors.
1.3.1.8.1 Study Reporting Good Correlation of Predicted Binding Affinity with Experiments
Huynh et al. performed MD simulation of apo and ligand-bound Mpro structures31 for preparing targets for further studies. Docking of 19 drugs that were tested in vitro or in clinics for COVID-19 were carried out in target structures. They concluded that a drug molecule can be more potent with a hydrophobic bulky group occupying the “anchor” site in Mpro. Such insights provided by modelling studies are useful for experimentalists, especially those intending to design new drugs against Mpro. We found that five of these drugs were tested in a recombinant assay of Mpro and showed good correlation of Mpro IC50 with binding affinity from docking (r = 0.72). We also observed that six drugs for which cellular in vitro SARS-CoV-2 (EC50)/IC50 values are available show excellent correlation with the binding affinity of drugs calculated in this paper (r = 0.92).
1.3.1.8.2 Studies Using Pharmacophore or Shape-based Methods for Screening of Drugs
Arun et al. generated an energy-optimized pharmacophore hypothesis using a non-covalent inhibitor X77-bound crystal structure of Mpro of SARS-CoV-2 (PDB ID: 6W63).44 This hypothesis was used for virtual screening followed by screening of selected drugs using the Glide docking program in stepwise manner leading to a shortlist of six drugs. The stability of binding of the selected drugs complexed with Mpro was confirmed by MD simulation to show that binifibrate and bamifylline may bind to the SARS-CoV-2 Mpro active site and inhibit its activity. Kanhed et al.66 also reported use of a pharmacophore model for screening of drugs. This was followed by docking carried out at various precision levels that led to three promising drugs – ritonavir, nelfinavir and saquinavir – that are proposed to be SARS-CoV-2 Mpro inhibitors. Apart from these, pralmorelin, iodixanol and iotrolan were also identified from the systematic screening. The experimental inhibitory assay for Mpro of SARS-CoV-2 showed that nelfinavir and saquinavir have IC50 values of 234 µM and 411 µM, respectively. The corresponding cellular inhibitory activity in SARS-CoV-2 infection showed EC50 values of 1.13 µM and 8.83 µM for nelfinavir and saquinavir, respectively. This clearly indicates that Mpro may not be the primary target for these drugs.
Few studies have utilized ligand shape-based methods for virtual screening of drug databases. Fischer et al. reported use of shape-based screening and ensemble-based docking of a large database of compounds.46 The top-ranked docking poses were subjected to MD simulation, and free-energy estimates using the MM-GBSA method identified apixaban, and the two known antivirals nelfinavir and glecaprevir, as well as rivaroxaban, betrixaban and amprenavir as promising hits. The inhibitory activity against SARS-CoV-2 of some of these drugs are known, e.g. apixaban (5.91 µM), nelfinavir (1.13 µM) and amprenavir (31.32 µM). Ferraz et al. reported use of multiple methods to arrive at drug repurposing.56 They performed a 3D shape-based (using the ROCS program) and electrostatic profile based (using the EON program) similarity search of SARS-CoV-2 Mpro using the X77 ligand (PDB ID: 6W63) as the query ligand. In addition, they generated a structure-based pharmacophore model for drug screening followed by docking to propose glibenclamide, bedaquiline and miconazole as promising hits likely to inhibit SARS-CoV-2 Mpro.
1.3.1.8.3 Novel Methods of Screening
Sencanski et al. reported a novel two-step approach to identify drug repurposing opportunities in Mpro of SARS-CoV-2.55 In the first step, the Informational Spectrum Method Applied for Small Molecules (ISM-SM) was used for searching the DrugBank database to identify drugs having potential interactions with Mpro. In the second step, molecular docking was used as an additional filter to propose repurposed drugs. Using the ISM-SM method, one can determine whether a protein interacts with small molecules and the corresponding binding region in the protein. Regions of allosteric binding are also identified using this method. The promising candidates binding to catalytic sites were mezlocillin, camazepam and spirapril, whereas raltegravir, rolitetracycline, tolvaptan, ciclesonide and rescinnamine were found to target the allosteric domain. Jordaan et al. reported use of density-functional parameters such as chemical potential and hardness in addition to standard virtual screening methods of docking and MD simulation.59 The compounds proposed for drug repurposing from this method include simvastatin, lovastatin, oxacillin, podophyllotoxin and gefitinib.59 Such novel approaches can lead to different list of repurposing candidates missed by conventional methods.
1.3.1.8.4 Studies in which Important Observations are Reported
Jin et al. reported identification of lead compounds by combining structure-assisted drug design, virtual drug screening and experimental high-throughput screening.53 The docking studies show that cinanserin fits in the substrate binding pocket of Mpro and has demonstrated activity at 125 µM in Mpro assay. Molecular docking could predict how disulfiram, tideglusib and shikonin bind to Mpro in the substrate-binding pocket. The prediction was confirmed from fluorescence resonance energy transfer assay for disulfiram with EC50 9.35 µM. Gimeno et al. performed virtual screening for Mpro by three different docking programs to identify high-affinity drugs.57 The drugs that have equivalent high-affinity docked poses in all docking schemes were considered as hits. However, this study reported that the docking protocol could not reproduce the experimental results of 26 Mpro inhibitors. The authors considered that the most reliable part of protein–ligand docking algorithms is their capacity to explore the hypothetical binding modes of a ligand at the binding site, and hence proposed visually inspection of the matching of the poses without considering their docking scores. Two of the suggested drugs (celecoxib and carprofen) were tested for Mpro inhibition, but have poor inhibitory activity at 50 µM. Thus, the consensus docking was not found to be an effective tool for virtual screening.
1.3.2 Docking Studies in RNA-dependent RNA Polymerase
Few structure-based studies are reported for SARS-CoV-2 RNA-dependent RNA polymerase (RdRp), although X-ray crystal structures for this target are available. A summary of the studies conducted so far is given in Table 1.7.
Summary of RNA-dependent RNA polymerase studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
HM (6NUR) | Antivirals | AutoDock Vina | Galidesivir, remdesivir, tenofovir, sofosbuvir, ribavirinc | 80 | |||
HM (6NUR) | 30 known drugs | AutoDock Vina | NAMD | 51 ns | Sofosbuvir, ribavirin, galidesivir, remdesivir, favipiravir, cefuroxime, tenofovir, hydroxychloroquine | 82 | |
HM (6NUR) + 6M71 | FDA-approved drugs from e-Drugs3d | AutoDock Vina | NAMD | 100 ns | Quinupristin, dactinomycin, sirolimus, cetrorelix, rifampin | 83 | |
6M71 | 7922 FDA-approved drugs database from NCGC | Glide | RdRp alone: ornipressin, atosiban, lanreotide, argiprestocin, demoxytocin, carbetocin, lypressin, examorelin, colistin, polymyxin B1. RdRp cofactor complex: nacartocin, cistinexine, cisatracurium, pegamotecan, polymyxin B1, ebiratide, sulfomyxine, diagastrin, ditercalinium chloride, benzquercin, examorelin, lypressin, ornipressin, colistin | 84 | |||
HM (6NUR) | FDA-approved drugs database from SelleckChem | Glide | Desmond | 50 ns | MM-GBSA | Methylcobalamin | 85 |
7BW4 6NUR | ZINC: FDA-approved, world-not-FDA and investigational-only | AutoDock Vina | AMBER | 5 ns | MM-GBSA | NSP12-NSP7 interface: nilotinib, dihydroergotamine, pranlukast, cepharanthine, nafamostat. NSP12-NSP8 interface: cepharanthine, dutasteride, simeprevir, grazoprevir, saquinavir | 86 |
HM (6NUR) | 10 antivirals | MOE | Remdesivir, sofosbuvir, galidesivir | 87 | |||
6M71 | 30 drugs | Molegro | Chlorhexidine, remdesivir, novobiocin, ceftibuten, ribavirin | 88 |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
HM (6NUR) | Antivirals | AutoDock Vina | Galidesivir, remdesivir, tenofovir, sofosbuvir, ribavirinc | 80 | |||
HM (6NUR) | 30 known drugs | AutoDock Vina | NAMD | 51 ns | Sofosbuvir, ribavirin, galidesivir, remdesivir, favipiravir, cefuroxime, tenofovir, hydroxychloroquine | 82 | |
HM (6NUR) + 6M71 | FDA-approved drugs from e-Drugs3d | AutoDock Vina | NAMD | 100 ns | Quinupristin, dactinomycin, sirolimus, cetrorelix, rifampin | 83 | |
6M71 | 7922 FDA-approved drugs database from NCGC | Glide | RdRp alone: ornipressin, atosiban, lanreotide, argiprestocin, demoxytocin, carbetocin, lypressin, examorelin, colistin, polymyxin B1. RdRp cofactor complex: nacartocin, cistinexine, cisatracurium, pegamotecan, polymyxin B1, ebiratide, sulfomyxine, diagastrin, ditercalinium chloride, benzquercin, examorelin, lypressin, ornipressin, colistin | 84 | |||
HM (6NUR) | FDA-approved drugs database from SelleckChem | Glide | Desmond | 50 ns | MM-GBSA | Methylcobalamin | 85 |
7BW4 6NUR | ZINC: FDA-approved, world-not-FDA and investigational-only | AutoDock Vina | AMBER | 5 ns | MM-GBSA | NSP12-NSP7 interface: nilotinib, dihydroergotamine, pranlukast, cepharanthine, nafamostat. NSP12-NSP8 interface: cepharanthine, dutasteride, simeprevir, grazoprevir, saquinavir | 86 |
HM (6NUR) | 10 antivirals | MOE | Remdesivir, sofosbuvir, galidesivir | 87 | |||
6M71 | 30 drugs | Molegro | Chlorhexidine, remdesivir, novobiocin, ceftibuten, ribavirin | 88 |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
Remdesivir, tenofovir, sofosbuvir and ribavirin have been tested against SARS-CoV-2.81
Most of the studies have developed homology models for RdRp using a template structure of SARS-CoV RdRp. In two studies, some key residues were kept as flexible to accommodate changes in the active site.80,83 One study reported virtual screening of drugs in the NSP12 proteins of both SARS-CoV and SARS-CoV-2.86 The published crystal structures for SARS‐CoV‐2 NSP12 (PDB ID: 7BW4) and SARS‐CoV NSP12 (PDB ID: 6NUR) were used as the target proteins. This study showed that binding olysio and cepharanthine were promising in SARS-CoV interfaces, indicating that these drugs could be broad-spectrum antivirals. Among the suggested promising drugs, cepharanthine has been tested for inhibition of SARS-CoV-2 infection. One study uses binding-energy comparison with native nucleotides as a criterion for shortlisting the drugs.80 As can be seen in Table 1.7, four different studies reported remdesivir as a promising hit for RdRp, whereas sofosbuvir appears as a hit in three studies and favipiravir in one study. These drugs are known RdRp inhibitors and have been reported to inhibit SARS-CoV-2 in cellular assays.
1.3.3 Docking Studies in Papain-like Protease
Although this protease is an important drug target, very few in silico and experimental studies on drug repurposing using this target are reported in the literature. A recent experimental study demonstrated that none of the FDA-approved drugs show promising binding to SARS-CoV-2 papain-like protease (PLpro).89 Two studies have reported in silico screening in this target.90,91 A summary of the studies conducted so far is given in Table 1.8.
Summary of papain-like protease protein studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6W9C | SelleckChem | Glide | AMBER | 50 ns | MM-GBSA | Ritonavir, quercetin, phenformin | 90 |
HM (4MM3) | 5 protease inhibitors | AutoDock | Nelfinavir, lopinavir, ritonavir, remdesivir, ketoamide | 91 |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6W9C | SelleckChem | Glide | AMBER | 50 ns | MM-GBSA | Ritonavir, quercetin, phenformin | 90 |
HM (4MM3) | 5 protease inhibitors | AutoDock | Nelfinavir, lopinavir, ritonavir, remdesivir, ketoamide | 91 |
Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
Kandeel et al.89 reported virtual screening of FDA-approved drugs where ritonavir, quercetin and phenformin showed promising binding affinities toward the PLpro target of SARS-CoV-2. As seen earlier, none of the FDA-approved drugs bind effectively the to SARS-CoV-2 PLpro target, hence these predicted drugs may be false positives in the method used for prediction, or the method is inadequate for the purpose. Similarly, from study by Mothay and Ramesh,91 nelfinavir was found to be most active, based on docking score, which may also be a false positive.
1.3.4 Docking Studies in the Nucleocapsid Protein (N-protein)
Two structure-based drug repurposing studies have reported on the SARS-CoV-2 N-protein92,93 using X-ray crystal structure (PDB ID: 6VYO). A summary of the studies conducted so far is given in Table 1.9.
Summary of nucleocapsid protein studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6VYO | Chloroquine, hydroxychloroquine | AutoDock | Chloroquine, hydroxychloroquine | 92 | |||
6VYO | Antivirals, FDA-approved anti-infectives | Glide | 50 ns | MM-GBSA | Zidovudine, valganciclovir, ribavirin | 93 |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6VYO | Chloroquine, hydroxychloroquine | AutoDock | Chloroquine, hydroxychloroquine | 92 | |||
6VYO | Antivirals, FDA-approved anti-infectives | Glide | 50 ns | MM-GBSA | Zidovudine, valganciclovir, ribavirin | 93 |
Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
Amin and Abbas reported docking studies92 of chloroquine and hydroxychloroquine in the SARS-CoV-2 NTD of N-protein and the trend of their binding energies matches with the EC50 of cellular SARS-CoV-2. Yadav et al.93 explored three sites in N-protein for docking. The binding free energies by the MM-GBSA method showed drugs such as zidovudine, valganciclovir and ribavirin with promising binding to all three sites in the N-protein. These drugs have not yet been tested for inhibition of N-protein, but ribavirin has been tested in cellular SARS-CoV-2 infection with an EC50 value of 109.5 µM.
1.3.5 Docking Studies in the Spike Glycoprotein (S-protein)
Few structure-based studies are reported for SARS-CoV-2 S-protein, although X-ray crystal structures for this target are available. A summary of these studies is given in Table 1.10.
Summary of spike protein studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6VSB | Ligands, approved drugs from SWEETLEAD database | AutoDock Vina | GROMACS | 18 ns | MM-PBSA | Quinupristin, nilotinib, tirilazad, selamectin, doramectin | 94 |
6VSB | Nelfinavir | AutoDock | Nelfinavir | 95 | |||
6LZG | DrugBank | AutoDock Vina | AMBER | 100 ns | MM-PBSA | Digitoxin, nilotinib, lemborexant, raltegravir, antrafenine, flunitrazepam, entrectinib, pazopanib, loxapine | 96 |
HM (6ACD) | 4015 drugs from DrugCentral database | Glide | GROMACS | 100 ns | Streptomycin, ciprofloxacin, glycyrrhizic acid | 97 | |
HM (2AJF) | FDA-approved drugs from CureFFI data, DrugCentral and BindingDB | AutoDock Vina | Pemirolast, sulfamethoxazole, valaciclovir, sulfanilamide, tazobactam, nitrofurantoin | 98 |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6VSB | Ligands, approved drugs from SWEETLEAD database | AutoDock Vina | GROMACS | 18 ns | MM-PBSA | Quinupristin, nilotinib, tirilazad, selamectin, doramectin | 94 |
6VSB | Nelfinavir | AutoDock | Nelfinavir | 95 | |||
6LZG | DrugBank | AutoDock Vina | AMBER | 100 ns | MM-PBSA | Digitoxin, nilotinib, lemborexant, raltegravir, antrafenine, flunitrazepam, entrectinib, pazopanib, loxapine | 96 |
HM (6ACD) | 4015 drugs from DrugCentral database | Glide | GROMACS | 100 ns | Streptomycin, ciprofloxacin, glycyrrhizic acid | 97 | |
HM (2AJF) | FDA-approved drugs from CureFFI data, DrugCentral and BindingDB | AutoDock Vina | Pemirolast, sulfamethoxazole, valaciclovir, sulfanilamide, tazobactam, nitrofurantoin | 98 |
Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
None of the drugs proposed as spike glycoprotein inhibitors in the studies listed in Table 1.10 have been validated experimentally against the target. The docking of the nelfinavir mesylate to the spike protein of SARS-CoV‐2 showed that nelfinavir stabilizes near the helices of the HR1 region.95 The pocket between the helices of the fusion peptide and HR1 region as well as the lower part of the NTD region were preferred for binding by nelfinavir over the S1/S2 cleavage site. Batra et al. investigated the spike protein and the spike–ACE-2 interface using machine learning and docking methods.98 The machine learning model uses the Vina score derived from intermolecular contributions of the free energy of binding which ranks molecular conformations. Vina scores of a molecule with the S-protein and S-protein:ACE-2 complex were used for training the models. Based on the chosen screening criteria, 187 ligands were selected from the three datasets, of which 80 were approved drugs. Ensemble docking studies on the selected 187 ligands were performed. Based on this approach, the top approved drugs were proposed for repurposing (Table 1.10). None of these drugs have been tested in an assay of spike protein or in a cellular assay of SARS-CoV-2 infection.
