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. 2020 Jun 1:1–9. doi: 10.1080/07391102.2020.1770127

Design of multi-epitope vaccine candidate against SARS-CoV-2: a in-silico study

K Abraham Peele 1, T Srihansa 1, S Krupanidhi 1, Vijaya Sai Ayyagari 1, T C Venkateswarulu 1,✉
PMCID: PMC7284139  PMID: 32419646

Abstract

The best therapeutic strategy to find an effective vaccine against SARS-CoV-2 is to explore the target structural protein. In the present study, a novel multi-epitope vaccine is designed using in silico tools that potentially trigger both CD4 and CD8 T-cell immune responses against the novel Coronavirus. The vaccine candidate was designed using B and T-cell epitopes that can act as an immunogen and elicits immune response in the host system. NCBI was used for the retrieval of surface spike glycoprotein, of novel corona virus (SARS-CoV-2) strains. VaxiJen server screens the most important immunogen of all the proteins and IEDB server gives the prediction and analysis of B and T cell epitopes. Final vaccine construct was designed in silico composed of 425 amino acids including the 50S ribosomal protein adjuvant and the construct was computationally validated in terms of antigenicity, allergenicity and stability on considering all critical parameters into consideration. The results subjected to the modeling and docking studies of vaccine were validated. Molecular docking study revealed the protein-protein binding interactions between the vaccine construct and TLR-3 immune receptor. The MD simulations confirmed stability of the binding pose. The immune simulation results showed significant response for immune cells. The findings of the study confirmed that the final vaccine construct of chimeric peptide could able to enhance the immune response against nCoV-19.

Keywords: COVID-19, vaccine design, immuno-informatics, Vaxigen, IEDB, spike glycoprotein

1. Introduction

COVID-19 pandemic is result of the infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and it attacks the vital organs of body and targets pneumocytes in lungs which leading to fatal respiratory distress (Elfiky, 2020; Galante et al., 2016; Joshi et al., 2020; Tse et al., 2004; Yi et al., 2020). SARS, MERS, and SARS-CoV-2 caused diseases are characterized by a lower respiratory ailment like bronchitis, bronchiolitis, and pneumonia (Bogoch et al., 2020; Elfiky & Azzam, 2020). Coronaviruses are enveloped, single stranded RNA viruses which bears club shaped glycoproteins on their surface (Prajapat et al., 2020; Sarma et al., 2020). Corona virus is contagious and spreads through inhalation, ingestion of viral droplets resulting from coughing and sneezing. The coronavirus genome is comprised of ∼30000 nucleotides and it encodes with four structural proteins, Nucleocapsid (N) protein, Membrane (M) protein, Spike (S) protein and Envelop (E) protein and several non-structural proteins (nsp) (Boopathi et al., 2020; Gupta et al., 2020; Hasan et al., 2020; Khan et al., 2020). The non-structural protein 16 (nsp16) or 2′-OMTase is the crucial protein responsible for viral replication and expression in host cells (Khan et al., 2020). The SARS-CoV-2 has a surface spike like glycoprotein (S-Spike), it binds specifically to angiotensin-converting enzyme 2 (ACE2) to access host type-2 pneumocyte cells that could bind to human cells (Hoffmann et al., 2020; Verdecchia et al., 2020). The enveloped virus covered with spike surface glycoprotein and after binding the host cell is engulfed in to the cell and releases positive sense single stranded RNA in to type-2 pneumocyte. RNA dependent RNA polymerases and proteinases makeup the components of viral proteins like nucleocapsid spike proteins and enzymes necessary for viral replication and damage to host cells. Type-2 pneumocytes that produces a surfactant molecule and decreases surface tension in the alveoli and reduce the collapsing pressure (Belouzard et al., 2012; Weiss & Navas-Martin, 2005). However, at present researchers are aiming to uncover this spike protein processing proteases for early drug development using various approved inhibitor drugs/other compounds (Aanouz et al., 2020; Elmezayen et al., 2020; Enmozhi et al., 2020; Islam et al., 2020; Muralidharan et al., 2020; Pant et al., 2020; Sinha et al., 2020; Wahedi et al., 2020). The viral proteins encounters immune cells and release specific cytokines leads to vasodilation, capillary permeability, aleveolar edema and finally increase the collapsing pressure to burst out the pneumocyte (van de Veerdonk et al., 2020). SARS-CoV-2 surface spike (S) protein contains two subunits S1 and S2, of which S1 is sole responsible for host cell receptor and S2 harbors the membrane fusion machinery. Spike protein from SARS-CoV-2 shares the high structural similarity with SARS-CoV spike (Weiss & Navas-Martin, 2005). The vaccine development against SARS-CoV-2 spike protein is an important approach and hence, the present study is focused on epitope prediction analysis for construction of vaccine candidate by computational methods.

