A novel approach to designing viral precision vaccines applied to SARS-CoV-2

doi:10.21203/rs.3.rs-3202563/v1

Download PDF

Article

A novel approach to designing viral precision vaccines applied to SARS-CoV-2

https://doi.org/10.21203/rs.3.rs-3202563/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Efficient precision vaccines against several highly pathogenic zoonotic viruses are currently lacking. Here, we describe a new approach to developing viral precision vaccines based on specific antigen selection through its application to the design of a SARS-CoV-2 cross-variant vaccine. This approach targets viral structural regions involved in infectivity to identify conserved immunogenic epitopes and select those eliciting specific immune responses in infected people. An important step of this approach consists of engineering vaccinating antigens that carry the selected epitopes and mimic their 3D native structure. In our study, the epitopes selected from SARS-CoV-2 revealed an antibody response significantly associated with mild and asymptomatic COVID-19, which strongly suggests protective immunity. Engineered antigens containing the SARS-CoV-2 selected epitopes generated a robust antibody response in mice. Limitations include the need for animal challenge with live virus. Taken together, our results show the potential of this approach for designing precision viral vaccines.

Biological sciences/Biotechnology

Biological sciences/Immunology

Biological sciences/Molecular biology

While vaccines developed to combat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) helped mitigate the coronavirus disease 2019 (COVID-19) pandemic ^1,2, numerous highly pathogenic zoonotic viruses, such as Ebola, Zika, Dengue, West Nile, MERS-CoV and a few others that can potentially cause pandemics, still lack efficient vaccines ^3–5. Even for SARS-CoV-2, the continual emergence of more transmissible genetic variants that escape the immune response ⁶ to previous variants is raising concerns about the resurgence of the COVID-19 pandemic. For SARS-CoV-2, currently available vaccines use a conventional approach to choosing vaccinating antigens. Indeed, these vaccines are based on a non-precision targeting approach using the entire S protein as an antigen⁷. Therefore, a new design concept is needed to develop efficient precision vaccines against SARS-CoV-2 and other concerning viruses, particularly those prone to genetic variations. The current COVID-19 vaccines elicit a good immune response, namely, a humoral response with neutralizing antibodies ^8–11. Nevertheless, these vaccines showed variable efficacy, from very high to moderate, and none conferred sterilizing immunity. Furthermore, some instances of concerning undesirable effects, such as thrombocytopenia and micro blood clots, cardiac tissue inflammation and even possible reprogramming of the immune response, have been reported ^12–15. An approach based on the identification of epitopes eliciting an immunity that interferes precisely with viral cell entry and controls infectivity could be very useful for the design of precision vaccines. This is particularly relevant because for most pathogenic human viruses, the structure responsible for cell entry and infectivity undergoes precisely orchestrated specific proteolysis carried out by numerous proprotein convertases ¹⁶, with furin being the most reactive ¹⁷. Immunity that hampers this process can control viral infectivity.

Compared to common coronaviruses, SARS-CoV-2 has higher infectivity and enhanced pathogenicity, which allowed it to cause the COVID-1 9 pandemic. In this respect, comparison of the structural features of SARS-CoV-2 with those of other coronaviruses revealed differences that account for the enhanced infectivity. These differences particularly relate to the coronavirus spike (S) protein ¹⁸, which plays a key role in the early steps of viral infection. One particularly striking feature of the SARS-CoV-2 genome revealed by comparative genomic studies is the presence of a polybasic furin cleavage site (FCS) at the S1/S2 boundary after the insertion of 12 nucleotides encoding the four amino acid residues PRRA downstream of the transmembrane protease serine 2 (TMPRSS2) S’2 cleavage site. This FCS is not present in the S proteins of any of the less pathogenic coronaviruses of the same clade¹⁹. The FCS lies in a solvent-exposed random coil loop ²⁰. Furin cleavage strongly increases cleavage at the S1/S2 boundary²¹. This proteolytic cleavage of the SARS-CoV-2 S protein acts in concert with cleavage by the host cell protease TMPRSS2 at the S2’ site downstream of S2 ^{22, 23}. This double proteolytic processing of the S protein by furin and TMPRSS2 liberates the fusion peptide (FP) located immediately downstream of the TMPRSS2 site and allows virus‒host cell membrane fusion and virus cell entry. This is probably a major determinant of the high infectivity of SARS-CoV-2 ²⁴. The presence of the FCS has been associated with SARS-CoV-2 pathogenesis, which is consistent with the high infectivity of SARS-CoV-2 ²⁵.