1.3.6 Docking Studies in NSP1
Only two in silico studies reported virtual screening in NSP1.99,100 Although X-ray crystal structures for this target (NSP1) are available, the drug repurposing efforts using structure-based methods reported screening in the homology models of this protein.
Sharma et al.99 developed a homology model of the SARS-CoV-2 NSP1 using structure of NSP1 of SARS-CoV (PDB ID: 2HSX) as the template which covers 68% query protein with 86% identity. Menezes and Silva100 used different template (PDB ID: 2GDT). They performed blind docking of two cyclophilin inhibitors, viz. alisporivir and cyclosporine (known CoV suppressors) as reference standards for further screening in the DrugBank database. The use of reference standards provides the basis for the selection of hits. From the hits of a virtual screening exercise (Table 1.11) none of the drugs have been tested experimentally for inhibition of NSP1.
Summary of NSP1 studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
HM (2HSX) | DrugBank | Glide | Desmond | 20 ns | MM-GBSA | Esculin, cidofovir, lactose, edoxudine, brivudine, remdesivir | 99 |
HM (2GDT) | DrugBank | AutoDock Vina | GROMACS | 150 ns | MM-GBSA | Tirilazad, phthalocyanine, Zk-806450 | 100 |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
HM (2HSX) | DrugBank | Glide | Desmond | 20 ns | MM-GBSA | Esculin, cidofovir, lactose, edoxudine, brivudine, remdesivir | 99 |
HM (2GDT) | DrugBank | AutoDock Vina | GROMACS | 150 ns | MM-GBSA | Tirilazad, phthalocyanine, Zk-806450 | 100 |
Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
1.3.7 Docking Studies in NSP13/Helicase
Despite having two crystal structures in protein databank, Borgio et al. screened approved drugs in the homology model structure of NSP13/helicase of SARS-CoV-2.101 The SARS-CoV-2 helicase was modelled using template structures of helicase of SARS-CoV (PDB Id:6JYT) and Middle East respiratory syndrome-CoV (PDB ID:5WWP) using the SWISS-MODEL server. The 23 approved antiviral drugs were subjected to flexible molecular docking of drugs in helicase using Molecular Operating Environment (MOE) software. The docking studies showed that vapreotide and atazanavir were the most potent inhibitors of helicase of SARS-CoV-2. None of the drugs have been studied in the assay of helicase of SARS-CoV-2.
1.3.8 Docking Studies in NSP15/Endonucleases
The two studies involving drug repurposing using structure-based methods used crystal structures for screening in this target.102,103 Krishnan et al.102 screened a database of 3978 antiviral molecules including approved drugs from the Enamine database (https://enamine.net/). None of the promising hits from this study were approved drugs. Chandra et al.103 used a bound citrate molecule as a reference standard and performed molecular docking to show that idarubicin and glisoxepide were promising hits. Both the drugs have not yet been tested experimentally by any group for inhibition of NSP15 or cellular assay of SARS-CoV-2 infection (Table 1.12).
Summary of NSP15 studies
HMa(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6VWW | 3978 antivirals from Enamine database | Glide | No approved drugs as hits | 102 | |||
6W01 | FDA-approved drugs from ZINC12 | AutoDock Vina, iDock, Smina | GROMACS | 100 ns | MM-PBSA | Dihydroergotamine, glisoxepide, idarubicin, ergotamine, tasosartan | 103 |
HMa(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6VWW | 3978 antivirals from Enamine database | Glide | No approved drugs as hits | 102 | |||
6W01 | FDA-approved drugs from ZINC12 | AutoDock Vina, iDock, Smina | GROMACS | 100 ns | MM-PBSA | Dihydroergotamine, glisoxepide, idarubicin, ergotamine, tasosartan | 103 |
Homology model using template with PDB ID.
1.3.9 Docking Studies in NSP16
Drug repurposing efforts using structure-based methods reported screening in the X-ray crystal structures as well as a homology model for this target (NSP16). A summary of these studies is given in Table 1.13.
Summary of NSP16 studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6W4H | Swiss Similarity, DrugBank | AutoDock Vina, AutoDock, SWISS-DOCK | GROMACS | 60 ns | Raltegravir, maraviroc, favipiravir, prednisolone | 104 | |
6W4H | FDA drugs from ZINC, DrugBank, Specs database | Glide | AMBER | 200 ns | MM-PBSA | Fludarabine, saquinavir, cangrelor | 105 |
HM (3R24, 2XYR, 5YN5) | FDA drugs from ZINC, DrugBank | AutoDock Vina, AutoDock | GROMACS | 50 ns | MM-GBSA | Sinefungin, dihydroergotamine, digitoxin, irinotecan, teniposide | 106 |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
6W4H | Swiss Similarity, DrugBank | AutoDock Vina, AutoDock, SWISS-DOCK | GROMACS | 60 ns | Raltegravir, maraviroc, favipiravir, prednisolone | 104 | |
6W4H | FDA drugs from ZINC, DrugBank, Specs database | Glide | AMBER | 200 ns | MM-PBSA | Fludarabine, saquinavir, cangrelor | 105 |
HM (3R24, 2XYR, 5YN5) | FDA drugs from ZINC, DrugBank | AutoDock Vina, AutoDock | GROMACS | 50 ns | MM-GBSA | Sinefungin, dihydroergotamine, digitoxin, irinotecan, teniposide | 106 |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
Tazikeh-Lemeski et al.104 used shape-based screening of FDA-approved drugs with a known template of the MTase inhibitor, sinefungin. The selected drugs were further investigated using multiple docking methods followed by MD simulations. The results showed that raltegravir and maraviroc among other compounds can bind strongly to the active site of the protein compared to sinefungin (reference drug) and can be potential candidates to inhibit NSP16. Vijayan et al.105 reported fludarabine, saquinavir and cangrelor as promising drugs. However, these drugs showed poor binding to NSP16 compared to the native ligand (S-adenosyl methionine). This indicates that none of the drugs can act as inhibitors of NSP16. Sharma et al.106 showed that complexes of digitoxin, dihydroergotamine, irinotecan, and teniposide were stable and exhibit more affinity to the receptor compared to the reference ligand, i.e. sinefungin, with IC50 of 286 nM for NSP16 of SARS-CoV-2. The IC50 for the proposed drugs have not been reported for this target in the literature. However, an EC50 for digitoxin of 0.16/0.23 µM has been reported for SARS-CoV-2 infection in cells.
1.3.10 Docking in Main Protease and Spike Glycoprotein
This section reviews four in silico drug repurposing studies involving both main protease and spike glycoprotein using crystal structures as well as the homology models listed in Table 1.14.
Summary of combined main protease and spike glycoprotein studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Main protease: 6LU7 | 2454 FDA-approved drugs from BindingDB | AutoDock Vina | Main protease: Saquinavir, tadalafil, rivaroxaban, sildenafil, dasatinib, vardenafil, montelukast | 107 | |||
Spike glycoprotein: HM (6ACD) | Spike glycoprotein: Ergotamine, amphotericin B, vancomycin, zafirlukast, lanicor | ||||||
Main protease: 6Y84 | 15 antimalarial drugs, 2413 FDA-approved drugs DrugBank | Glide | NAMD | 50 ns | MM-PBSA | Main protease: Acarbose, colistin, paromomycin | 108 |
Spike glycoprotein: 6VW1 | Spike glycoprotein: Framycetin, acarbose, paromomycin | ||||||
Main protease: 6LU7 | FDA-approved drugs DrugBank | Glide | Main protease: Zanamivir, bortezomib, saquinavir, flavin adenine dinucleotide (FAD) adeflavin, cangrelor, carfilzomib, indinavir, remdesivir | 109 | |||
Spike glycoprotein: HM (2GHV) | Spike glycoprotein: Cangrelor, flavin adenine dinucleotide (FAD) adeflavin, tiludronate | ||||||
Main protease: 6LU7 | 13 approved antimalarial drugs | Glide | Main protease: Halofantrine, mefloquine | 110 | |||
Spike glycoprotein: 6M0J, 6YLA | Spike glycoprotein: Doxycycline, hydroxychloroquine |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Main protease: 6LU7 | 2454 FDA-approved drugs from BindingDB | AutoDock Vina | Main protease: Saquinavir, tadalafil, rivaroxaban, sildenafil, dasatinib, vardenafil, montelukast | 107 | |||
Spike glycoprotein: HM (6ACD) | Spike glycoprotein: Ergotamine, amphotericin B, vancomycin, zafirlukast, lanicor | ||||||
Main protease: 6Y84 | 15 antimalarial drugs, 2413 FDA-approved drugs DrugBank | Glide | NAMD | 50 ns | MM-PBSA | Main protease: Acarbose, colistin, paromomycin | 108 |
Spike glycoprotein: 6VW1 | Spike glycoprotein: Framycetin, acarbose, paromomycin | ||||||
Main protease: 6LU7 | FDA-approved drugs DrugBank | Glide | Main protease: Zanamivir, bortezomib, saquinavir, flavin adenine dinucleotide (FAD) adeflavin, cangrelor, carfilzomib, indinavir, remdesivir | 109 | |||
Spike glycoprotein: HM (2GHV) | Spike glycoprotein: Cangrelor, flavin adenine dinucleotide (FAD) adeflavin, tiludronate | ||||||
Main protease: 6LU7 | 13 approved antimalarial drugs | Glide | Main protease: Halofantrine, mefloquine | 110 | |||
Spike glycoprotein: 6M0J, 6YLA | Spike glycoprotein: Doxycycline, hydroxychloroquine |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
The homology model generated by Qiao et al.107 compared well with the crystal structure of S-protein released later (e.g. PDB ID: 6LZG, 6VW1). However, such comparison with existing X-ray structure was not provided by Hall and Ji.109 Most of the studies have used Glide for molecular docking. Two studies showed saquinavir as a promising hit for main protease, whereas montelukast was reproduced as Mpro inhibitor of SARS-CoV-2 in one of the studies. Sachdeva et al.110 performed virtual screening in both the targets of the SARS-CoV-2 considering remdesivir as the reference drug. In addition, for docking with SARS-CoV-2 Mpro, the co-crystallized ligand N3 has also been considered as reference. However, it should be noted that the target for remdesivir is RdRp and not Mpro or spike protein, and hence is not a relevant reference drug for these targets.
1.3.11 Docking in Multiple Structural Proteins
This section reports in silico drug repurposing studies in multiple structural proteins such as E-protein, N-protein and M-protein using crystal structures as well as homology models111,112 (Table 1.15).
Summary of structural protein studiesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
E-protein: HM (5X29) | Ligands of E-protein and N-protein from PDB | MOE | E-protein: Mycophenolic acid | 111 | |||
N-protein: HM (1SSK) | |||||||
E-protein: HM (2MM4) | 200 natural and 348 antiviral compounds from SelleckChem | AutoDock Vina, AutoDock | GROMACS | 100 ns | E-protein: Rutin, doxycycline | 112 | |
N-protein:6-3M | N-protein:Simeprevir, grazoprevir | ||||||
M-protein:HM (4F91) | M-protein:Caffeic acid, ferulic acid |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
E-protein: HM (5X29) | Ligands of E-protein and N-protein from PDB | MOE | E-protein: Mycophenolic acid | 111 | |||
N-protein: HM (1SSK) | |||||||
E-protein: HM (2MM4) | 200 natural and 348 antiviral compounds from SelleckChem | AutoDock Vina, AutoDock | GROMACS | 100 ns | E-protein: Rutin, doxycycline | 112 | |
N-protein:6-3M | N-protein:Simeprevir, grazoprevir | ||||||
M-protein:HM (4F91) | M-protein:Caffeic acid, ferulic acid |
Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
Azeez et al. reported the homology model of the envelope and nucleocapsid phosphoprotein of SARS-CoV-2.111 Some selected ligands known to bind E-protein and N-protein were docked using MOE software. A promising drug binding to envelope protein was mycophenolic acid. Mycophenolate mofetil has shown inhibitory activity against SARS-CoV-2 in VeroE6 cells with an EC50 of 0.47 µM. In another study, Bhowmik et al. identified potential molecules against three essential structural proteins of SARS-CoV-2, envelope (E)-protein, membrane (M)-protein and nucleocapsid (N)-protein.112 The proposed hits have not yet been experimentally tested in the assay of respective proteins, but doxycycline has been reported to have EC50 of 5.6 µM for cellular inhibition of SARS-CoV-2.
1.3.12 Docking in Proteases
Two in silico studies reported drug repurposing on proteases, viz. main protease and papain-like protease113,114 (Table 1.16).
Summary of studies on proteasesa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Main protease: 6LU7 | 8 protease inhibitors | VINA/VegaZZ | Main protease:Lopinavir, darunavir, famotidine | 113 | |||
PLpro: 6WUC | PLpro: Ribavirin, famotidine | ||||||
Main protease: 6LU7 | Databases of natural products, approved drugs from ZINC | AutoDock Vina | GROMACS | 50 ns | MM-PBSA | Main protease: Lopinavir, tipranavir, nelfinavir | 114 |
PLpro: 6W9C | PLpro: Lopinavir, tipranavir, nelfinavir |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Main protease: 6LU7 | 8 protease inhibitors | VINA/VegaZZ | Main protease:Lopinavir, darunavir, famotidine | 113 | |||
PLpro: 6WUC | PLpro: Ribavirin, famotidine | ||||||
Main protease: 6LU7 | Databases of natural products, approved drugs from ZINC | AutoDock Vina | GROMACS | 50 ns | MM-PBSA | Main protease: Lopinavir, tipranavir, nelfinavir | 114 |
PLpro: 6W9C | PLpro: Lopinavir, tipranavir, nelfinavir |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
From a docking study in Mpro,113 both lopinavir and darunavir showed lower binding free energy as compared to famotidine. A similar trend was found in docking studies in PLpro, where the antiviral drug ribavirin showed lower binding energy than famotidine. Mitra et al. developed two pharmacophore models (Ph-1 and Ph-2) obtained from reported specific inhibitors of SARS-CoV Mpro and PLpro.114 The shortlisted hits from pharmacophore models were docked into the active sites of SARS-CoV-2 Mpro and model of PLpro followed by MD simulations. Both nelfinavir and tipranavir showed stable binding to both proteases indicating promising repurposed candidates. The reported Mpro inhibitory activity for nelfinavir and tipranavir was 234 µM and 180 µM, respectively, whereas it was not reported for PLpro.
1.3.13 Docking in Multiple Targets
There are studies in the literature which report structure-based studies on multiple SARS-CoV-2 targets along with some human targets, and these are summarized in Table 1.17.