2. Materials and methods

2.1. CTL epitope prediction

The complete amino acid sequence of spike glycoprotein (NCBI Accession id = “QHD43416.1) of SARS-CoV-2 strain was retrieved from NCBI. Cytotoxic T-cell lymphocyte (CTL) epitopes were predicted for spike glycoprotein using artificial neural network algorithm based online server NetCTL 1.2 which predicts the MHC-class I binding, and then followed by submitting predicted NetCTL generated results to VaxiJen v2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html), ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) servers to predict protective nontoxic antigens (Larsen et al., 2007). After the screening of epitopes with VaxiJen v2.0 and ToxinPred servers, the resultant epitopes were subjected to immunogenicity prediction using IEDB server (https://www.iedb.org/).

2.2. HTL and linear epitope prediction

IEDB server was used to predict the MHC-II restricted epitopes as it uses special patterns for HLA-DRB1*01:01, HLA-DPA1*01/DPB1*04:01, HLA-DQA1*03:01/DQB1*03:02 alleles, further, T-helper 1-type immune response activation and IFN-γ production was predicted using IFNepitope server (http://crdd.osdd.net/raghava/ifnepitope/). Toxicity was predicted using ToxinPred server (http://crdd.osdd.net/raghava/toxinpred/) and BCPRED 2.0 online server was used to predict the linear B-cell epitopes of spike protein (http://crdd.osdd.net/raghava/bcepred/). The results of linear B-cell epitopes and HTL epitopes of overlapping regions were assembled and considered as final predicted epitopes.

2.3. Construction of multi-epitope vaccine sequence

The potential non toxic and probable antigenic vaccine was constructed using selected epitopes. The linear B-cell and HTL epitopes were joined with GPGPG linker peptides and CTL epitopes were joined using AAY linker. The N- terminal position of the vaccine construct was linked with the sequence of 125 amino acid residue 50S ribosomal L7/L12 peptide which acts as an adjuvant and C-terminal portion was linked with HHHHHH (6HIS) linker. The basic property of allergenicity was assessed using an online server, AllerTOP v. 2.0 (https://www.ddg-pharmfac.net/AllerTOP/) and then submitted to VaxiJen server to find out whether the construct vaccine could be a probable antigen to elicit immune response.

2.4. Homology modeling and molecular docking

PSIPRED web tool was employed for finding the secondary structure analysis (http://bioinf.cs.ucl.ac.uk/psipred/). Homology modeling of the vaccine protein tertiary structure was performed using fully automated protein modeling I-TASSER server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) and the best model was selected and then optimized using SPDB viewer (https://spdbv.vital-it.ch/). Loop refinement was done using ModLoop (https://modbase.compbio.ucsf.edu/modloop/). Ramachandran plot and ERRAT server (https://servicesn.mbi.ucla.edu/ERRAT/) analysis were performed for further validation study. The designed vaccine candidate was subjected to docking using GRAMM-X Simulation web server (http://vakser.compbio.ku.edu/resources/gramm/grammx/) for docking vaccine protein model with TLR-3 (PDB ID: 1ziw) and interaction were visualized using LIGPLUS 1.2 software (https://www.ebi.ac.uk/thornton-srv/software/LIGPLOT/).

2.5. Molecular dynamic simulations

The protein & receptor complex was subjected to MD simulations. The MD simulations were done by GROMACS 2018 package to carry out 20 ns simulations using OPLS force field. The TIP3P water model was selected for solvating complexes followed by the addition of ions to neutralize. Periodic boundary conditions were used and Equilibration of the system was done using NVT and NPT ensemble for 100 ps. The trajectory was set to be generated every 2 fs and save every 2 ps. The protein-protein complex result was then analyzed (Enayatkhani et al., 2020).

