|Year : 2020 | Volume
| Issue : 12 | Page : 563-568
Teicoplanin is a potential inhibitor of SARS CoV-2 replication enzymes: A docking study
Aatika Sadia, Muhammad Azam, Muhammad Asim Raza Basra
Institute of Chemistry, University of the Punjab, New Campus, Lahore-54590, Pakistan
|Date of Submission||19-Apr-2020|
|Date of Decision||15-May-2020|
|Date of Acceptance||20-Jun-2020|
|Date of Web Publication||19-Oct-2020|
Muhammad Asim Raza Basra
Institute of Chemistry, University of the Punjab, New Campus, Lahore-54590
Source of Support: None, Conflict of Interest: None
Objective: To explore potential inhibitors of viral enzymes of SARS CoV-2.
Methods: The in-silico docked potential of anti-viral, antibiotic, and analgesic drugs were studied for inhibition of the nonstructural protein (NSP) 9, NSP3, and NSP15 of SARS CoV-2 using recent structural peculiarities of these enzymes, 3D optimized structures of drugs and algorithm-based ligand inhibitory potential.
Results: Teicoplanin, azithromycin, and remdesivir potentially inhibited NSP9 (Dock-score 9 620, 5 472 and 6 252, respectively), NSP3 (Dock-score 9 846, 5 604 and 5 548, respectively) and NSP15 (Dock-score 10 960, 6414 and 6 002, respectively).
Conclusions: Teicoplanin acts as a significant receptor antagonist and potentially inhibits the SARS CoV-2 enzymes.
Keywords: Molecular docking; Oxicam; Antibiotics; Antiviral; SARS CoV-2
|How to cite this article:|
Sadia A, Azam M, Basra MA. Teicoplanin is a potential inhibitor of SARS CoV-2 replication enzymes: A docking study. Asian Pac J Trop Biomed 2020;10:563-8
|How to cite this URL:|
Sadia A, Azam M, Basra MA. Teicoplanin is a potential inhibitor of SARS CoV-2 replication enzymes: A docking study. Asian Pac J Trop Biomed [serial online] 2020 [cited 2020 Nov 30];10:563-8. Available from: https://www.apjtb.org/text.asp?2020/10/12/563/294093
| 1. Introduction|| |
The viruses of genus Betacoronavirus have been known to cause zoonotic respiratory infections such as Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome in human beings. A new and highly contagious respiratory infection Coronavirus Disease- 2019 (COVID-19) erupted from novel SARS CoV-2 in December 2019 and became pandemic across the globe. SARS CoV-2 is a positive sense, single stranded RNA enveloped virus. Apart from spike, envelope, nucleocapsid, and membrane structural proteins, the nonstructural proteins (NSPs) of SARS CoV-2 mediate COVID-19 pathogenesis. NSP9-replicase and NSP3-ADP-ribose-phosphatase catalyze viral genome replication through restriction-transcription- complex . NSP15-endoribonuclease triggers cytokine storm featuring CD 4+-CD 6+ T-cells interleukins (IL-1β, IL-4, IL-10), monocyte chemoattractant protein-1, and interferon-γ. As NSP14-exonuclease proofreads the synthesized viral RNA during pathogenesis, the drugs that function through nucleotide analog therapy fail to alleviate COVID-19. Presently, binding of various drugs was studied against NSP9, NSP3, and NSP15 of SARS CoV-2, illuminating receptor analog mechanism as a promising solution.
| 2. Materials and methods|| |
2.1. Selection and structural optimization of ligands
The commonly available non-steroidal antiinflammatory drugs (NSAIDs) ampiroxicam (AMX), meloxicam (MEX), droxicam (DRX), isoxicam (ISX), lornoxicam (LOX), piroxicam (PIX) and tenoxicam (TNX) were selected due to their plausible inhibitory potential against intruding viroids in the host cells as reported previously while the antibiotic drugs including teicoplanin (TPN), azithromycin (AZN), cefixime (CFM), antiviral drugs ribavirin (RBV), favipiravir (FVR), remdesivir (RVR), along with antimalarial drugs chloroquine (CQ) and hydroxychloroquine (HCQ) were selected for computational study due to promising results reported for COVID-19 treatment in the clinical trials of these drugs . The 3D structures of drugs to be used for docking were downloaded from PubChem and optimized structures were generated by using Avogadro software [Table 1]. All these structures were subjected to optimization by Avogadro software for accurate docking .
2.2. Binding pockets determination
The NSPs including NSP9-replicase RNA binding protein, NSP3- ADP-ribose-phosphatase, and NSP15-endoribonuclease (PDB ids: 6W4B, 6W02, and 6VWW) were selected for docking and were obtained from RCSB PDB in the PDB formats. The structures were subjected to binding pocket determination by Deepsite- PlayMolecule .