Summary of studies on all key viral and human targetsa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . | |
---|---|---|---|---|---|---|---|---|
NSP1: HM (2K87) | Databases of natural products, approved drugs from ZINC + 78 antiviral drugs | ICM | NSP1:Piperacillin, cefpiramide, streptomycin, lymecycline, tetracycline | 115 | ||||
NSP3b: HM (2FKV) | NSP3b: Remdesivir, lopinavir, chloroquine | |||||||
NSP3c: HM (2W2G) | NSP3c: Ritonavir, darunavir, lopinavir | |||||||
PLpro: HM (3E9S) | PLpro: Ribavirin, valganciclovir, thymidine, chloramphenicol, cefamandole, tigecycline, chlorphenesin carbamate, levodropropizine | |||||||
Mpro: HM (2AW0) | Mpro: Lymecycline, demeclocycline, doxycycline, oxytetracycline, nicardipine, telmisartan, conivaptan, montelukast | |||||||
NSP7_Nsp8 complex: HM (2AHM) | NSP7_Nsp8 complex: Arbidol | |||||||
NSP9: HM (3EE7) | ||||||||
Nsp10: HM (5NFY) | ||||||||
RdRp: HM (6NUR) | RdRp: Itraconazole, novobiocin, chenodeoxycholic acid, cortisone, idarubicin, silybin, pancuronium bromide, dabigatran etexilate | |||||||
Helicase: HM (6JYT) | Helicase: Lymecycline, cefsulodin, rolitetracycline, itraconazole, saquinavir, dabigatran, canrenoic acid | |||||||
NSP14: HM (5NFY) | NSP14: Arbidol | |||||||
NSP15: HM (2H85) | NSP15: Arbidol | |||||||
NSP16: HM (3R24) | ||||||||
S-protein: HM (3SCI) | S-protein: Rescinnamine, iloprost, prazosin, posaconazole, itraconazole, sulfasalazine, azlocillin, penicillin, cefsulodin, dabigatran etexilate | |||||||
TMPRSS2: HM (1Z8A) | TMPRSS2: Pivampicillin, hetacillin, cefoperazone, clindamycin | |||||||
ACE-2: Spike–ACE-2: Docked | ACE-2: Troglitazone, losartan, ergotamine, cefmenoxime, silybin | |||||||
Spike–ACE-2: Hesperidine | ||||||||
NSP1: HM (2GDT) | DrugBank | AutoDock Vina | Mpro: Dolutegravir, raltegravir, saquinavir, bictegravir, beclabuvir, tivatinib, filibuvir | 116 | ||||
NSP3: HM (6VXS) | ||||||||
NSP4: HM (3VCB) | ||||||||
Mpro: 6LU7 | ||||||||
NSP7: 6M71 | ||||||||
NSP8: 7BV1 | NSP8: Saquinavir | |||||||
NSP9: 6W4B | ||||||||
Nsp10: 6ZCT | Nsp10: Saquinavir | |||||||
RdRp: 7BV2 | RdRp: Saquinavir, remdesivir, filibuvir, sofosbuvir | |||||||
Helicase: HM (6JYT) | Helicase: Saquinavir, bictegravir | |||||||
NSP14: HM (5C8S) | NSP14: Saquinavir, bictegravir, nilotinib, montelukast | |||||||
NSP15: 6W01 | NSP15: Bictegravir, remdesivir, nilotinib | |||||||
NSP16: 6W4H | NSP16: Dolutegravir, raltegravir, saquinavir, montelukast | |||||||
S-protein: 6VYB | S-protein: Saquinavir | |||||||
N-protein: HM (2GIB) | N-protein: Radotinib | |||||||
E-protein: HM (1SSK) | E-protein: Beclabuvir | |||||||
ACE-2: 6CS2 | ||||||||
NSP3: 6VXS | 11 promising drugs | AutoDock Vina | NSP3: Lopinavir, pemirolast, eriodictyol | 117 | ||||
Mpro: 6LU7, 6Y84, 6M03 | Mpro: Lopinavir, remdesivir, eriodictyol, ritonavir, pemirolast, ribavirin, mycophenolic acid, chloroquine, hydroxychloroquine | |||||||
NSP9: 6W4B | NSP9: Lopinavir, eriodictyol, pemirolast | |||||||
NSP10: 6W75 | NSP10: Remdesivir, eriodictyol | |||||||
NSP16: 6W75 | NSP16: Ritonavir, eriodictyol | |||||||
S-protein: 6VSB, 2GHV | S-protein: Remdesivir, lopinavir, ritonavir, eriodictyol | |||||||
ACE-2: 6M18 | ACE-2: Lopinavir, ritonavir |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . | |
---|---|---|---|---|---|---|---|---|
NSP1: HM (2K87) | Databases of natural products, approved drugs from ZINC + 78 antiviral drugs | ICM | NSP1:Piperacillin, cefpiramide, streptomycin, lymecycline, tetracycline | 115 | ||||
NSP3b: HM (2FKV) | NSP3b: Remdesivir, lopinavir, chloroquine | |||||||
NSP3c: HM (2W2G) | NSP3c: Ritonavir, darunavir, lopinavir | |||||||
PLpro: HM (3E9S) | PLpro: Ribavirin, valganciclovir, thymidine, chloramphenicol, cefamandole, tigecycline, chlorphenesin carbamate, levodropropizine | |||||||
Mpro: HM (2AW0) | Mpro: Lymecycline, demeclocycline, doxycycline, oxytetracycline, nicardipine, telmisartan, conivaptan, montelukast | |||||||
NSP7_Nsp8 complex: HM (2AHM) | NSP7_Nsp8 complex: Arbidol | |||||||
NSP9: HM (3EE7) | ||||||||
Nsp10: HM (5NFY) | ||||||||
RdRp: HM (6NUR) | RdRp: Itraconazole, novobiocin, chenodeoxycholic acid, cortisone, idarubicin, silybin, pancuronium bromide, dabigatran etexilate | |||||||
Helicase: HM (6JYT) | Helicase: Lymecycline, cefsulodin, rolitetracycline, itraconazole, saquinavir, dabigatran, canrenoic acid | |||||||
NSP14: HM (5NFY) | NSP14: Arbidol | |||||||
NSP15: HM (2H85) | NSP15: Arbidol | |||||||
NSP16: HM (3R24) | ||||||||
S-protein: HM (3SCI) | S-protein: Rescinnamine, iloprost, prazosin, posaconazole, itraconazole, sulfasalazine, azlocillin, penicillin, cefsulodin, dabigatran etexilate | |||||||
TMPRSS2: HM (1Z8A) | TMPRSS2: Pivampicillin, hetacillin, cefoperazone, clindamycin | |||||||
ACE-2: Spike–ACE-2: Docked | ACE-2: Troglitazone, losartan, ergotamine, cefmenoxime, silybin | |||||||
Spike–ACE-2: Hesperidine | ||||||||
NSP1: HM (2GDT) | DrugBank | AutoDock Vina | Mpro: Dolutegravir, raltegravir, saquinavir, bictegravir, beclabuvir, tivatinib, filibuvir | 116 | ||||
NSP3: HM (6VXS) | ||||||||
NSP4: HM (3VCB) | ||||||||
Mpro: 6LU7 | ||||||||
NSP7: 6M71 | ||||||||
NSP8: 7BV1 | NSP8: Saquinavir | |||||||
NSP9: 6W4B | ||||||||
Nsp10: 6ZCT | Nsp10: Saquinavir | |||||||
RdRp: 7BV2 | RdRp: Saquinavir, remdesivir, filibuvir, sofosbuvir | |||||||
Helicase: HM (6JYT) | Helicase: Saquinavir, bictegravir | |||||||
NSP14: HM (5C8S) | NSP14: Saquinavir, bictegravir, nilotinib, montelukast | |||||||
NSP15: 6W01 | NSP15: Bictegravir, remdesivir, nilotinib | |||||||
NSP16: 6W4H | NSP16: Dolutegravir, raltegravir, saquinavir, montelukast | |||||||
S-protein: 6VYB | S-protein: Saquinavir | |||||||
N-protein: HM (2GIB) | N-protein: Radotinib | |||||||
E-protein: HM (1SSK) | E-protein: Beclabuvir | |||||||
ACE-2: 6CS2 | ||||||||
NSP3: 6VXS | 11 promising drugs | AutoDock Vina | NSP3: Lopinavir, pemirolast, eriodictyol | 117 | ||||
Mpro: 6LU7, 6Y84, 6M03 | Mpro: Lopinavir, remdesivir, eriodictyol, ritonavir, pemirolast, ribavirin, mycophenolic acid, chloroquine, hydroxychloroquine | |||||||
NSP9: 6W4B | NSP9: Lopinavir, eriodictyol, pemirolast | |||||||
NSP10: 6W75 | NSP10: Remdesivir, eriodictyol | |||||||
NSP16: 6W75 | NSP16: Ritonavir, eriodictyol | |||||||
S-protein: 6VSB, 2GHV | S-protein: Remdesivir, lopinavir, ritonavir, eriodictyol | |||||||
ACE-2: 6M18 | ACE-2: Lopinavir, ritonavir |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
1.3.13.1 Studies Involving Large Numbers of Targets
Three studies have reported screening of drugs in several key viral and human targets involved in COVID-19.115–117
Wu et al. reported one of the earlier computational drug repurposing studies in the early stages of SARS-CoV-2 infection.115 The authors systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses to predict their structures using homology modelling with PDB1018 database. The built model of the SARS-CoV-2 3CLpro compared with its crystal structure of SARS-CoV-2 3CLpro (IDB ID: 6LU7) showed a Cα RMSD of 0.471 Å on the overall structure and 0.126 Å in the substrate binding regions indicating adequate quality of the models for further studies. The selected drug database was screened using docking to identify possible targets of these compounds and potential drugs acting on certain targets, as reported in Table 1.17.
Xu et al. reported large-scale structure-based virtual screening116 in 16 viral proteins and ACE-2 protein (PDB ID: 6CS2). The promising hits from this large-scale exercise are reported (https://shennongproject.ai/) and include the following.
Dolutegravir and raltegravir exhibit strong binding affinity with multiple targets such as the main protease and exonuclease.
Saquinavir shows a strong affinity with the main protease as well as with many targets like S-protein, NSP8, NSP10, NSP12, NSP13, NSP14, and NSP16, suggesting that saquinavir might be a multi-target inhibitor.
Bictegravir exhibited strong affinity with Mpro, NSP13, NSP14, and NSP15, making it one of best performing drugs.
The cancer drugs like tivantinib, lifirafenib, entrectinib, nilotinib and radotinib and asthma drugs like montelukast and zafirlukast also were promising.
It is noted that the binding affinity of remdesivir with RdRp is poor over that with endonuclease, indicating limitations of this docking.
Some of the drugs reported as promising in this study have been investigated experimentally with saquinavir exhibiting an IC50 of 411 µM in Mpro, whereas its cellular assay for SARS-CoV-2 infection showed an EC50 of 8.83 µM, indicating that Mpro may not be the target of saquinavir. Dolutegravir and remdesivir have been reported to have an EC50 of 22.04 µM and 0.77 µM, respectively, for cellular SARS-CoV-2 infection.
Deshpande et al.117 showed that ritonavir exhibited efficient binding to NSP16 and spike protein, whereas remdesivir is more active against Mpro, spike protein and NSP10. Lopinavir showed promising binding with Mpro, NSP9, spike glycoprotein, NSP3 and ACE 2 protein. Eriodictyol showed high binding affinity against all selected proteins and pemirolast showed high binding efficiencies against Mpro and NSP9. Thus, the screened drugs have potential against multiple targets. The available experimental information shows that lopinavir binding to Mpro has been reproduced in this study.
1.3.13.2 Studies Involving Main Protease and Other Targets
The following studies have reported screening of drugs in main protease as well as some other viral and human targets involved in COVID-19.118–132
Cavasotto and Filippo performed a docking-based screening of approved drugs118 with receptor flexibility and multiple structures and ranking was calculated using ICM (ICM pro software) and QMDS (QM docking scoring function) scoring leading to identification of promising drugs for various targets as reported in Table 1.18. The Mpro inhibition observed in experiments for saquinavir and indinavir has been reproduced in this study. Iftikhar et al. explored in silico drug repurposing120 using a ranking score based on binding energy, clustering score, shape complementarity and functional significance of the binding pocket was applied to identify promising binders to respective targets. Three targets of SARS-CoV-2, viz. Mpro/3CLpro, RdRp and spike–ACE-2 interface investigated by Alexpandi et al.121 provided hits from quinoline drugs. None of them except saquinavir has been experimentally validated in their respective targets or even for SARS-CoV-2 inhibition. Saquinavir exhibited IC50 of 411 µM in Mpro, whereas its in cellular assay for SARS-CoV-2 infection showed an EC50 of 8.83 µM, indicating that Mpro may not be the target of saquinavir.