2.6. Immune simulation

The immune response profile of vaccine construct was recorded by in silco method C-ImmSim, online simulation server (http://150.146.2.1/C-IMMSIM/index.php). The C-ImmSim model describes both humoral and cellular response of a mammalian immune system against vaccine construct. The target product profile of a prophylactic vaccine, three injections were given at different intervals of four weeks. The time step of simulation corresponds few hours of real life and simulation was performed with default parameters. The sequence of injections is Ag1, Ag2, Ag3 were administered four weeks apart. The simulation volume and simulation steps were set at 1000, (random seed = 12345 with an injection of vaccine containing no LPS.

2.7. Analysis of vaccine construct and in silico cloning

The physiochemical properties of the vaccine construct was assessed using online web tool the ProtParam (https://web.expasy.org/protparam/), where as the solubility, allergenicity and probable antigenic prediction were performed using protsol, AllerTOP v. 2.0 and VaxiJen v2.0 online web servers. J-CAT tool (http://www.jcat.de/) is used for codon optimization of vaccine construct and E.coli (K12) strain is selected as source organism. SnapGene software (https://www.snapgene.com/try-snapgene/) was used for in silico cloning of vaccine construct in to pET-28a vector.

3. Results and discussion

The amino acid sequence was used to predict the possible probable antigenic epitopes of linear B-cell, HTL and CTL epitopes for designing the multi-epitope vaccine. The vaccine construct consisted of 425 amino acid residues derived from different peptide sequences. CTL epitopes of 9-mer lengths were predicted using NetCTL1.2 (Table 1). Based on high binding affinity score, the results were submitted to VaxiJen v2.0 and predicted the 16 protective probable antigens. The non antigenic epitopes were removed and subjected to predict the toxicity using ToxinPred and after removing two toxin epitopes 14 non-allergenic epitopes were selected using toxinpred and, the IEDB immunogenicity server produced the results of seven epitopes and were given in the Table 2. The predicted probable antigenic HTL epitopes were selected for further screening of toxigenicity prediction using vaxigen 2.0 server and 13 HTL epitopes were selected and further classified as non-toxins using Toxinpred server (Tables 3 and 4). The final HTL epitopes were selected as a result of IFN-γ inducing epitopes (Table 5). The linear B-cell epitopes were used in vaccine construct as overlapping B-cell and T-cell epitopes.

Table 1.

List of predicted T-cell epitopes on the basis of C-terminal cleavage and TAP scores.

Residue No Peptide Sequence MHC binding affinity Rescale binding affinity c-terminal cleavage affinity Transport affinity Prediction score
865 LTDEMIAQY 0.7953 3.3768 0.9723 2.7790 3.6616
258 WTAGAAAYY 0.6735 2.8596 0.7339 2.8630 3.1128
604 TSNQVAVLY 0.6559 2.7847 0.9440 2.9910 3.0758
361 CVADYSVLY 0.5348 2.2705 0.9764 3.1800 2.5759
733 KTSVDCTMY 0.4908 2.0840 0.9649 3.0160 2.3795
746 STECSNLLL 0.5136 2.1808 0.8879 0.7030 2.3492
652 GAEHVNNSY 0.4042 1.7163 0.9769 2.6630 1.9960
196 NIDGYFKIY 0.3921 1.6649 0.9664 3.0150 1.9606
160 YSSANNCTF 0.3975 1.6878 0.9032 2.5980 1.9531
152 WMESEFRVY 0.3902 1.6569 0.7993 2.9290 1.9232
162 SANNCTFEY 0.3737 1.5865 0.9196 2.9900 1.8739
687 VASQSIIAY 0.3529 1.4986 0.9656 3.0890 1.7978
30 NSFTRGVYY 0.3389 1.4389 0.6421 3.1240 1.6915
136 CNDPFLGVY 0.2613 1.1095 0.6900 2.4500 1.3355
392 FTNVYADSF 0.2704 1.1480 0.3800 2.3170 1.3208
261 GAAAYYVGY 0.2253 0.9568 0.7608 2.9690 1.2194
357 RISNCVADY 0.2106 0.8941 0.9292 3.3940 1.2032
465 ERDISTEIY 0.2097 0.8903 0.9744 2.6460 1.1687
285 ITDAVDCAL 0.2350 0.9979 0.8708 0.7900 1.1680
1039 RVDFCGKGY 0.2036 0.8644 0.7618 3.2320 1.1403
343 NATRFASVY 0.1955 0.8300 0.9342 2.8730 1.1138
1237 MTSCCSCLK 0.2260 0.9595 0.7525 0.4790 1.0963
50 STQDLFLPF 0.1974 0.8383 0.5530 2.5110 1.0468
1096 VSNGTHWFV 0.2012 0.8544 0.6143 0.2180 0.9574
880 GTITSGWTF 0.1656 0.7031 0.7489 2.5570 0.9433
815 RSFIEDLLF 0.1421 0.6035 0.5938 3.0320 0.8441
1264 VLKGVKLHY 0.1262 0.5356 0.9783 2.8590 0.8253
748 ECSNLLLQY 0.1413 0.6000 0.5316 2.7470 0.8171
370 NSASFSTFK 0.1671 0.7093 0.5456 0.5070 0.8165
372 ASFSTFKCY 0.1180 0.5010 0.9587 3.2750 0.8085
628 QLTPTWRVY 0.1189 0.5047 0.9661 2.7820 0.7887
296 LSETKCTLK 0.1515 0.6432 0.8919 0.2200 0.7879
192 FVFKNIDGY 0.1358 0.5767 0.4093 2.9130 0.7837
445 VGGNYNYLY 0.1164 0.4941 0.9518 2.6580 0.7698
83 VLPFNDGVY 0.1130 0.4797 0.9703 2.8460 0.7675
1095 FVSNGTHWF 0.1232 0.5231 0.7203 2.6210 0.7622
612 YQDVNCTEV 0.1531 0.6501 0.5870 0.2420 0.7502
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Table 2.