2.3. Protein-inhibitor molecular docking
The molecular docking of selected proteins was performed by PatchDock Beta 1.3 a molecular docking algorithm that is based on shape complementarity principles . The outcomes were carefully selected according to the binding pockets of proteins. The docking scores, approximate interface area of protein-ligand complex, and atomic contact energy (ACE) values were recorded for each proposed inhibitor ligand. The docking mode was selected as enzyme-inhibition and the root mean square deviations for atomic positions were 1.5. The outcomes were visualized using UCSF Chimera 1.14.
| 3. Results|| |
3.1. Ligand optimizations
Structures of all the drugs were optimized to minimum energy such that differential energy and constraints of the structures became zero [Figure 1]A, [Figure 1]B, [Figure 1]C, [Figure 1]D, [Figure 1]E, [Figure 1]F, [Figure 1]G, [Figure 1]H, [Figure 1]I, [Figure 1]J, [Figure 1]K, [Figure 1]L, [Figure 1]M, [Figure 1]N, [Figure 1]O.
|Figure 1: Optimized 3D structures of selected drugs. (A) Ampiroxicam, (B) droxicam, (C) isoxicam, (D) lornoxicam, (E) meloxicam, (F) piroxicam, (G) tenoxicam, (H) cefixime, (I) chloroquine, (J) hydroxychloroquine, (K) ribavirin, (L) teicoplanin, (M) remdesivir, (N) azithromycin and (O) favipiravir.|
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3.2. Binding pockets determination
The NSP9-replicase was found to contain potential binding pocket lined by amino acids Arg40A, Phe41A, Val42A, Lys59A, Thr68A, Glu71A, Pro72A, Cys74A, Tyr90A, Phe91A, Gly101A, Val103A, and Leu104A at its margins [Figure 2]A, [Figure 2]B. Amino acids Ser5A, Phe6A, Ser7A, Gly8A, Tyr9A, Tyr9B, Leu10B, Lys11B, Leu12B, Thr13B, Tyr17B, Ile18B, Lys19B, Val165B, Ser166B, and Phe168B resided around the margins of the binding pocket in NSP3-ADP- ribose-phosphatase [Figure 2]C, [Figure 2]D. The potential binding pocket for NSP15-endoribonuclease exhibited amino acids Thr48A, Thr49A, Leu50A, Met105A, Thr106A, Ile97A, Ser98A, Asp22B, Ile23B, His45B, Thr49B, Leu50B, Pro51B, Ser98B, Asp157B, Met105B, and Thr106A at its margins [Figure 2]E, [Figure 2]F.
|Figure 2: 3D structures and binding pockets of SARS CoV-2 proteins. (A) 3D structure of NSP9 replicase, (B) Binding pockets of NSP9 replicase, (C) 3D structure of NSP3 ADP ribose phosphatase, (D) Binding pockets of Nsp3 ADP ribose phosphatase, (E) 3D structure of NSP15 endoribonuclease, and (F) Binding pockets NSP15 endoribonuclease.|
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3.3. Protein-inhibitor molecular docking
The docking results were selected based on the efficient interaction of drugs with the amino acids lining the binding pockets of the proteins. Although all the drugs showed interaction with NSP9- replicase of SARS CoV-2, TPN showed the most significant inhibition, with the highest dock score of 9 620 and the lowest ACE values of -547.52 Kcal/mol. The ligand completely obstructed the binding pocket and exhibited hydrogen bond of 0.4 Å relaxed constraints with Thr36B ([Figure 3]A). Prominent docking scores of 6 252 and 5 472 were shown by RVR and AZN, respectively against NSP9-replicase. The order of docking score of selected medicinal compounds with inhibition against NSP9-replicase was as follows: TPN > RVR > AZN > HCQ > CQ > DRX > CFM > MEX > AMX > ISX > TNX > PIX > LOX > RBV > FVR [Table 2].
|Figure 3: Docking studies of teicoplanin. (A) NSP9 replicase docked with teicoplanin, (B) NSP3 ADP ribose phosphatase docked with teicoplanin and (C) NSP15 endoribonuclease docked with teicoplanin.|
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|Table 2: Docking scores, area of interface, and atomic contact energy values of selected drugs.|
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NSP3-ADP-ribose-phosphatase of SARS CoV-2 also showed interactions with all selected drugs. TPN showed the highest binding potentials followed by AZN and RVR with docking scores of 9 846, 5 604, and 5 548 and ACE values of -220.30, -132.28, and -116.49 Kcal/mol, respectively. However, the lowest ACE value of -291.10 was shown by AMX with a docking score of 4 640 [Table 2]. The TPN showed hydrogen bonds of 0.4 Å relaxed constraints with Val147A, Thr149A, Asp 14A, Asn15A, Thr33B and covered the binding pocket completely for its inhibition [Figure 3]B. The order of the docking scores of compounds with the inhibition against NSP3-ADP-ribose-phosphatase was as follows: TPN > AZN > RVR > AMX > MEX > CQ > HCQ > LOX > DRX >CFM > PIX > TNX > ISX > RBV > FVR [Table 2].