Summary of studies on main protease and other targetsa
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Mpro: 6YB7, 6LU7 | 11 552 unique compounds from ChEMBL, DrugBank, DrugCentral, SelleckChem | ICM | Mpro: Saquinavir, ritonavir, indinavir | 118 | |||
PLpro: 3E9S, 6WX4 | PLpro: Ziprasidone, darolutamide | ||||||
S-protein: 6M17 | S-protein: Pralatrexate | ||||||
Mpro: 6LU7 | 4500 drugs from ChEMBL, DrugBank and SelleckChem | AutoDock Vina | NAMD | 50 ns | MM-PBSA | Mpro: Talampicillin, lurasidone | 119 |
TMPRSS2: HM (2OQ5) | TMPRSS2: Rubitecan, loprazolam | ||||||
Mpro: 6LU7 | 4512 FDA-approved drugs from MTiOpen screen | AutoDock Vina, AutoDock | Mpro: Rimantadine, bagrosin, grazoprevir | 120 | |||
RdRp: HM (6NUR) | RdRp: Casopitant | ||||||
Helicase: HM (6JYT) | Helicase: Meclonazepam, oxiphenisatin | ||||||
Mpro: 6LU7 | 113 quinoline-based drugs from DrugBank | iGEMDOCK, AutoDock Vina | Mpro: Rilapladib, saquinavir, oxolinic acid, elvitegravir, batefenterol, sitafloxacin, alatrofloxacin, quarfloxin | 121 | |||
RdRp: HM (6NUR) | RdRp: Elvitegravir, oxolinic acid, saquinavir, garenoxacin, rilapladib, pelitinib, difloxacin, batefenterol, danofloxacin | ||||||
Spike–ACE-2 interface: 6M17 | Spike–ACE-2 interface: Saquinavir, rilapladib, quarfloxin, batefenterol, oxolinic acid, alatrofloxacin, dovitinib, rebamipide | ||||||
Mpro: 6LU7 | 76 antiviral drugs from Pubchem | AutoDock Vina, GOLD | YASARA | 100 ns | MM-PBSA | Mpro: Raltegravir, daclatasvir simeprevir, cobicistat, remdesivir | 122 |
RdRp: HM (6NUR) | RdRp: Raltegravir, daclatasvir, simeprevir, cobicistat, remdesivir | ||||||
Mpro: 6Y84 | 24 selected drugs | AutoDock Vina | Mpro: Saquinavir, metaquine, mefloquine, nelfinavir, piperaquine, dolutegravir | 123 | |||
NSP9: 6W4B | NSP9: Saquinavir, metaquine | ||||||
NSP15: 6VWW | NSP15: Saquinavir, metaquine, mefloquine, lopinavir, ritonavir | ||||||
Spike–ACE-2 interface: HM | Spike–ACE-2 interface: Saquinavir, metaquine, lopinavir, ritonavir | ||||||
Mpro: 6LU7 | Database of antiviral, anti-infectious and antiprotease compounds from SelleckChem | Glide | Mpro: Cyclocytidine hydrochloride, trifluridine, adonitol, meropenem, penciclovir | 125 | |||
NSP10/NSP16: 6W6I | NSP10/NSP16: Telbivudine, oxytetracycline dihydrate, methylgallate, 2-deoxyglucose, daphnetin | ||||||
Mpro: 6Y84 | FDA-approved drugs from ZINC database | AutoDock Vina | NAMD | 20 ns | MM-GBSA | Mpro: Tetracycline, dihydroergotamine, dutasteride, nelfinavir, paliperidone | 126 |
RdRp–NSP8 interface: HM (6NUR) | RdRp–NSP8 interface: Ergotamine, dihydroergotamine, bromocriptine, tipranavir, conivaptan, eltrombopag | ||||||
Mpro: 6LU7 | 4 known drugs | AutoDock Vina | Mpro: Ribavirin, luteolin | 127 | |||
S-protein: 6VSB | S-protein: Luteolin | ||||||
RdRp: HM (6NUS) | RdRp: Luteolin | ||||||
PLpro: HM (4OVZ) | PLpro: Ribavirin, luteolin | ||||||
Mpro: 1Q2Wc | Anitimalarial, antifungal, antiviral drugs | MOE | Mpro: Brincidofovir | 128 | |||
ACE-2: 6M0J | ACE-2: Brincidofovir | ||||||
Mpro: 6LU7 | Anti-HIV, anti-hepatitis drugs, antiviral phytochemicals | AutoDock | Mpro: Paritaprevir, ergotamine tartrate, telaprevir, dihydroergotamine, simeprevir, ergotamine alkaloid, telmisartan, ritonavir | 129 | |||
RdRp: 6NUR | RdRp: Pemetrexed, raltitrexed | ||||||
PLpro: 3E9S | PLpro: Chloroquine, paritaprevir | ||||||
Furin: 6HZD | Furin: Paritaprevir, chloroquine, ritonavir | ||||||
Mpro: HM (1UJ1) | 123 antiviral drugs | AutoDock Vina | GROMACS | 100 ns | Mpro: Paritaprevir, raltegravir | 130 | |
NSP16: HM (3R24) | NSP16: Dolutegravir, bictegravir | ||||||
Mpro: HMd | 2471 FDA-approved drugs from DrugBank | AutoDock Vina | Mpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | 131 | |||
PLpro: HMd | PLpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | ||||||
S-protein: HMd | S-protein: Glycyrrhizic acid, hesperidin | ||||||
ACE-2: 1R42 | ACE-2: Glycyrrhizic acid, hesperidin | ||||||
TMPRSS2: HMd | TMPRSS2: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | ||||||
HR1: HMd | HR1: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | ||||||
Mpro: 6Y2E | 3 drugs | AutoDock Vina | Mpro: Azithromycin, chloroquine, hydroxychloroquine | 132 | |||
S-protein: 6VW1 | S-protein: Azithromycin, chloroquine | ||||||
ACE-2: 6VW1 | ACE-2: Azithromycin, chloroquine | ||||||
Cathepsin: 2XU3 | Cathepsin: Azithromycin, chloroquine, hydroxychloroquine |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Mpro: 6YB7, 6LU7 | 11 552 unique compounds from ChEMBL, DrugBank, DrugCentral, SelleckChem | ICM | Mpro: Saquinavir, ritonavir, indinavir | 118 | |||
PLpro: 3E9S, 6WX4 | PLpro: Ziprasidone, darolutamide | ||||||
S-protein: 6M17 | S-protein: Pralatrexate | ||||||
Mpro: 6LU7 | 4500 drugs from ChEMBL, DrugBank and SelleckChem | AutoDock Vina | NAMD | 50 ns | MM-PBSA | Mpro: Talampicillin, lurasidone | 119 |
TMPRSS2: HM (2OQ5) | TMPRSS2: Rubitecan, loprazolam | ||||||
Mpro: 6LU7 | 4512 FDA-approved drugs from MTiOpen screen | AutoDock Vina, AutoDock | Mpro: Rimantadine, bagrosin, grazoprevir | 120 | |||
RdRp: HM (6NUR) | RdRp: Casopitant | ||||||
Helicase: HM (6JYT) | Helicase: Meclonazepam, oxiphenisatin | ||||||
Mpro: 6LU7 | 113 quinoline-based drugs from DrugBank | iGEMDOCK, AutoDock Vina | Mpro: Rilapladib, saquinavir, oxolinic acid, elvitegravir, batefenterol, sitafloxacin, alatrofloxacin, quarfloxin | 121 | |||
RdRp: HM (6NUR) | RdRp: Elvitegravir, oxolinic acid, saquinavir, garenoxacin, rilapladib, pelitinib, difloxacin, batefenterol, danofloxacin | ||||||
Spike–ACE-2 interface: 6M17 | Spike–ACE-2 interface: Saquinavir, rilapladib, quarfloxin, batefenterol, oxolinic acid, alatrofloxacin, dovitinib, rebamipide | ||||||
Mpro: 6LU7 | 76 antiviral drugs from Pubchem | AutoDock Vina, GOLD | YASARA | 100 ns | MM-PBSA | Mpro: Raltegravir, daclatasvir simeprevir, cobicistat, remdesivir | 122 |
RdRp: HM (6NUR) | RdRp: Raltegravir, daclatasvir, simeprevir, cobicistat, remdesivir | ||||||
Mpro: 6Y84 | 24 selected drugs | AutoDock Vina | Mpro: Saquinavir, metaquine, mefloquine, nelfinavir, piperaquine, dolutegravir | 123 | |||
NSP9: 6W4B | NSP9: Saquinavir, metaquine | ||||||
NSP15: 6VWW | NSP15: Saquinavir, metaquine, mefloquine, lopinavir, ritonavir | ||||||
Spike–ACE-2 interface: HM | Spike–ACE-2 interface: Saquinavir, metaquine, lopinavir, ritonavir | ||||||
Mpro: 6LU7 | Database of antiviral, anti-infectious and antiprotease compounds from SelleckChem | Glide | Mpro: Cyclocytidine hydrochloride, trifluridine, adonitol, meropenem, penciclovir | 125 | |||
NSP10/NSP16: 6W6I | NSP10/NSP16: Telbivudine, oxytetracycline dihydrate, methylgallate, 2-deoxyglucose, daphnetin | ||||||
Mpro: 6Y84 | FDA-approved drugs from ZINC database | AutoDock Vina | NAMD | 20 ns | MM-GBSA | Mpro: Tetracycline, dihydroergotamine, dutasteride, nelfinavir, paliperidone | 126 |
RdRp–NSP8 interface: HM (6NUR) | RdRp–NSP8 interface: Ergotamine, dihydroergotamine, bromocriptine, tipranavir, conivaptan, eltrombopag | ||||||
Mpro: 6LU7 | 4 known drugs | AutoDock Vina | Mpro: Ribavirin, luteolin | 127 | |||
S-protein: 6VSB | S-protein: Luteolin | ||||||
RdRp: HM (6NUS) | RdRp: Luteolin | ||||||
PLpro: HM (4OVZ) | PLpro: Ribavirin, luteolin | ||||||
Mpro: 1Q2Wc | Anitimalarial, antifungal, antiviral drugs | MOE | Mpro: Brincidofovir | 128 | |||
ACE-2: 6M0J | ACE-2: Brincidofovir | ||||||
Mpro: 6LU7 | Anti-HIV, anti-hepatitis drugs, antiviral phytochemicals | AutoDock | Mpro: Paritaprevir, ergotamine tartrate, telaprevir, dihydroergotamine, simeprevir, ergotamine alkaloid, telmisartan, ritonavir | 129 | |||
RdRp: 6NUR | RdRp: Pemetrexed, raltitrexed | ||||||
PLpro: 3E9S | PLpro: Chloroquine, paritaprevir | ||||||
Furin: 6HZD | Furin: Paritaprevir, chloroquine, ritonavir | ||||||
Mpro: HM (1UJ1) | 123 antiviral drugs | AutoDock Vina | GROMACS | 100 ns | Mpro: Paritaprevir, raltegravir | 130 | |
NSP16: HM (3R24) | NSP16: Dolutegravir, bictegravir | ||||||
Mpro: HMd | 2471 FDA-approved drugs from DrugBank | AutoDock Vina | Mpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | 131 | |||
PLpro: HMd | PLpro: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | ||||||
S-protein: HMd | S-protein: Glycyrrhizic acid, hesperidin | ||||||
ACE-2: 1R42 | ACE-2: Glycyrrhizic acid, hesperidin | ||||||
TMPRSS2: HMd | TMPRSS2: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | ||||||
HR1: HMd | HR1: Glecaprevir, simeprevir, paritaprevir, glycyrrhizic acid, ledipasvir, hesperidin | ||||||
Mpro: 6Y2E | 3 drugs | AutoDock Vina | Mpro: Azithromycin, chloroquine, hydroxychloroquine | 132 | |||
S-protein: 6VW1 | S-protein: Azithromycin, chloroquine | ||||||
ACE-2: 6VW1 | ACE-2: Azithromycin, chloroquine | ||||||
Cathepsin: 2XU3 | Cathepsin: Azithromycin, chloroquine, hydroxychloroquine |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
Wrong choice of target.
Information on template used for homology modelling not provided in the reference.
Ahmed et al.122 developed a quantitative structure–activity relationship (QSAR) model using docking energies as dependent variables and several molecular descriptors as independent variables. QSAR analysis shows that molecular weight, Total Polar Surface Area TPSA (Å2), number of hydrogen donor, hydrogen atom, and C–C single bonds contribute to the variation in binding affinity. Such QSAR relationships would help estimating binding affinity of novel molecules designed from similar class of drugs. The study by Barros et al.123 proposed that saquinavir and metaquine exhibit binding to Mpro, NSP9, NSP15 and the spike–ACE-2 interface and may be promising candidates. Saquinavir, nelfinavir and lopinavir have been tested in Mpro assay with IC50 of 411, 234 and 486 µM, respectively.124 The cellular assay for these drugs for SARS-CoV-2 infection showed EC50 of 8.83, 1.13, and 9.12 µM, respectively. This indicates that Mpro may not be the target for these drugs for inhibition of SARS-CoV-2 infection. Yu et al.127 showed that luteolin binds to all studied targets, viz. 3CLpro, PLpro, RdRp and spike glycoprotein. Luteolin exhibited inhibition of the spike–ACE-2 complex in SARS-CoV with an EC50 of 9.02 µM; however, this has not yet been explored experimentally in other target assays.
Manikyam and Joshi129 performed molecular docking in multiple human and SARS-CoV-2 proteins. Among the proposed hits, the drugs tested experimentally in Mpro assay are telaprevir and simeprevir, with IC50 of 10.7 and 13.74 µM, respectively. The other drugs have not been tested in assays of the respective targets. It should be noted that the authors claim to have performed molecular docking in the viral spike protein; however, no structure or model for spike protein was used for screening. Khan et al.130 systematically explored docking of 123 antiviral drugs against 3CLpro and NSP16/2′-OMTase of SARS-CoV-2 with two drugs each were found to be hits for both the targets. Dolutegravir, a predicted NSP16 inhibitor, has shown inhibition of SAR-CoV-2 infection in cellular assay with EC50 of 22.04 µM.
1.3.13.3 Studies on Mixed Targets
A summary of in silico screening in mixed SARS-CoV-2 and human targets not involving screening in main protease is reported in this section (Table 1.19).
Summary of studies on targets other than main proteasea
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Helicase: HM (6JYT) | 54 FDA-approved antiviral drugs | AutoDock | Helicase: Simeprevir, paritaprevir, grazoprevir | 133 | |||
NSP14: HM (5C8S) | NSP14: Simeprevir, paritaprevir, grazoprevir, sinefungin | ||||||
S-protein: 6VYB | 4 drugs | AutoDock | GROMACS | 50 ns | MM-PBSA | S-protein: Chloroquine, SAA09E2 | 134 |
RdRp: 6M71 | RdRp: Remdesivir, Favipiravir | ||||||
ACE-2: 1R4L | |||||||
NSP9: 6W4B | 2000 FDA-approved drugs | AutoDock Vina, AutoDock | Desmond | 100 ns | NSP9: Conivaptan, telmisartan, phaitanthrin D | 135 | |
S-protein: 6LZG | S-protein: Tegobuvir, bromocriptine, baicalin |
HMb(template)/PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|
Helicase: HM (6JYT) | 54 FDA-approved antiviral drugs | AutoDock | Helicase: Simeprevir, paritaprevir, grazoprevir | 133 | |||
NSP14: HM (5C8S) | NSP14: Simeprevir, paritaprevir, grazoprevir, sinefungin | ||||||
S-protein: 6VYB | 4 drugs | AutoDock | GROMACS | 50 ns | MM-PBSA | S-protein: Chloroquine, SAA09E2 | 134 |
RdRp: 6M71 | RdRp: Remdesivir, Favipiravir | ||||||
ACE-2: 1R4L | |||||||
NSP9: 6W4B | 2000 FDA-approved drugs | AutoDock Vina, AutoDock | Desmond | 100 ns | NSP9: Conivaptan, telmisartan, phaitanthrin D | 135 | |
S-protein: 6LZG | S-protein: Tegobuvir, bromocriptine, baicalin |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Homology model using template with PDB ID.
Gurung reported homology modelling of two SARS-CoV-2 targets, viz. NSP13 helicase and NSP14 (N7-MTase domain) for drug repurposing.133 None of the hit drugs have been experimentally tested in the respective assays and cellular assay. Bibi et al.134 explored inhibitory mechanisms of four drugs in multiple targets. The molecular docking and MD simulation indicated that remdesivir and favipiravir block the catalytic site of RdRp, which corroborates experimental findings.
1.3.14 Discussion and Consensus Screening Protocol from the Reviewed Literature
Based on this discussion, it is clear that a large number of in silico studies (∼52) have been performed on main protease as compared to other SARS-CoV-2 proteins. In these studies, protein structures having co-crystallized ligands were preferably selected over apo structures (e.g. for main protease the structure with inhibitor N3 was used; PDB ID: 6LU7 in most reports). In the general procedure for docking that emerges from various studies, the hydrogens are added to the selected protein structure and the system is minimized using the force-field method. The drug structures are taken from the literature and are energy-minimized. The docking of drugs or ligands is done within the binding pocket of co-crystallized ligand (e.g. N3 site for Mpro or respective sites in selected targets) by several studies; however, only a few have explored all cavities in the target. For screening of a library of drugs or ligands, majority of studies have used AutoDock Vina as initial screening methodology. The filtered drugs are further docked with additional precision using AutoDock program. The other programs used for docking are Glide, ICM, iDock, etc. with increasing precision of docking in the respective software. The drug–receptor complexes of promising hits from the docking exercise are normally subjected to MD simulation to confirm the stability of the complex. The initial complex structure taken from docking is subjected to 1 ns to 20 ns MD simulations using different tools like AMBER, GROMACS, NAMD, etc. The filtered hits from such short MD are subjected to production runs of long duration such as 50 ns to 200 ns MD simulations and complexes are evaluated for stability using RMSD, radius of gyration, root mean square fluctuations, intermolecular H-bonds, solvent accessible surface area, etc. The binding energy is generally calculated from the MM-GBSA or MM-PBSA method. Based on comparison of binding energy obtained from such exercises for reference drug/ligand, promising hits for repurposing in SARS-CoV-2 are proposed (hits have lower binding energy than reference drug/ligand). The reference drugs/ligands are known drugs/ligands or co-crystallized ligands for the selected target (e.g. remdesivir has been used as a reference ligand for RdRp, whereas N3 has been used for Mpro/3CLpro). The majority of drugs bind to the active site or co-crystallized ligand site and there are rare reports of allosteric binding of drugs in proteins. For the protein structures without co-crystallized ligands, all cavities have been explored to find best binding site for the drugs. The assessment of promising hits from various structure-based approaches shows that many have potential to provide clues to experimentalists. Some of the structure-based studies have demonstrated that they are able to reproduce experimental information, e.g. remdesivir was a promising hit in RdRp and boceprevir, telaprevir, atazanavir were promising hits in Mpro. One study also demonstrated good correlation between calculated binding affinity or binding scores and Mpro assay as well as SARS-CoV-2 inhibitory activity (IC50/EC50). Thus, it may be inferred that structure-based approaches indeed provide clues for selecting drugs for experimental screening in SARS-CoV-2 infection.
Based on available experimental information on Mpro assay as well as SARS-CoV-2 cellular assay, it may be inferred that Mpro may not be the primary target for following drugs: nelfinavir, saquinavir, lopinavir and tipranavir, although they have predicted as promising hits in several structure-based studies reported earlier.