List of seven predicted T-cell epitopes predicted after IEDB immunogenicity prediction server screen.

S.No. Peptide Length Score
1. QLTPTWRVY 9 0.31555
2. VLPFNDGVY 9 0.1815
3. WTAGAAAYY 9 0.15259
4. CNDPFLGVY 9 0.15232
5. GAAAYYVGY 9 0.09963
6. ITDAVDCAL 9 0.08501
7. STQDLFLPF 9 0.06828
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Table 3.

Helper T-Cell (HTL) epitopes for spike glycoprotein using IEDB MHC-II module.

Allele Start End Length Peptide VaxiJen probability (threshold 0.4)
HLA-DRB1*01:01 513 527 15 LSFELLHAPATVCGP Antigen
HLA-DRB1*01:01 512 526 15 VLSFELLHAPATVCG Antigen
HLA-DRB1*01:01 511 525 15 VVLSFELLHAPATVC Antigen
HLA-DRB1*01:01 514 528 15 SFELLHAPATVCGPK Non-antigen
HLA-DRB1*01:01 510 524 15 VVVLSFELLHAPATV Antigen
HLA-DRB1*01:01 509 523 15 RVVVLSFELLHAPAT Antigen
HLA-DPA1*01/DPB1*04:01 504 518 15 GYQPYRVVVLSFELL Antigen
HLA-DPA1*01/DPB1*04:01 507 521 15 PYRVVVLSFELLHAP Antigen
HLA-DPA1*01/DPB1*04:01 506 520 15 QPYRVVVLSFELLHA Antigen
HLA-DPA1*01/DPB1*04:01 505 519 15 YQPYRVVVLSFELLH Antigen
HLA-DPA1*01/DPB1*04:01 508 522 15 YRVVVLSFELLHAPA Antigen
HLA-QA1*03:01/DQB1*03:02 761 775 15 TQLNRALTGIAVEQD Antigen
HLA-QA1*03:01/DQB1*03:02 762 776 15 QLNRALTGIAVEQDK Antigen
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Table 4.

Helper T-Cell epitopes for spike glycoprotein using Toxinpred server.