The NSP15-endoribonuclease of SARS CoV-2 also interacted with all selected drugs. TPN showed the highest docking score and lowest ACE value as 10 960 and -583.02 Kcal/mol, respectively. Amino acids Gly77A, Thr121A, and Lys110B on the surface of NSP15-endoribonuclease pocket exhibited hydrogen bonds of 0.4 Å relaxed constraints with TPN molecule such that its binding pocket was completely obstructed [Figure 3]C. The order of docking score of selected medicinal compounds with inhibition against NPS15- endoribonuclease was as follows: TPN > AZN > RVR > AMX > CFM > CQ > MEX > ISX > LOX > DRX > HCQ > PIX > TNX > RBV > FVR [Table 2].
| 4. Discussion|| |
Various drugs are under consideration for the treatment of COVID- 19. The mild clinical features of COVID-19 include fever, sore throat, cough, and viremia. It may aggravate pneumonia and can be fatal in severe cases. NSAIDs are required to treat the fever and pleuritis, and at least one of the NSAIDs is included in the prescribed medicines to cure the infectious disease. PIX has been used to inhibit the viral pathogenesis of herpes , while MEX is known for its antiproliferative and cytotoxic activity . Likewise, MEX has been used in the treatment of COVID-19 along with HCQ and showed viral clearance. Hence these NSAIDs including AMX, MEX, DRX, ISX, LOX, PIX, and TNX might be considered for in- vitro and in-vivo evaluation and further clinical trials in COVID-19 treatment. The antimalarial drugs CQ and its less toxic derivative HCQ, antibiotics TPN, AZN and CFM, and antiviral RBV have been earlier reported for antiviral effects against SARS CoV and MERS CoV, while FVR has been reported to treat Ebola and influenza. The drugs RVR and TPN promisingly inhibit SARS CoV-2 in vitro while CQ, HCQ, AZN, RBV, and FVR have also been reported to show varying degrees of SARS CoV-2 inhibition. RVR and all these mentioned drugs showed viral clearance and potential in treatment of COVID-19.
In the computational drug discovery, the high docking scores and highly negative ACE values indicate the greater affinity of the ligand with protein and higher potential to inhibit the protein from its functioning. The inhibition of HCQ and CQ against NSP9- replicase supports the effectiveness of these drugs in treating the COVID-19. In the same way, CFM has also been recommended based on in-silico molecular docking against another SARS CoV-2 enzyme protease . Our comparative findings suggest that the viral replication proteins of SARS CoV-2 are more efficiently inhibited by TPN as compared with other drugs, which is similar to previously reported therapeutic strategies. The half-life of TPN is much shorter (3-5 days) as compared with CQ and HCQ (45-55 and 22 days, respectively). The antibiotic AZN and antiviral RVR also show promising results. In addition to the inhibition of viral enzymes and NSPs, the suppression of proinflammatory cytokines is also required. Hence, the analgesics AMX and MEX should be considered for the treatment of COVID-19.
Our study depicted complete blockade of the binding sites of NSP3, NSP9 and NSP15 of SARS CoV-2 by TPN, AZN and RVR through prominent receptor antagonism. Inhibiting replication of the viral genome and suppression of the cytokine storm may aid the COVID- 19 patients to effectively combat the disease. In addition to their regular analgesic and antibiotic roles, these prescribed drugs could also be potent inhibitors of viral proteins of SARS CoV-2.
Conflict of interest statement
We declare that there is no conflict of interest.
This work was supported by Institute of Chemistry, University of the Punjab Lahore, Pakistan.
AS is in charge of experimental studies, manuscript preparation; MA in charge of data analysis and interpretation; MARB in charge of concept, design, manuscript editing, and review.
| References|| |
Jing H, Song T, Cao S, Sun Y, Wang J, Dong W, et al.
Nucleotide-binding oligomerization domain-like receptor X1 restricts porcine reproductive and respiratory syndrome virus-2 replication by interacting with viral Nsp9. Virus Res
Hackbart M, Deng X, Baker SC. Coronavirus endoribonuclease targets viral polyuridine sequences to evade activating host sensors. Proc Natl Acad Sci USA
Astani A, Albrecht U, Schnitzler P. Piroxicam inhibits herpes simplex virus type 1 infection in vitro. Pharmazie
Sanders JM, Monogue ML, Jodlowski TZ, Cutrell JB. Pharmacologic treatments for coronavirus disease 2019 (COVID-19): A review. JAMA
Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. J Cheminformatics
Jiménez J, Doerr S, Martínez-Rosell G, Rose AS, De Fabritiis G. DeepSite: Protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al.
UCSF Chimera--A visualization system for exploratory research and analysis. J Comput Chem
Culita DC, Alexandrova R, Dyakova L, Marinescu G, Patron L, Kalfin R, et al.
Evaluation of cytotoxic and antiproliferative activity of Co (II), Ni ( Π ), Cu ( Π ) and Zn ( Π ) complexes with meloxicam on virus- transformed tumor cells. Cell
Ohe M, Shida H, Jodo S, Kusunoki Y, Seki M, Furuya K, et al.
Macrolide treatment for COVID-19: Will this be the way forward? Biosci Trends
2020. Doi: 10.5582/bst.2020.03058. [online ahead of print].
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]