1.4 Ligand-based Approaches for Drug Repurposing
The ligand-based approaches make use of QSAR, similarity-based approaches and pharmacophore-based approaches for identifying drug repurposing opportunities. These approaches largely depend on available information of ligands with corresponding experimental activities against specific target as collected in different databases such as BindingDB, ChEMBL and PubChem. A limited number of reports have been published that cover ligand-based approaches for drug repurposing in SARS-CoV-2.136–138
1.4.1 QSAR-based Approaches
Alves et al.136 reported QSAR using the SARS-CoV Mpro inhibitory activities of 113 compounds, while Gao et al.137 reported a machine-learning ligand-based model using inhibitory activities of 314 SARS-CoV or SARS-CoV-2 3CLpro inhibitors. The developed QSAR models were used to screen larger drug datasets like DrugBank and FDA-approved drugs. These two articles suggest sufugolix, cenicriviroc, proglumetacin, lopinavir, ritonavir, tetrandrine, cobicistat, losartan, ribavirin, remdesivir, aviptadil, danoprevir, proflavine, chloroxine, demexiptiline, fluorouracil and oteracil for repurposing. Alves et al. also performed docking of drugs in Mpro of SARS-CoV-2 (PDB ID: 6LU7) using Glide docking; however, the docking scores and binding affinity were poorly correlated. Thus, the authors concluded that docking was not a viable virtual screening approach for Mpro of SARS-CoV-2.
1.4.2 Pharmacophore-based Approaches
Yoshino et al. reported identification of key pharmacophoric features that contribute to interaction between SARS-CoV-2 Mpro and its inhibitors.138 Co-crystals from Mpro of SARS-CoV (PDB ID: 2A5I, 2OP9) and SARS-CoV-2 Mpro (PDB ID: 6LU7) were used to extract pharmacophores in Phase program. Indinavir was aligned to the pharmacophore features and subjected to hydrogen bond optimization, energy minimization and MD simulations for 1 µs under the isothermal-isobaric NPT ensemble. Indinavir satisfies all criteria and was hence suggested by the authors as a drug against SARS-CoV-2.
1.5 Other Approaches for Drug Repurposing
Various advanced methods such as network-based methods, machine learning and artificial intelligence have been applied for repurposing of drugs against COVID-19. This section summarizes of efforts reported in the literature using these methods.
1.5.1 Machine Learning-based Methods
Machine learning methods have been applied to quickly identify drugs against SARS-CoV-2. Details of these machine learning efforts are summarized in Table 1.20; these efforts used either the molecular descriptors or docking interactions for machine learning.
Summary of machine learning studiesa
PDB/HM . | Database used . | Method applied . | Descriptors . | Hits . | Reference . |
---|---|---|---|---|---|
UNII Database, DrugBank, Therapeutic Targets Database | Support Vector Machine (SVM) | ∼5300 AlvaDesc descriptors | Phenazopyridine, abemaciclib, promazine, tykerb, pirenzepine, ebastine, alectinib lestaurtinib, vorinostat, cefmenoxime, lifitegrast, vemurafenib, selimexor, amorolfine, dacomitinib, enalaprilat | 139 | |
DrugBank | Deep Neural Network (DNN) | 613 molecular descriptors | Boceprevir, chloroquine, homoharringtonine, tilorone, salinomycin | 140 | |
6LU7 | FDA-approved drugs | Molecule Transformer-Drug Target Interaction (MT-DTI) | Drug–target interactions | Atazanavir, remdesivir, efavirenz, ritonavir, dolutegravir | 141 |
3TNT | Chimdiv; Targetmols, database of tripeptides | Dense Fully Convolutional Neural Network (DFCNN) | Drug–target interactions | Meglumine, adenosine, vidarabine, d-sorbitol, sodium gluconate, d-mannitol, ganciclovir, chlorobutanol | 142 |
PDB/HM . | Database used . | Method applied . | Descriptors . | Hits . | Reference . |
---|---|---|---|---|---|
UNII Database, DrugBank, Therapeutic Targets Database | Support Vector Machine (SVM) | ∼5300 AlvaDesc descriptors | Phenazopyridine, abemaciclib, promazine, tykerb, pirenzepine, ebastine, alectinib lestaurtinib, vorinostat, cefmenoxime, lifitegrast, vemurafenib, selimexor, amorolfine, dacomitinib, enalaprilat | 139 | |
DrugBank | Deep Neural Network (DNN) | 613 molecular descriptors | Boceprevir, chloroquine, homoharringtonine, tilorone, salinomycin | 140 | |
6LU7 | FDA-approved drugs | Molecule Transformer-Drug Target Interaction (MT-DTI) | Drug–target interactions | Atazanavir, remdesivir, efavirenz, ritonavir, dolutegravir | 141 |
3TNT | Chimdiv; Targetmols, database of tripeptides | Dense Fully Convolutional Neural Network (DFCNN) | Drug–target interactions | Meglumine, adenosine, vidarabine, d-sorbitol, sodium gluconate, d-mannitol, ganciclovir, chlorobutanol | 142 |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay. Drugs in bold italics have both the assays reported.
1.5.1.1 Machine Learning Using Molecular Descriptors
Kowalewski and Ray reported application of machine learning to identify several drug candidates against COVID-19.139 Assay data of 24 human targets related to SARS-CoV-2 were used to train machine learning models for predicting drugs. Out of the predicted drugs phenazopyridine (EC50 28 µM), abemaciclib (EC50 43.7 µM) and ebastine (EC50 6.92 µM) were reported to inhibit the cellular SARS-CoV-2 infection. Ke et al. reported application of an artificial intelligence (AI)-based platform to identify potential drugs for COVID-19.140 The authors used a unique approach of re-learning the models after testing promising hits against feline infectious peritonitis. Drugs reported to inhibit SARS-CoV-2 in cellular assay are chloroquine (EC50 1.13 µM), homoharringtonine (EC50 2.55 µM), tilorone (EC50 4.09 µM) and salinomycin (EC50 0.24 µM), while chloroquine (EC50 7.16 µM) and boceprevir (EC50 0.95 µM) are reported to inhibit 3CLpro. Both articles reported models that were able to predict compounds acting against SARS-CoV-2, thus demonstrating the promise of machine learning methods.
1.5.1.2 Machine Learning Using Docking Interactions
Beck et al. reported use of a pre-trained deep learning model to repurpose drugs inhibiting 3CLpro for SARS-CoV-2.141 The hits predicted from this approach are atazanavir (EC50 9.36 µM), remdesivir (EC50 0.62 µM), ritonavir (EC50 1 µM) and dolutegravir (EC50 22.04 µM), which have been found active in cellular assay of SARS-CoV-2. However, remdesivir is known to act via RdRp inhibition and its ability to inhibit 3CLpro can be confirmed only after experimental evidence. Zhang et al. reported repurposing using a deep learning based method in 3CLpro with the help of drug–receptor interactions.142 Valganciclovir, a prodrug of ganciclovir has been tested in 3CLpro of SARS-CoV-2 with IC50 of 16.66 µM.
1.5.2 Pharmacology-based Network Analysis Methods
Pharmacology-based network analysis methods also have been utilized in the quest of repurposing drugs against SARS-CoV-2. The network analysis was performed either by utilizing protein–protein interactions or gene expression profiling.
1.5.2.1 Protein–Protein Interactions
Gysi et al. identified promising drugs using network proximity, diffusion and AI-based metrics.143 A list of 81 repurposed drugs was finalized after prioritization and screening. Such an extensive list of hits has limited relevance to the pandemic, as computational methods are expected to pinpoint towards effective drugs for specific targets for experimental testing. Zhou et al. reported use of systems pharmacology based network medicine for repurposing drugs for COVID-19.144 The network-based proximity analyses suggested repurposable candidates using a gene set enrichment analysis (GSEA) score. Out of 16 predicted opportunities, viz. mesalazine, toremifene (EC50 11.3 µM), eplerenone, paroxetine, sirolimus, dactinomycin, irbesertan, mercaptopurine, melatonin, quniacrine, carvedilol, colchicine, camphor, equilin, oxymetholone and emodin, only one drug was tested for SARS-CoV-2 inhibition. Furthermore, Cava et al. presented repurposing drugs for COVID-19, by building correlation between ACE-2 and genes in TCGA-LUAD.145 A protein–protein interaction network was constructed considering interactions among ACE-2-correlated genes. None of the predicted drugs nimesulide, fluticasone, thiabendazole, photofrin, didanosine and flutamide was tested against SARS-CoV-2 infection.
1.5.2.2 Expression Profiling
Glinsky et al.146 reported identification of repressors and activators of ACE-2 and FURIN using GSEA scores and expression profiling experiments. This suggested that quercetin and vitamin D, respectively, alter significant numbers of human and SARS-CoV-2 proteins which are relevant for COVID-19. Two interventional randomized clinical trials evaluating effects of vitamin D on prevention and treatment of COVID-19 are listed on ClinicalTrials.gov. Furthermore, vitamin D is being routinely prescribed by clinicians to reduce ill effects of COVID-19.
1.6 Understanding Human Targets in COVID-19 From an In Silico Perspective
Viruses are unique parasites which contain genetic material encapsulated in a protein shell and can only replicate inside a living host. Since they lack metabolic machinery of their own, they are totally dependent on the host cell for completing their life cycle. Post-infection the virus utilizes the host machinery for attachment and penetration into the host cells, replication and assembly, and finally is released to infect other cells. Given the criticality of host targets in the viral life cycle, host factors could be important targets for COVID-19. Exploring host targets or pathways that are critical in COVID-19 has some benefits,147 as follows.
Host targets may provide a broader antiviral spectrum, e.g. drug acting on a particular host target can provide protection against all viruses dependent on that target. Converesly, viruses have very high mutation rates, which pose challenges in terms of resistance to drugs in the long term.
Host targets involved in progression of the disease can be targeted so that worsening of disease can be controlled, e.g. targets involved in ARDS or targets which can activate host immune response.
Host targets can have known drugs acting on these targets, which can make repurposing easier and faster.
1.6.1 Host Proteins Involved in the SARS-CoV-2 Life Cycle
The entry of SARS-CoV-2 into the host cells via the spike protein is discussed in Section 1.2.1. Post-attachment, the SARS-CoV-2 is internalized and releases the genetic material (RNA) into the host cell cytoplasm either by fusion at the cell plasma membrane or engulfing of the ACE-2 and SARS-CoV-2 bound complex. The virus then smartly utilizes the host machinery for transcription, translation, post-translational modifications and finally release to infect adjoining cells.
1.6.2 Host Response to SARS-CoV-2 Infection
1.6.2.1 SARS-CoV-2 Induced Immune Response
SARS-CoV-2 infection can activate the host immune responses, which in severe cases could result in exhausted cytotoxic lymphocytes, lymphocytopenia and a severe cytokine storm. Higher levels of pro-inflammatory cytokines and chemokines, viz. interleukin (IL)-17, IL-2, IL-7, IL-6, granulocyte colony-stimulating factor (G-CSF), IP-10, monocyte chemoattractant protein (MCP)-1, macrophage inflammatory protein (MIP)1A and tumour necrosis factor (TNF)-α are reported in a typical cytokine storm. The cytokine storm also results in neutrophils and macrophage infiltration, diffused alveolar damage and a diffused thickening of the alveolar wall. This may result into viral sepsis, pneumonitis, inflammation-induced lung injury, ARDS, respiratory failure, shock, multiple organ failure and potential death.148–151
Understanding the modus operandi of SARS-CoV-2 to utilize human machinery to invade the immune system and replicate and the response of human body to the infection is critical in drug repurposing for COVID-19.
1.6.3 Structural Information of Human Proteins in COVID-19
Crystal structures for most of the human targets involved in attachment of SARS-CoV-2 and host response to infection are already available on www.rcsb.org. Currently, there are 159 structures reported in PDB and Table 1.21 provides structural information of human targets involved in COVID-19.
Human protein structures associated with COVID-19 available in PDB
Macromolecule name . | No of structures in PDB . | Resolution range . | No of ligands . |
---|---|---|---|
ACE-2 | 14 | 2.2 to 3.31 Å | 13 |
Furin | 20 | 1.8 to 2.7 Å | 16 |
GM-CSF | 10 | 2.0 to 3.3 Å | 6 |
GRP78 | 21 | 1.6 to 2.9 Å | 18 |
IL-17 | 13 | 1.7 to 3.3 Å | 9 |
IL-2 | 22 | 1.9 to 3.8 Å | 12 |
IL-6 | 9 | 1.9 to 3.6 Å | 3 |
IL-7 | 2 | 2.7 to 2.9 Å | 0 |
IP-10 | 3 | 1.92 to 3 Å | 0 |
MCP-1 | 9 | 1.85 to 2.8 Å | 3 |
MIP1 | 9 | 1.76 to 3.1 Å | 1 |
TNF-α | 27 | 1.4 to 3.1 Å | 14 |
Macromolecule name . | No of structures in PDB . | Resolution range . | No of ligands . |
---|---|---|---|
ACE-2 | 14 | 2.2 to 3.31 Å | 13 |
Furin | 20 | 1.8 to 2.7 Å | 16 |
GM-CSF | 10 | 2.0 to 3.3 Å | 6 |
GRP78 | 21 | 1.6 to 2.9 Å | 18 |
IL-17 | 13 | 1.7 to 3.3 Å | 9 |
IL-2 | 22 | 1.9 to 3.8 Å | 12 |
IL-6 | 9 | 1.9 to 3.6 Å | 3 |
IL-7 | 2 | 2.7 to 2.9 Å | 0 |
IP-10 | 3 | 1.92 to 3 Å | 0 |
MCP-1 | 9 | 1.85 to 2.8 Å | 3 |
MIP1 | 9 | 1.76 to 3.1 Å | 1 |
TNF-α | 27 | 1.4 to 3.1 Å | 14 |
The key human targets that can be utilized for computational repurposing of drugs for COVID-19 are listed in Table 1.22.