Peptide sequence SVM score Prediction Hydrophobicity Hydropathicity Hydrophilicity Charge Mol.wt
LSFELLHAPATVCGP −1.10 Non-Toxin 0.12 0.85 −0.60 −0.50 1555.04
VLSFELLHAPATVCG −1.31 Non-Toxin 0.16 1.23 −0.70 −0.50 1557.06
VVLSFELLHAPATVC −1.39 Non-Toxin 0.19 1.54 −0.80 −0.50 1599.14
VVVLSFELLHAPATV −1.49 Non-Toxin 0.22 1.65 −0.83 −0.50 1595.14
FVFLVLLPLVSSQCV −1.06 Non-Toxin 0.28 2.23 −1.23 0.00 1664.31
RVVVLSFELLHAPAT −1.58 Non-Toxin 0.07 1.07 −0.53 0.50 1652.19
GYQPYRVVVLSFELL −1.40 Non-Toxin 0.04 0.66 −0.70 0.00 1783.33
PYRVVVLSFELLHAP −1.50 Non-Toxin 0.06 0.81 −0.63 0.50 1740.30
QPYRVVVLSFELLHA −1.65 Non-Toxin 0.02 0.68 −0.61 0.50 1771.32
YQPYRVVVLSFELLH −1.53 Non-Toxin 0.00 0.47 −0.73 0.50 1863.42
YRVVVLSFELLHAPA −1.63 Non-Toxin 0.08 1.03 −0.66 0.50 1714.26
TQLNRALTGIAVEQD −1.87 Non-Toxin −0.17 −0.26 0.06 −1.00 1629.03
QLNRALTGIAVEQDK −1.56 Non-Toxin −0.23 −0.47 0.29 0.00 1656.10
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Table 5.

Final selection of HTL epitopes after IFN-epitope analysis.

S.no Peptide Method Result Score
1 VVLSFELLHAPATVC SVM POSITIVE 0.47729732
2 VVVLSFELLHAPATV SVM POSITIVE 0.48135984
3 RVVVLSFELLHAPAT SVM POSITIVE 0.5092653
4 GYQPYRVVVLSFELL SVM POSITIVE 0.6533982
5 PYRVVVLSFELLHAP SVM POSITIVE 0.56872818
6 QPYRVVVLSFELLHA SVM POSITIVE 0.60855322
7 YRVVVLSFELLHAPA SVM POSITIVE 0.77626818
8 TQLNRALTGIAVEQD SVM POSITIVE 0.43169637
9 QLNRALTGIAVEQDK SVM POSITIVE 0.44573276
10 WTAGAAAYY SVM POSITIVE 0.57688791
11 GAAAYYVGY SVM POSITIVE 1.0042404
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3.1. Homology modeling, tertiary structure prediction and validation

The final multi-epitope subunit vaccine model was generated using I-TASSER server. The top finest threading templates for building the protein models were selected (1rqu, 6f0k, 1dd3, 3j4a, 2ftc) and based on high c-score value -0.65 and an estimated TM score of 0.63, model protein was selected. Energy minimization and refinement of the modeled structure was carried out with SPBD viewer and loop regions were identified and refined. Final structure was checked with Ramachandran plot and showed 99% of the residues are in favorable region (Figure 1a) and ERRAT server showed 81% quality score (not shown). The results of ProSA-web obtained for vaccine construct was provided the z-score value of −0.79, as the major parts of the energy plot with N-terminal region and C-terminal region showed highly positive energy values (Figure 1b).

Figure 1.

Figure 1.

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(a) Ramachandran plot is showing 99% of the residues are in favorable region; (b) ProSA-web z-score determined by X-ray crystallography (light blue) and NMR spectroscopy (dark blue) according to the length.

3.2. Analysis of vaccine construct and insilico cloning

PSIPRED produced the secondary structural information of the vaccine construct (Figure 2a). The Prosol server provided the solubility prediction calculations and average of all residues produced the value of 0.46 and indicated good solubility of the vaccine construct (Figure 2b).

Figure 2.

Figure 2.

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(a) Secondary structure predictions of vaccine construct using PSIPRED and (b) Solubility analysis vaccine construct using ProtSol.

The physicochemical features of the vaccine were analyzed by ProtParam tool and were given the Molecular weight of 44 kda, therotical pI caluculated value of 5.16. The instability index (II) is computed to be 16.88 and protein classified as stable and the aliphatic index is 94.92%. Grand average of hydropathicity (GRAVY): 0.262. The estimated half-life is: 30 h (mammalian reticulocytes, in vitro). The overall prediction of the vaccine construct is found to be probable antigen with a score of 0.5848 generated by vaxigen 2.0 and AllerTOP 2.0 has classified the construct to be non-allergen and predicted that the nearest protein to be Scarecrow 1 in Oryza sativa (Figure 3). Optimized codons sequence length was found to be 1175 nucleotides and average GC content was 59.1%. The pET28a (+) vector was used to clone the vaccine construct DNA sequence using SnapGene software (Figure 4).