Details of human protein structures associated with COVID-19 available in PDB
PDB . | Resolution (Å) . | Target name . | Ligand ID . | PDB . | Resolution (Å) . | Target name . | Ligand ID . |
---|---|---|---|---|---|---|---|
1R42 | 2.20 | ACE-2 | 5LQB | 1.95 | IL-2 | NARA1a | |
1R4L | 3.00 | ACE-2 | XX5 | 1M47 | 1.99 | IL-2 | |
2AJF | 2.90 | ACE-2 | SPIKEa | 1M49 | 2.00 | IL-2 | CMM |
3D0G | 2.80 | ACE-2 | SPIKEa | 1M4B | 2.15 | IL-2 | NMP |
3D0H | 3.10 | ACE-2 | SPIKEa | 1M4A | 2.18 | IL-2 | MPE |
3D0I | 2.90 | ACE-2 | SPIKEa | 1NBP | 2.20 | IL-2 | MHC |
3KBH | 3.31 | ACE-2 | SPIKEa | 2B5I | 2.30 | IL-2 | |
3SCI | 2.90 | ACE-2 | SPIKEa | 5M5E | 2.30 | IL-2 | |
3SCJ | 3.00 | ACE-2 | SPIKEa | 1M4C | 2.40 | IL-2 | |
3SCK | 3.00 | ACE-2 | SPIKEa | 3INK | 2.50 | IL-2 | |
3SCL | 3.00 | ACE-2 | SPIKEa | 1PW6 | 2.60 | IL-2 | FRB |
6LZG | 2.50 | ACE-2 | SPIKEa | 1QVN | 2.70 | IL-2 | FRI |
6M0J | 2.45 | ACE-2 | SPIKEa | 5UTZ | 2.75 | IL-2 | Fab 5111a |
6VW1 | 2.68 | ACE-2 | SPIKEa | 1PY2 | 2.80 | IL-2 | FRH |
5E84 | 2.99 | GRP78 | ATP | 1Z92 | 2.80 | IL-2 | |
5E85 | 2.57 | GRP78 | 2ERJ | 3.00 | IL-2 | ||
5E86 | 2.68 | GRP78 | 3QB1 | 3.10 | IL-2 | ||
3IUC | 2.40 | GRP78 | ADP | 6VWU | 3.40 | IL-2 | |
3LDL | 2.30 | GRP78 | ATP | 3QAZ | 3.80 | IL-2 | |
3LDN | 2.20 | GRP78 | 3DI2 | 2.70 | IL-7 | ||
3LDO | 1.95 | GRP78 | ANP | 3DI3 | 2.90 | IL-7 | |
3LDP | 2.20 | GRP78 | 3P1 | 1CSG | 2.7 | GM-CSF | |
5EVZ | 1.85 | GRP78 | ADP | 2GMF | 2.4 | GM-CSF | |
6ASY | 1.85 | GRP78 | ATP | 4NKQ | 3.3 | GM-CSF | |
6CZ1 | 1.68 | GRP78 | 3FD | 4RS1 | 2.7 | GM-CSF | |
6DFM | 2.14 | GRP78 | 3BH | 5C7X | 3.0 | GM-CSF | G1 Faba |
6DFO | 2.54 | GRP78 | GBA | 5D70 | 2.1 | GM-CSF | G1 Faba |
6DO2 | 1.70 | GRP78 | H5V | 5D71 | 2.3 | GM-CSF | G1 Faba |
6DWS | 1.90 | GRP78 | HFY | 5D72 | 2.6 | GM-CSF | G1 Faba |
5F0X | 1.60 | GRP78 | DTP | 6BFQ | 2.6 | GM-CSF | Faba |
5F1X | 1.90 | GRP78 | ATP | 6BFS | 2.0 | GM-CSF | Faba |
5F2R | 2.15 | GRP78 | ACP | 1O7Y | 3.00 | IP-10 | |
5EX5 | 1.90 | GRP78 | 7DD | 1O7Z | 1.92 | IP-10 | |
5EXW | 1.90 | GRP78 | 7DT | 1O80 | 2.00 | IP-10 | |
5EY4 | 1.86 | GRP78 | DTP | 1DOK | 1.85 | MCP-1 | |
1ALU | 1.90 | IL-6 | 3IFD | 1.90 | MCP-1 | ||
1P9M | 3.56 | IL-6 | 4R8I | 2.05 | MCP-1 | ||
4CNI | 2.20 | IL-6 | Olokizumaba | 1DOL | 2.40 | MCP-1 | |
4J4L | 2.30 | IL-6 | 2NZ1 | 2.50 | MCP-1 | ||
4NI7 | 2.40 | IL-6 | 2BDN | 2.53 | MCP-1 | 11K2a | |
4NI9 | 2.55 | IL-6 | 4ZK9 | 2.60 | MCP-1 | ||
4O9H | 2.42 | IL-6 | Camelida | 1ML0 | 2.80 | MCP-1 | M3a |
4ZS7 | 2.93 | IL-6 | Llamaa | 4DN4 | 2.80 | MCP-1 | CNTO 888a |
5FUC | 2.70 | IL-6 | 3FPU | 1.76 | MIP1 | Evasin-1a | |
4OMC | 2.30 | Furin | 2X6G | 2.18 | MIP1 | ||
4OMD | 2.70 | Furin | Phac-RVR-Amba a | 5COR | 2.55 | MIP1 | |
4RYD | 2.15 | Furin | para-guanidinomethyl-Phac-R-Tle-R-Ambaa | 4RA8 | 2.60 | MIP1 | |
4Z2A | 1.89 | Furin | 2X69 | 2.65 | MIP1 | ||
5JMO | 2.00 | Furin | Nb14a | 3KBX | 2.65 | MIP1 | |
5JXG | 1.80 | Furin | 4ZKB | 2.90 | MIP1 | ||
5JXH | 2.00 | Furin | meta-guanidinomethyl-Phac-RVR-Ambaa | 3H44 | 3.00 | MIP1 | |
5JXI | 2.00 | Furin | 5D65 | 3.10 | MIP1 | ||
5JXJ | 2.00 | Furin | meta-guanidinomethyl-Phac-RVR-Ambaa | 5UUI | 1.40 | TNF-α | MTN |
5MIM | 1.90 | Furin | 1N | 4Y6O | 1.60 | TNF-α | |
6EQV | 1.90 | Furin | Phac-Cit-Val-Arg-Ambaa | 2E7A | 1.80 | TNF-α | |
6EQW | 1.99 | Furin | 4-aminomethyl-phenylacetyl-Arg-Val-Arg-Ambaa | 4TSV | 1.80 | TNF-α | |
6EQX | 1.99 | Furin | Arg-Arg-Arg-Val-Arg-Ambaa | 5M2J | 1.90 | TNF-α | Llama VHH2a |
6HLB | 2.00 | Furin | c[succinyl-Phe-2-Nal-(Arg)4-Lys]-Arg-4-Ambaa | 2AZ5 | 2.10 | TNF-α | 307 |
6HLD | 2.10 | Furin | c[succinyl-Phe-2-Nal-(Arg)3-Lys]-Lys-4-Ambaa | 3L9J | 2.10 | TNF-α | |
6HLE | 1.99 | Furin | H-Lys-Arg-Arg-Tle-Lys-4-Ambaa | 5M2I | 2.15 | TNF-α | Llama VHH2a |
6HZA | 1.90 | Furin | c[glutaryl-Arg-Arg-Lys]-Arg-4-Ambaa | 1A8M | 2.30 | TNF-α | |
6HZB | 1.90 | Furin | c[glutaryl-Arg-Arg-Lys]-Lys-4-Ambaa | 5M2M | 2.30 | TNF-α | Llama VHH2a |
6HZC | 1.90 | Furin | c[glutaryl-BVK-Lys-Arg-Arg-Tle-Lys]-4-Ambaa | 2ZJC | 2.50 | TNF-α | |
6HZD | 1.90 | Furin | c[glutaryl-Arg-Arg-Arg-Lys]-Arg-4-Ambaa | 5TSW | 2.50 | TNF-α | |
2VXS | 2.63 | IL-17 | CAT-2200a | 6OOY | 2.50 | TNF-α | A7M |
4HR9 | 2.48 | IL-17 | 6OP0 | 2.55 | TNF-α | A7A | |
4HSA | 3.15 | IL-17 | 1TNF | 2.60 | TNF-α | ||
4QHU | 2.20 | IL-17 | Fab6785a | 4G3Y | 2.60 | TNF-α | Infliximaba |
5HHV | 2.20 | IL-17 | IL-17RAa | 6RMJ | 2.65 | TNF-α | |
5HHX | 3.00 | IL-17 | IL-17RAa | 5YOY | 2.73 | TNF-α | Golimumaba |
5HI3 | 2.15 | IL-17 | 63O | 3IT8 | 2.80 | TNF-α | |
5HI4 | 1.80 | IL-17 | 63P | 6OOZ | 2.80 | TNF-α | A6Y |
5HI5 | 1.80 | IL-17 | 63Q | 2ZPX | 2.83 | TNF-α | |
5N7W | 1.96 | IL-17 | 4TWT | 2.85 | TNF-α | M21 | |
5N92 | 2.30 | IL-17 | 5WUX | 2.90 | TNF-α | Certolizumaba | |
5NAN | 3.30 | IL-17 | IL-17RAa | 3ALQ | 3.00 | TNF-α | |
5VB9 | 1.70 | IL-17 | Peptide inhibitora | 5MU8 | 3.00 | TNF-α | JNI |
4NEJ | 1.92 | IL-2 | 2K1 | 2TUN | 3.10 | TNF-α | |
4NEM | 1.93 | IL-2 | 2JY | 3WD5 | 3.10 | TNF-α | Adalimumaba |
1M48 | 1.95 | IL-2 | FRG |
PDB . | Resolution (Å) . | Target name . | Ligand ID . | PDB . | Resolution (Å) . | Target name . | Ligand ID . |
---|---|---|---|---|---|---|---|
1R42 | 2.20 | ACE-2 | 5LQB | 1.95 | IL-2 | NARA1a | |
1R4L | 3.00 | ACE-2 | XX5 | 1M47 | 1.99 | IL-2 | |
2AJF | 2.90 | ACE-2 | SPIKEa | 1M49 | 2.00 | IL-2 | CMM |
3D0G | 2.80 | ACE-2 | SPIKEa | 1M4B | 2.15 | IL-2 | NMP |
3D0H | 3.10 | ACE-2 | SPIKEa | 1M4A | 2.18 | IL-2 | MPE |
3D0I | 2.90 | ACE-2 | SPIKEa | 1NBP | 2.20 | IL-2 | MHC |
3KBH | 3.31 | ACE-2 | SPIKEa | 2B5I | 2.30 | IL-2 | |
3SCI | 2.90 | ACE-2 | SPIKEa | 5M5E | 2.30 | IL-2 | |
3SCJ | 3.00 | ACE-2 | SPIKEa | 1M4C | 2.40 | IL-2 | |
3SCK | 3.00 | ACE-2 | SPIKEa | 3INK | 2.50 | IL-2 | |
3SCL | 3.00 | ACE-2 | SPIKEa | 1PW6 | 2.60 | IL-2 | FRB |
6LZG | 2.50 | ACE-2 | SPIKEa | 1QVN | 2.70 | IL-2 | FRI |
6M0J | 2.45 | ACE-2 | SPIKEa | 5UTZ | 2.75 | IL-2 | Fab 5111a |
6VW1 | 2.68 | ACE-2 | SPIKEa | 1PY2 | 2.80 | IL-2 | FRH |
5E84 | 2.99 | GRP78 | ATP | 1Z92 | 2.80 | IL-2 | |
5E85 | 2.57 | GRP78 | 2ERJ | 3.00 | IL-2 | ||
5E86 | 2.68 | GRP78 | 3QB1 | 3.10 | IL-2 | ||
3IUC | 2.40 | GRP78 | ADP | 6VWU | 3.40 | IL-2 | |
3LDL | 2.30 | GRP78 | ATP | 3QAZ | 3.80 | IL-2 | |
3LDN | 2.20 | GRP78 | 3DI2 | 2.70 | IL-7 | ||
3LDO | 1.95 | GRP78 | ANP | 3DI3 | 2.90 | IL-7 | |
3LDP | 2.20 | GRP78 | 3P1 | 1CSG | 2.7 | GM-CSF | |
5EVZ | 1.85 | GRP78 | ADP | 2GMF | 2.4 | GM-CSF | |
6ASY | 1.85 | GRP78 | ATP | 4NKQ | 3.3 | GM-CSF | |
6CZ1 | 1.68 | GRP78 | 3FD | 4RS1 | 2.7 | GM-CSF | |
6DFM | 2.14 | GRP78 | 3BH | 5C7X | 3.0 | GM-CSF | G1 Faba |
6DFO | 2.54 | GRP78 | GBA | 5D70 | 2.1 | GM-CSF | G1 Faba |
6DO2 | 1.70 | GRP78 | H5V | 5D71 | 2.3 | GM-CSF | G1 Faba |
6DWS | 1.90 | GRP78 | HFY | 5D72 | 2.6 | GM-CSF | G1 Faba |
5F0X | 1.60 | GRP78 | DTP | 6BFQ | 2.6 | GM-CSF | Faba |
5F1X | 1.90 | GRP78 | ATP | 6BFS | 2.0 | GM-CSF | Faba |
5F2R | 2.15 | GRP78 | ACP | 1O7Y | 3.00 | IP-10 | |
5EX5 | 1.90 | GRP78 | 7DD | 1O7Z | 1.92 | IP-10 | |
5EXW | 1.90 | GRP78 | 7DT | 1O80 | 2.00 | IP-10 | |
5EY4 | 1.86 | GRP78 | DTP | 1DOK | 1.85 | MCP-1 | |
1ALU | 1.90 | IL-6 | 3IFD | 1.90 | MCP-1 | ||
1P9M | 3.56 | IL-6 | 4R8I | 2.05 | MCP-1 | ||
4CNI | 2.20 | IL-6 | Olokizumaba | 1DOL | 2.40 | MCP-1 | |
4J4L | 2.30 | IL-6 | 2NZ1 | 2.50 | MCP-1 | ||
4NI7 | 2.40 | IL-6 | 2BDN | 2.53 | MCP-1 | 11K2a | |
4NI9 | 2.55 | IL-6 | 4ZK9 | 2.60 | MCP-1 | ||
4O9H | 2.42 | IL-6 | Camelida | 1ML0 | 2.80 | MCP-1 | M3a |
4ZS7 | 2.93 | IL-6 | Llamaa | 4DN4 | 2.80 | MCP-1 | CNTO 888a |
5FUC | 2.70 | IL-6 | 3FPU | 1.76 | MIP1 | Evasin-1a | |
4OMC | 2.30 | Furin | 2X6G | 2.18 | MIP1 | ||
4OMD | 2.70 | Furin | Phac-RVR-Amba a | 5COR | 2.55 | MIP1 | |
4RYD | 2.15 | Furin | para-guanidinomethyl-Phac-R-Tle-R-Ambaa | 4RA8 | 2.60 | MIP1 | |
4Z2A | 1.89 | Furin | 2X69 | 2.65 | MIP1 | ||
5JMO | 2.00 | Furin | Nb14a | 3KBX | 2.65 | MIP1 | |
5JXG | 1.80 | Furin | 4ZKB | 2.90 | MIP1 | ||
5JXH | 2.00 | Furin | meta-guanidinomethyl-Phac-RVR-Ambaa | 3H44 | 3.00 | MIP1 | |
5JXI | 2.00 | Furin | 5D65 | 3.10 | MIP1 | ||
5JXJ | 2.00 | Furin | meta-guanidinomethyl-Phac-RVR-Ambaa | 5UUI | 1.40 | TNF-α | MTN |
5MIM | 1.90 | Furin | 1N | 4Y6O | 1.60 | TNF-α | |
6EQV | 1.90 | Furin | Phac-Cit-Val-Arg-Ambaa | 2E7A | 1.80 | TNF-α | |
6EQW | 1.99 | Furin | 4-aminomethyl-phenylacetyl-Arg-Val-Arg-Ambaa | 4TSV | 1.80 | TNF-α | |
6EQX | 1.99 | Furin | Arg-Arg-Arg-Val-Arg-Ambaa | 5M2J | 1.90 | TNF-α | Llama VHH2a |
6HLB | 2.00 | Furin | c[succinyl-Phe-2-Nal-(Arg)4-Lys]-Arg-4-Ambaa | 2AZ5 | 2.10 | TNF-α | 307 |
6HLD | 2.10 | Furin | c[succinyl-Phe-2-Nal-(Arg)3-Lys]-Lys-4-Ambaa | 3L9J | 2.10 | TNF-α | |
6HLE | 1.99 | Furin | H-Lys-Arg-Arg-Tle-Lys-4-Ambaa | 5M2I | 2.15 | TNF-α | Llama VHH2a |
6HZA | 1.90 | Furin | c[glutaryl-Arg-Arg-Lys]-Arg-4-Ambaa | 1A8M | 2.30 | TNF-α | |
6HZB | 1.90 | Furin | c[glutaryl-Arg-Arg-Lys]-Lys-4-Ambaa | 5M2M | 2.30 | TNF-α | Llama VHH2a |
6HZC | 1.90 | Furin | c[glutaryl-BVK-Lys-Arg-Arg-Tle-Lys]-4-Ambaa | 2ZJC | 2.50 | TNF-α | |
6HZD | 1.90 | Furin | c[glutaryl-Arg-Arg-Arg-Lys]-Arg-4-Ambaa | 5TSW | 2.50 | TNF-α | |
2VXS | 2.63 | IL-17 | CAT-2200a | 6OOY | 2.50 | TNF-α | A7M |
4HR9 | 2.48 | IL-17 | 6OP0 | 2.55 | TNF-α | A7A | |
4HSA | 3.15 | IL-17 | 1TNF | 2.60 | TNF-α | ||
4QHU | 2.20 | IL-17 | Fab6785a | 4G3Y | 2.60 | TNF-α | Infliximaba |
5HHV | 2.20 | IL-17 | IL-17RAa | 6RMJ | 2.65 | TNF-α | |
5HHX | 3.00 | IL-17 | IL-17RAa | 5YOY | 2.73 | TNF-α | Golimumaba |
5HI3 | 2.15 | IL-17 | 63O | 3IT8 | 2.80 | TNF-α | |
5HI4 | 1.80 | IL-17 | 63P | 6OOZ | 2.80 | TNF-α | A6Y |
5HI5 | 1.80 | IL-17 | 63Q | 2ZPX | 2.83 | TNF-α | |
5N7W | 1.96 | IL-17 | 4TWT | 2.85 | TNF-α | M21 | |
5N92 | 2.30 | IL-17 | 5WUX | 2.90 | TNF-α | Certolizumaba | |
5NAN | 3.30 | IL-17 | IL-17RAa | 3ALQ | 3.00 | TNF-α | |
5VB9 | 1.70 | IL-17 | Peptide inhibitora | 5MU8 | 3.00 | TNF-α | JNI |
4NEJ | 1.92 | IL-2 | 2K1 | 2TUN | 3.10 | TNF-α | |
4NEM | 1.93 | IL-2 | 2JY | 3WD5 | 3.10 | TNF-α | Adalimumaba |
1M48 | 1.95 | IL-2 | FRG |
Co-crystal ligands are proteins.