Figure 3.

Figure 3.

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AllerTOP 2.0 predicted the vaccine construct is a non allergen.

Figure 4.

Figure 4.

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Cloning of the final vaccine construct into the pET28a (+) expression vector and purple represents the gene coding vaccine DNA sequence.

3.3. Molecular docking analysis and MD simulation

The adjuvant which is a 50S ribosomal protein has the ability to stimulate TLR3, Molecular docking using the GRAMXX server (http://vakser.compbio.ku.edu/resources/gramm/grammx/) produced the best protein-protein docking model complex with binding score for molecular docking produced the best structure with global energy -35.98 and interactions and attractive vanderwaals (-32.28) was selected and DIMPLOT of LIGPLUS version 1.2 visualized the interaction between chain A- TLR-3 protein and chain B – Vaccine construct (Figure 5a–c). The RMSD values of protein-ligand complexes were recorded from 0 to 20 ns. The RMSD values steadily increased from 0 to 5 ns and reached a stable state throughout the simulation. The average RMSD values of the complex were found to be 0.27 nm (Figure 6).

Figure 5.

Figure 5.

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(a) Homology modeling of vaccine construct generated by I-TASSER; (b) Docking complex of TLR-3 (PDB ID: 1ziw) and vaccine construct; (c) Docking complex of TLR-3 and vaccine construct interactions visualized using DIMPLOT (Ligplus 1.2 version) (chain A- TLR-3 protein; chain B – Vaccine construct).

Figure 6.

Figure 6.

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The RMSD values of simulated complex of the TLR-3 and Vaccine construct.

3.4. Immune simulation

C-ImmSim considers the successive and successful immune responses of the state of the cell and model the memory of immune cells by a mechanism that increases their half-life. The result of process is that few cells increase their half-life considerably and live longer than other cells. ImmSim server immune simulation results confirmed consistency with actual immune responses. High levels of IgM indicated the primary response. Furthermore, an increase in the B-cell population was characterized by an increase in the expression of immunoglobulins which resulted in a decrease in the concentration of the antigen. Also, there is a consistent rise in Th (helper) cell population with memory development (Figure 7a–c). It was also observed that the production of IFN-γ was stimulated after immunization (Figure 7d). The results clearly explained the T cell population was highly responsive as the memory developed and all other immune cell population shown to be consistent.

Figure 7.

Figure 7.

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C-ImmSim server prediction results of immune response after administering vaccine construct; (a) Antigen and immunoglobulins; (b) B-lymphocytes cell population; (c) CD4+ helper T cells population per state; (d) Induced levels of the cytokine and Simpson index, D.

4. Conclusion

The vaccine candidate against spike viral surface glycoprotein of SARS-CoV-2 was designed by in silico methods. The epitopes predicted with different web servers and adjuvant linkers were used to construct a potent antigenic, non – allergenic vaccine that could elicit strong immune response against SARS-CoV-2. Docking analysis provided the validation in the form of affinity between two molecules (TLR-3 and vaccine) and stability of complex was supported by MD simulations. The in silico immune simulation confirmed immune cell response against antigen clearance rate. The computational cloning by SnapGene confirmed the strong expression of proteins. However, the experimental validation could be essential to ensure to vaccine construct efficacy against COVID-19.

Acknowledgements

The authors acknowledge to VFSTR (Deemed to be university) and DST-FIST (LSI- 576/2013) networking facility to carry out this work.

Glossary

Abbreviations

TLR3

Toll-like receptor3

MD

Molecular dynamics

SARS

Severe Acute Respiratory Syndrome

MERS

Middle East Respiratory Syndrome

MHC

Major histocompatibility complex

CTL

Cytotoxic T-lymphocytes

HTL

Helper T-lymphocytes

RMSD

Root mean square deviation

ns

Nanoseconds

ACE2

angiotensin-converting enzyme 2

IEDB

Immune Epitope Database

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

No potential conflict of interest is reported by the authors.

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