1.7 Structure-based Approaches for Drug Repurposing Using Human Proteins
Computational approaches for drug repurposing are more useful in terms of shortlisting the candidates for experimental verification or proposing mechanism of action of selected drugs in disease. Some such notable attempts reported in the literature are summarized in this section.
1.7.1 Docking Studies in Angiotensin Converting Enzyme-2
ACE-2 is a key human target for repurposing of drugs, since it is involved in entry of SARS-CoV-2 in the human cells.152,153 Various in silico attempts reported for repurposing are summarized in Table 1.23.
Summary of efforts targeting ACE-2a
PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . | |
---|---|---|---|---|---|---|---|---|
1R4L | Library of 7173 ligands | Auto Dock Vina | Lividomycin, burixafor, fluprofylline, quisinostat, spirofylline, edotecarin, pemetrexed, diniprofylline | 154 | ||||
1r42, 6m0j | ACE inhibitiors | MOE | MOE | 600 ps | MM-GBSA | Ramipril, delapril, lisinopril | 155 | |
2AJF, 6VSB | LOPAC library | AutoDock Vina | GROMACS | 50 ns | GR 127935, hydrochloride hydrate, GNF-5, RS504393, TNP, eptifibatide acetate | 156 | ||
6MJ0 | 78 small molecules | AutoDock | Remdesivir, oseltamivir, zanamivir | 157 |
PDB ID . | Database used . | Docking tool . | MD program . | MD time . | Free energy . | Hits . | Reference . | |
---|---|---|---|---|---|---|---|---|
1R4L | Library of 7173 ligands | Auto Dock Vina | Lividomycin, burixafor, fluprofylline, quisinostat, spirofylline, edotecarin, pemetrexed, diniprofylline | 154 | ||||
1r42, 6m0j | ACE inhibitiors | MOE | MOE | 600 ps | MM-GBSA | Ramipril, delapril, lisinopril | 155 | |
2AJF, 6VSB | LOPAC library | AutoDock Vina | GROMACS | 50 ns | GR 127935, hydrochloride hydrate, GNF-5, RS504393, TNP, eptifibatide acetate | 156 | ||
6MJ0 | 78 small molecules | AutoDock | Remdesivir, oseltamivir, zanamivir | 157 |
Drugs in italic are reported to inhibit SARS-CoV-2 in cellular assay.
1.7.1.1 Targeting the ACE-2 Receptor
Terali et al. reported an in silico study to identify viral-entry inhibitors.154 The authors propose that the hits can stabilize the closed conformation of ACE-2, thereby modulating the interaction of ACE-2 with spike protein. This is yet to be experimentally validated.
1.7.1.2 Targeting ACE-2 and Spike Receptor
Khelfaoui et al. reported screening of 18 drugs by molecular docking as inhibitors of ACE-2 and the spike–ACE-2 interface.155 Global reactivity descriptors were used for filtering followed by the MD study. Drugs proposed in this study are already approved as ACE inhibitors and none of them is reported to inhibit ACE-2. Choudhary et al. explored the spike–ACE-2 receptor complex for repurposing drugs from the LOPAC library.156 None of the proposed drugs in either articles have been experimentally validated for their actions.
1.7.1.3 Targeting ACE-2 and Spike Receptor in Addition to Network-based Associations
Zhou et al. reported utilization of KATZ (a network-based association prediction method)-based virus–drug associations.157 The top 10 hits from the network associations were docked in spike and ACE-2 receptors to finalize the hits, which included remdesivir (RdRp inhibitor). There is no experimental evidence to conclude that remdesivir inhibits spike–ACE-2 interactions.
1.7.2 Docking Studies in Transmembrane Protease, Serine 2 (TMPRSS2)
A key molecular target transmembrane protease, serine 2 (TMPRSS2) which is evidenced to assist the entry of SARS-CoV-2 in the human cells has been investigated for drug repurposing. Details of these efforts of drug repurposing are listed in Table 1.24.
Summary of efforts targeting ACE-2
Template . | HM tool . | Similarity . | Database Used . | Docking Tool . | MD Program . | MD Time . | Free Energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|---|---|
6O1G | Swiss Model | 44% | Drugs-lib, AIEfd-Db | AutoDock Vina | 158 | ||||
2ANY, 5TJX, 6O1G, 4NA8, 5CE1, 1O5E, 3W94 | Prime | 19 serine protease inhibitors | Glide | Argatroban, otamixaban, letaxaban, darexaban, edoxaban, nafamostat | 159 | ||||
5CE1 | Swiss Model | 35.2% | 6654 small molecules | Maestro | Desmond | 10 ns | MM-GBSA | Benzquercin | 160 |
1Z8G | Swiss Model | 35.2% | SelleckChem | MOE | AMBER 18 | 100 ns | MM-GBSA | Neohesperidin, myricitrin, quercitrin, naringin, icariin, ambroxol | 161 |
Template . | HM tool . | Similarity . | Database Used . | Docking Tool . | MD Program . | MD Time . | Free Energy . | Hits . | Reference . |
---|---|---|---|---|---|---|---|---|---|
6O1G | Swiss Model | 44% | Drugs-lib, AIEfd-Db | AutoDock Vina | 158 | ||||
2ANY, 5TJX, 6O1G, 4NA8, 5CE1, 1O5E, 3W94 | Prime | 19 serine protease inhibitors | Glide | Argatroban, otamixaban, letaxaban, darexaban, edoxaban, nafamostat | 159 | ||||
5CE1 | Swiss Model | 35.2% | 6654 small molecules | Maestro | Desmond | 10 ns | MM-GBSA | Benzquercin | 160 |
1Z8G | Swiss Model | 35.2% | SelleckChem | MOE | AMBER 18 | 100 ns | MM-GBSA | Neohesperidin, myricitrin, quercitrin, naringin, icariin, ambroxol | 161 |
Two studies reported screening by only docking methods.158,159 Singh et al. reported repositioning of approved, investigational and experimental drugs inhibiting TMPRSS2.158 Based on the analysis, the authors proposed 156 hits binding to the catalytic site and 100 hits binding to the exosite. In silico drug repurposing is expected to provide shortlisted drug candidates as help to experimentalists. Providing such a huge list of drugs for any target as proposed by the authors does not help main purpose of in silico screening. Rensi et al. explored repurposing of drug candidates binding to TMPRSS2.159 Nafamostat, which emerges as a hit, is a known inhibitor of TMPRSS2, while other compounds have not been tested specifically against the target.
Furthermore, two studies involved screening by docking and MD methods.160,161 Durdaği160 proposed benzquercin as hit, which has not been tested against TMPRSS2. Chikhale et al. reported structure-based screening to find out potential drugs as TMPRSS2 inhibitors.161 None of the proposed hit drugs has been tested against TMPRSS2.
1.7.3 Docking Studies in Glucose-Regulated Protein 78 (GRP78)
Palmeira et al. reported virtual screening of 10 761 drugs (DrugBank) to identify drugs acting against NBD of GRP78 (PDB ID: 5E84) using MOE software.162 The most promising candidates identified are imatinib, selonsertib, zilucoplan, obinepitide and corticorelin ovine triflutate. These compounds have not been tested against GRP78. The involvement of this target in attachment of spike protein is a theoretical possibility as reported by the authors, but there is no evidence to date that the spike protein attaches to GRP78.
1.7.4 Docking Studies in Furin
Villoutreix et al. reported virtual screening of ∼8000 drugs from different databases using Surflex-Dock (https://www.biopharmics.com/) to identify drugs that could inhibit furin.163 Docking studies in the furin (PDB ID: 5JXH, 5MIM) resulted in identifying sulconazole as the most promising drug. Sulconazole inhibited the furin-mediated cleavage of the substrate MT1-MMP, suggesting that sulconazole inhibits furin and can be repurposed for COVID-19. This study clearly demonstrates success of in silico methods in identifying potential hits.
1.7.5 Docking Studies in ARDS Targets
1.7.5.1 TNF-α
Leung et al. reported structure-based virtual screening of 3000 US FDA-approved drugs against TNF-α (PDB ID: 2AZ5) using the ICM-Pro program (http://www.molsoft.com/).164 Darifenacin and ezetimibe were predicted to directly inhibit TNF-α. Ezetimibe is also known to reduce TNF-α expression in macrophages.165 Although this study was not specifically reported for repurposing drugs for COVID-19, it has been reported here as TNF-α inhibition can be useful for alleviating ARDS in COVID-19. A recent cohort study showed that ezetimibe reduced hospitalization risk and a promising protective effect against COVID-19.166
In summary, a few human targets have been studied computationally for repurposing drugs against SARS-CoV-2. Most of the studies reported use of AutoDock, ICM, MOE, Discovery Studio and Glide for docking. Some of the authors have performed MD simulations to validate or reaffirm the ability of ligands to bind to the potential targets. Most of the drugs which were repurposed computationally have not yet been tested for the target for which they have been repurposed or in COVID-19 patients.
1.8 Summary of Hits from Reviewed Literature
This chapter reviews approximately 130 research papers published in peer-reviewed journals which use in silico methods for drug repurposing. The hits suggested as promising in the respective papers have been assessed and comments are provided where relevant. A target-wise collection of hits identified using different in silico methods has been collated and is presented in Table 1.25. This table also provides number of papers which report suggested drug as a hit (termed as frequency in Table 1.25). The validation of hits in terms of reported experimental assays, either specific target inhibition of SARS-CoV-2 or cellular inhibition of SARS-CoV-2, has also been provided in the table.
Target-wise summary of repurposed drugs reported by in silico methodsa
Target name . | Drugs (frequency)b . | References that report hits . |
---|---|---|
mPRO | Saquinavir (14), ritonavir (12), lopinavir (11), nelfinavir (9), indinavir (8), remdesivir (7), simeprevir (7), cobicistat (5), darunavir (5), paritaprevir (5), raltegravir (5), glecaprevir (4), ribavirin (4), amprenavir (3), apixaban (3), disulfiram (3), ledipasvir (3), tipranavir (3), acarbose (2), afatinib (2), atazanavir (2), azelastine (2), azithromycin (2), betrixaban (2), birinapant (2), bromocriptine (2), cangrelor (2), carfilzomib (2), chloroquine (2), cinanserin (2), colistin (2), conivaptan (2), daclatasvir (2), demeclocycline (2), dihydroergotamine (2), dolutegravir (2), doxycycline (2), ebselen (2), edoxaban (2), ergoloid (2), ergotamine (2), grazoprevir (2), hesperidin (2), hydroxychloroquine (2), mefloquine (2), montelukast (2), octreotide (2), oxytetracycline (2), perampanel (2), rivaroxaban (2), shikonin (2), tadalafil (2), telaprevir (2), telmisartan (2), teniposide (2), tetracycline (2), tetrandrine (2), tideglusib (2), tolvaptan (2), valrubicin (2), amodiaquine (1), boceprevir (1), carprofen (1), celecoxib (1), cenicriviroc (1), emetine (1), fluvastatin (1), itraconazole (1), ivermectin (1), proglumetacin (1), sufugolix (1) | 26, 27, 29, 31–79, 107–110, 113–123, 125–132, 136–138 |
RdRp | Remdesivir (7), sofosbuvir (4), galidesivir (3), ribavirin (3), saquinavir (3), cepharanthine (2), colistin (2), dihydroergotamine (2), examorelin (2), favipiravir (2), lypressin (2), novobiocin (2), ornipressin (2), polymyxin b1 (2), simeprevir (2), tenofovir (2), hydroxychloroquine (1) | 80, 82–88, 115, 116, 120–122, 126, 127, 129 and 134 |
PLproc | Ribavirin (3), lopinavir (2), nelfinavir (2), paritaprevir (2), ritonavir (2), cefamandole (1), chloramphenicol (1), chloroquine (1), chlorphenesin (1), darolutamide (1), famotidine (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ketoamide (1), ledipasvir (1), levodropropizine (1), luteolin (1), phenformin (1), quercetin (1), remdesivir (1), simeprevir (1), thymidine (1), tigecycline (1), tipranavir (1), valganciclovir (1), ziprasidone (1) | 90, 91, 113–115, 118, 127, 129 and 131 |
N-protein | Chloroquine (1), grazoprevir (1), hydroxychloroquine (1), radotinib (1), ribavirin (1), simeprevir (1), valganciclovir (1), zidovudine (1) | 92, 93, 112 and 116 |
SPIKE | Chloroquine (2), glycyrrhizic acid (2), nilotinib (2), azithromycin (1), digitoxin (1), doxycycline (1), lopinavir (1), nelfinavir (1), posaconazole (1), remdesivir (1), ritonavir (1), saquinavir (1) | 94–98, 107–110, 115–118, 127, 131, 132, 134 and 135 |
NSP1 | Cefpiramide (1), cidofovir (1), edoxudine (1), esculin (1), lactose (1), lymecycline (1), phthalocyanine (1), piperacillin (1), remdesivir (1), streptomycin (1), tetracycline (1), tirilazad (1) | 99, 100 and 115 |
NSP13 | Atazanavir (1), bictegravir (1), canrenoic acid (1), cefsulodin (1), dabigatran (1), grazoprevir (1), itraconazole (1), lymecycline (1), meclonazepam (1), oxiphenisatin (1), paritaprevir (1), rolitetracycline (1), simeprevir (1), vapreotide (1) | 101, 115, 116, 120 and 133 |
NSP15 | Arbidol (1), bictegravir (1), dihydroergotamine (1), ergotamine (1), glisoxepide (1), idarubicin (1), nilotinib (1), remdesivir (1), tasosartan (1) | 102, 103, 115 and 116 |
NSP16 | Dolutegravir (2), saquinavir (2), bictegravir (1), cangrelor (1), digitoxin (1), dihydroergotamine (1), eriodictyol (1), favipiravir (1), fludarabine (1), irinotecan (1), maraviroc (1), montelukast (1), prednisolone (1), raltegravir (1), raltgravir (1), ritonavir (1), sinefungin (1), teniposide (1) | 104–106, 116, 117 and 130 |
NSP10/NSP16 | 2-deoxyglucose (1), daphnetin (1), methylgallate (1), oxytetracycline (1), telbivudine (1) | 125 |
E-protein | Beclabuvir (1), doxycycline (1), mycophenolic acid (1), rutin (1) | 111, 112 and 116 |
M-protein | Caffeic acid (1), ferulic acid (1) | 112 |
NSP 3 | Lopinavir (3), chloroquine (1), darunavir (1), eriodictyol (1), pemirolast (1), remdesivir (1), ritonavir (1) | 115 and 117 |
NSP7-8 | Arbidol (1), saquinavir (1) | 115 and 116 |
NSP9 | Conivaptan (1), eriodictyol (1), lopinavir (1), metaquine (1), pemirolast (1), phaitanthrin D (1), saquinavir (1), telmisartan (1) | 117, 123 and 135 |
Nsp10 | Eriodictyol (1), remdesivir (1), saquinavir (1) | 116 and 117 |
NSP14 | Arbidol (1), bictegravir (1), grazoprevir (1), montelukast (1), nilotinib (1), paritaprevir (1), saquinavir (1), simeprevir (1), sinefungin (1) | 115, 116 and 133 |
NSP15 | Lopinavir (1), mefloquine (1), metaquine (1), ritonavir (1), saquinavir (1) | 123 |
HR1 | Glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ledipasvir (1), paritaprevir (1), simeprevir (1) | 131 |
Spike–ACE-2 | Saquinavir (2), alatrofloxacin (1), batefenterol (1), dovitinib (1), hesperidine (1), lopinavir (1), metaquine (1), oxolinic acid (1), quarfloxin (1), rebamipide (1), rilapladib (1), ritonavir (1) | 115, 121 and 123 |
ACE 2 | Azithromycin (1), brincidofovir (1), burixafor (1), cefmenoxime (1), chloroquine (1), delapril (1), diniprofylline (1), edotecarin (1), eptifibatide (1), ergotamine (1), fluprofylline (1), glycyrrhizic acid (1), hesperidin (1), lisinopril (1), lividomycin (1), lopinavir (1), losartan (1), oseltamivir (1), pemetrexed (1), quisinostat (1), ramipril (1), remdesivir (1), ritonavir (1), silybin (1), spirofylline (1), troglitazone (1), zanamivir (1) | 115, 117, 128, 131, 132, 154–157 |
TMPRSS2 | Ambroxol (1), argatroban (1), benzquercin (1), cefoperazone (1), clindamycin (1), darexaban (1), edoxaban (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), hetacillin (1), icariin (1), ledipasvir (1), letaxaban (1), loprazolam (1), myricitrin (1), nafamostat (1), naringin (1), neohesperidin (1), otamixaban (1), paritaprevir (1), pivampicillin (1), quercitrin (1), rubitecan (1), simeprevir (1) | 115, 119, 131, 159–161 |
Furin | Chloroquine (1), ritonavir (1), sulconazole (1) | 129 and 163 |
Cathepsin | Azithromycin (1), chloroquine (1), hydroxychloroquine (1) | 132 |
GRP78 | Imatinib (1), obinepitide (1), selonsertib (1), zilucoplan (1), | 162 |
TNF-α | Darifenacin (1), ezetimibe (1) | 164 |
No specific target | Abemaciclib (1), adenosine (1), alectinib (1), amorolfine (1), atazanavir (1), boceprevir (1), camphor (1), carvedilol (1), cefmenoxime (1), chlorobutanol (1), chloroquine (1), colchine (1), dacomitinib (1), dactinomycin (1), didanosine (1), d-mannitol (1), dolutegravir (1), d-sorbitol (1), ebastine (1), efavirenz (1), emodine (1), enalaprilat (1), eplerenone (1), equilin (1), flutamide (1), ganciclovir (1), homoharringtonine (1), irbesertan (1), lapatinib (1), lestaurtinib (1), lifitegrast (1), meglumine (1), melatonin (1), mercaptopurine (1), mesalazine (1), nimesulide (1), oxymetholone (1), paroxetine (1) phenazopyridine (1), photofrin (1), pirenzepine (1), promazine (1), quniacrine (1), remdesivir (1), ritonavir (1), salinomycin (1), selimexor (1), sirolimus (1), sodium gluconate (1), thiabendazole (1), tilorone (1), vemurafenib (1), vidarabine (1), vitamin D (1), vorinostat (1) | 139–142, 144–146 |
Target name . | Drugs (frequency)b . | References that report hits . |
---|---|---|
mPRO | Saquinavir (14), ritonavir (12), lopinavir (11), nelfinavir (9), indinavir (8), remdesivir (7), simeprevir (7), cobicistat (5), darunavir (5), paritaprevir (5), raltegravir (5), glecaprevir (4), ribavirin (4), amprenavir (3), apixaban (3), disulfiram (3), ledipasvir (3), tipranavir (3), acarbose (2), afatinib (2), atazanavir (2), azelastine (2), azithromycin (2), betrixaban (2), birinapant (2), bromocriptine (2), cangrelor (2), carfilzomib (2), chloroquine (2), cinanserin (2), colistin (2), conivaptan (2), daclatasvir (2), demeclocycline (2), dihydroergotamine (2), dolutegravir (2), doxycycline (2), ebselen (2), edoxaban (2), ergoloid (2), ergotamine (2), grazoprevir (2), hesperidin (2), hydroxychloroquine (2), mefloquine (2), montelukast (2), octreotide (2), oxytetracycline (2), perampanel (2), rivaroxaban (2), shikonin (2), tadalafil (2), telaprevir (2), telmisartan (2), teniposide (2), tetracycline (2), tetrandrine (2), tideglusib (2), tolvaptan (2), valrubicin (2), amodiaquine (1), boceprevir (1), carprofen (1), celecoxib (1), cenicriviroc (1), emetine (1), fluvastatin (1), itraconazole (1), ivermectin (1), proglumetacin (1), sufugolix (1) | 26, 27, 29, 31–79, 107–110, 113–123, 125–132, 136–138 |
RdRp | Remdesivir (7), sofosbuvir (4), galidesivir (3), ribavirin (3), saquinavir (3), cepharanthine (2), colistin (2), dihydroergotamine (2), examorelin (2), favipiravir (2), lypressin (2), novobiocin (2), ornipressin (2), polymyxin b1 (2), simeprevir (2), tenofovir (2), hydroxychloroquine (1) | 80, 82–88, 115, 116, 120–122, 126, 127, 129 and 134 |
PLproc | Ribavirin (3), lopinavir (2), nelfinavir (2), paritaprevir (2), ritonavir (2), cefamandole (1), chloramphenicol (1), chloroquine (1), chlorphenesin (1), darolutamide (1), famotidine (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ketoamide (1), ledipasvir (1), levodropropizine (1), luteolin (1), phenformin (1), quercetin (1), remdesivir (1), simeprevir (1), thymidine (1), tigecycline (1), tipranavir (1), valganciclovir (1), ziprasidone (1) | 90, 91, 113–115, 118, 127, 129 and 131 |
N-protein | Chloroquine (1), grazoprevir (1), hydroxychloroquine (1), radotinib (1), ribavirin (1), simeprevir (1), valganciclovir (1), zidovudine (1) | 92, 93, 112 and 116 |
SPIKE | Chloroquine (2), glycyrrhizic acid (2), nilotinib (2), azithromycin (1), digitoxin (1), doxycycline (1), lopinavir (1), nelfinavir (1), posaconazole (1), remdesivir (1), ritonavir (1), saquinavir (1) | 94–98, 107–110, 115–118, 127, 131, 132, 134 and 135 |
NSP1 | Cefpiramide (1), cidofovir (1), edoxudine (1), esculin (1), lactose (1), lymecycline (1), phthalocyanine (1), piperacillin (1), remdesivir (1), streptomycin (1), tetracycline (1), tirilazad (1) | 99, 100 and 115 |
NSP13 | Atazanavir (1), bictegravir (1), canrenoic acid (1), cefsulodin (1), dabigatran (1), grazoprevir (1), itraconazole (1), lymecycline (1), meclonazepam (1), oxiphenisatin (1), paritaprevir (1), rolitetracycline (1), simeprevir (1), vapreotide (1) | 101, 115, 116, 120 and 133 |
NSP15 | Arbidol (1), bictegravir (1), dihydroergotamine (1), ergotamine (1), glisoxepide (1), idarubicin (1), nilotinib (1), remdesivir (1), tasosartan (1) | 102, 103, 115 and 116 |
NSP16 | Dolutegravir (2), saquinavir (2), bictegravir (1), cangrelor (1), digitoxin (1), dihydroergotamine (1), eriodictyol (1), favipiravir (1), fludarabine (1), irinotecan (1), maraviroc (1), montelukast (1), prednisolone (1), raltegravir (1), raltgravir (1), ritonavir (1), sinefungin (1), teniposide (1) | 104–106, 116, 117 and 130 |
NSP10/NSP16 | 2-deoxyglucose (1), daphnetin (1), methylgallate (1), oxytetracycline (1), telbivudine (1) | 125 |
E-protein | Beclabuvir (1), doxycycline (1), mycophenolic acid (1), rutin (1) | 111, 112 and 116 |
M-protein | Caffeic acid (1), ferulic acid (1) | 112 |
NSP 3 | Lopinavir (3), chloroquine (1), darunavir (1), eriodictyol (1), pemirolast (1), remdesivir (1), ritonavir (1) | 115 and 117 |
NSP7-8 | Arbidol (1), saquinavir (1) | 115 and 116 |
NSP9 | Conivaptan (1), eriodictyol (1), lopinavir (1), metaquine (1), pemirolast (1), phaitanthrin D (1), saquinavir (1), telmisartan (1) | 117, 123 and 135 |
Nsp10 | Eriodictyol (1), remdesivir (1), saquinavir (1) | 116 and 117 |
NSP14 | Arbidol (1), bictegravir (1), grazoprevir (1), montelukast (1), nilotinib (1), paritaprevir (1), saquinavir (1), simeprevir (1), sinefungin (1) | 115, 116 and 133 |
NSP15 | Lopinavir (1), mefloquine (1), metaquine (1), ritonavir (1), saquinavir (1) | 123 |
HR1 | Glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), ledipasvir (1), paritaprevir (1), simeprevir (1) | 131 |
Spike–ACE-2 | Saquinavir (2), alatrofloxacin (1), batefenterol (1), dovitinib (1), hesperidine (1), lopinavir (1), metaquine (1), oxolinic acid (1), quarfloxin (1), rebamipide (1), rilapladib (1), ritonavir (1) | 115, 121 and 123 |
ACE 2 | Azithromycin (1), brincidofovir (1), burixafor (1), cefmenoxime (1), chloroquine (1), delapril (1), diniprofylline (1), edotecarin (1), eptifibatide (1), ergotamine (1), fluprofylline (1), glycyrrhizic acid (1), hesperidin (1), lisinopril (1), lividomycin (1), lopinavir (1), losartan (1), oseltamivir (1), pemetrexed (1), quisinostat (1), ramipril (1), remdesivir (1), ritonavir (1), silybin (1), spirofylline (1), troglitazone (1), zanamivir (1) | 115, 117, 128, 131, 132, 154–157 |
TMPRSS2 | Ambroxol (1), argatroban (1), benzquercin (1), cefoperazone (1), clindamycin (1), darexaban (1), edoxaban (1), glecaprevir (1), glycyrrhizic acid (1), hesperidin (1), hetacillin (1), icariin (1), ledipasvir (1), letaxaban (1), loprazolam (1), myricitrin (1), nafamostat (1), naringin (1), neohesperidin (1), otamixaban (1), paritaprevir (1), pivampicillin (1), quercitrin (1), rubitecan (1), simeprevir (1) | 115, 119, 131, 159–161 |
Furin | Chloroquine (1), ritonavir (1), sulconazole (1) | 129 and 163 |
Cathepsin | Azithromycin (1), chloroquine (1), hydroxychloroquine (1) | 132 |
GRP78 | Imatinib (1), obinepitide (1), selonsertib (1), zilucoplan (1), | 162 |
TNF-α | Darifenacin (1), ezetimibe (1) | 164 |
No specific target | Abemaciclib (1), adenosine (1), alectinib (1), amorolfine (1), atazanavir (1), boceprevir (1), camphor (1), carvedilol (1), cefmenoxime (1), chlorobutanol (1), chloroquine (1), colchine (1), dacomitinib (1), dactinomycin (1), didanosine (1), d-mannitol (1), dolutegravir (1), d-sorbitol (1), ebastine (1), efavirenz (1), emodine (1), enalaprilat (1), eplerenone (1), equilin (1), flutamide (1), ganciclovir (1), homoharringtonine (1), irbesertan (1), lapatinib (1), lestaurtinib (1), lifitegrast (1), meglumine (1), melatonin (1), mercaptopurine (1), mesalazine (1), nimesulide (1), oxymetholone (1), paroxetine (1) phenazopyridine (1), photofrin (1), pirenzepine (1), promazine (1), quniacrine (1), remdesivir (1), ritonavir (1), salinomycin (1), selimexor (1), sirolimus (1), sodium gluconate (1), thiabendazole (1), tilorone (1), vemurafenib (1), vidarabine (1), vitamin D (1), vorinostat (1) | 139–142, 144–146 |
Drugs in bold have reported activity for the specific target; italic drugs are reported to inhibit SARS-CoV-2 in cellular assay.
Frequency indicates number of papers which recommend this drug as a promising hit. For Mpro/3CLpro, RdRp, spike and ACE-2 the drugs that have a frequency of 1 and have not been experimentally tested have not been included in the table.
No approved drugs were found to inhibit PLpro of SARS-CoV-2.89
From Table 1.25, it is also clear that there are several viral targets for which specific assays are not available. For some targets, despite availability of assays, all drugs have not been tested. The in silico results provided in this review would help experimentalists to prioritize their screening.
1.9 Concluding Remarks
Considering the emergency of SARS-CoV-2 pandemic, significant efforts for drug repurposing using in silico methods have been reported in the literature (approximately 130 published research papers as of 31 August 2020). This chapter summarizes in silico reports on viral as well as human targets associated with COVID-19 and assesses the results based on emerging experimental information. Such comparison with experimental results helps in silico researchers to improve their models and undertake research taking cognizance of new information. The main purpose of these methods is to provide clues to experimentalists in focusing their efforts for specific testing, i.e. which drugs are to be prioritized for testing in a specific target(s) or what is the mechanism of action of a repurposed drug in COVID19? In this endeavor, some reports have succeeded in identifying hits that have been subsequently verified by experiments, whereas in some mechanism of drug was correctly predicted (cf. Table 1.25). It is observed that some studies have not performed any validation of their in silico method(s) prior to screening of compound databases to propose hits. This may lead to reporting of false positives and poor quality of scientific output for drug repurposing.
It is also noted that in spite of available structural information in PDB, very few studies are reported for NSP7, NSP8, NSP9, NSP10 and NSP13. Furthermore, no studies have reported in silico screening for Orf3a, Orf7a, Orf8 and Orf9b despite the availability of their crystal structures. The number of researchers investigating SARS-CoV-2 targets for in silico drug repurposing has been significantly large (∼88%) compared to investigations on human targets involved in COVID-19. A reason for this limited effort could be the possible implications of disrupting normal human processes (e.g. ACE-2 is an important player in the renin–angiotensin system which regulates blood pressure and blood flow to multiple organs, including the lungs, heart and kidneys). The human targets such as TMPRSS2 and furin, known to be involved in direct interaction with SARS-CoV-2, should be investigated further as both the targets are also involved in human diseases such as cancer and metastasis.167–170
It is also noticed that among in silico methods, structure-based approaches have dominated the literature so far. It is expected that as more target specific experimental information is emerging, efforts using ligand-based and AI approaches will continue to grow and provide robust models for screening of different compound databases.
The promising hits emerging from drug repurposing for SARS-CoV-2 and human targets associated with COVID-19 can further be utilized for development of more active derivatives of these drugs.171,172 Thus, even though some of the drugs have not shown excellent activities against specific targets, they can be used as leads for further new chemical entity development. The Nobel Prize-winning pharmacologist Sir James Black, said, “The most fruitful basis for the discovery of a new drug is to start with an old drug”.
1.10 Executive Summary
Drug repurposing is a way to identify treatment in pandemic such as SARS-CoV-2.
This chapter reviews drug repurposing effort for COVID-19 using in silico methods.
The review includes in silico work using structure-based, ligand-based and artificial intelligence/network-based methods on both SARS-CoV-2 and human targets.
Significant number of papers have been published on SARS-CoV-2 targets (∼88%) compared to human targets.
The papers using structure-based methods dominated over ligand-based and other methods.
The promising list of drugs for repurposing was provided through in silico methods for experimental testing and multiple papers have identified same drugs as promising hits.
Many predictions and hits provided by in silico methods have matched well with the reported experiments.
In silico methods have potential to provide drug candidates for experimental screening against specified targets or provide clues on mechanistic aspects of drugs.
Author Contributions
Kundan Ingale contributed Sections 1.5–1.8. The remaining sections were contributed by Sudhir Kulkarni.
Authors acknowledge help from several NovaLead Pharma team members, with special thanks to Amol Charya for providing figures.