Therefore, identifying immunogenic epitopes within this region can help develop a vaccine likely to generate an immune response that restricts the activation of the virus and thus mitigates infectivity. Most individuals infected with SARS-CoV-2 develop an antibody response 10 to 21 days following infection ^8–10. The detection of IgG antibodies is important for tracking immunity to SARS-CoV-2 during the COVID-19 pandemic. Indeed, IgG antibodies last for several months ²⁶ and, along with IgA antibodies, are generally endowed with antiviral activity. This is generally associated with protective immunity and recovery from COVID-19 ^11,27,28. The presence of antibodies directed to epitopes in the S protein region involved in SARS-CoV-2 activation and enhanced infectivity has not been investigated.

In this paper, we describe a new approach to design an antiviral precision vaccine for highly pathogenic viruses through its application to the design of a SARS-CoV-2 candidate vaccine. We have engineered vaccinating antigen candidates that structurally mimic predicted epitopes in the structural region involved in infectivity. These antigens generated a strong and long-lasting antibody response in mice. We also showed that in patients with COVID-19, these antigens elicit a natural protective immune response. This approach offers a new way to design viral precision vaccines that interfere with infectivity and likely reduce pathogenicity.

Identification of potential B- and T-cell epitopes in the SARS-CoV-2 S protein structural region involved in infectivity:

The first step of our approach to designing a SARS-CoV-2 precision vaccine consisted of delimiting solvent-exposed sequences in the S protein S1/S2 subunit junction that potentially contain immunogenic epitopes. We extracted the structure of the SARS-CoV-2 spike protein monomer from the structural 3D model PDB ID: 6VXX using PyMOL software and visualized it using the Bepipred.2 server. We observed that the S1/S2 junction is mostly solvent exposed (Figure 1). Moreover, the server revealed potential B-cell epitopes in this structural region (Supplementary Table 1). This analysis also showed that furin and TMPRSS2 proteolytic motif sequences lie in two distant solvent-exposed loops (Figure 1) and are part of the predicted potential epitopes, as indicated by the estimated epitope probabilities (Supplementary Table 1). These observations suggest that this important region for virus infectivity can elicit an antibody response. We also submitted the SARS-CoV-2 S protein S1/S2 junction to the improved immunoproteasome cleavage-prediction server to search for putative T-cell epitopes. We identified fourteen immunogenic epitopes that the immunoproteasome of infected cells might generate upon processing of the SARS-CoV-2 S protein (Figure 2). To check whether these predicted epitope sequences are conserved across SAR-CoV-2 variants, we aligned them using BioEdit software ³¹. Thus, we ran the aligned sequences on the Epitope Conservancy Analysis tool of the freely accessible Immune-Epitope Database (IEDB). The percentage of protein sequence matches showed that these peptides are conserved among the twenty-four SARS-CoV-2 variants that circulated and/or are still circulating (Supplementary Table 2). These epitopes are not conserved in SARS-CoV or MERS-CoV (Figure 2). Peptides 3 and 4 contain residues of the FCS sequence (PRRAR), peptide 11 contains the TMPRSS2 cleavage motif, and the sequence of peptide 12 overlaps with the fusion peptide (FP) sequence (Supplementary Table 2).

For confirmation of the T-cell nature of these epitopes, we used the IFNepitope prediction server ²⁹ to determine which epitopes potentially induce interferon g (IFN-g) . The results of this analysis identified peptides 3 and 11 as IFN-g inducing epitopes (Supplementary Table 3).

To further investigate the immunogenic epitopes at SARS-CoV-2 for presentation to the immune system by MHC molecules, we used the Net MHC pan, ProPred I, NetMHC II and ProPred II servers to run algorithms that perform epitope prediction with evaluation of antigenic peptide binding to defined MHC I and II molecules. This study showed that numerous predicted peptides from the SARS-CoV-2 S protein S1/S2 junction bind MHC class 1 and class 2 molecules of known sequence (Supplementary Tables 4, 5 and 6). Several of the predicted peptides contain or overlap amino acids from the FCS or the TMPRSS2 proteolytic site or lie near these sites. Some of these peptides were predicted to have strong binding affinity for specific MHC alleles, while others were predicted to have weak binding affinity (Supplementary Tables 4, 5, and 6). This finding implies that the predicted epitopes can be presented to the immune system through numerous common MHC 1 and class 2 molecules.

Taken together, the prediction data show that the SARS-CoV-2 S protein S1/S2 junction contains epitopes that can potentially elicit both T- and B-cell immune responses, including epitopes that overlap with the furin and TMPRSS2 proteolytic sites.

Design of synthetic polypeptides and study of antibody response to the S protein sequences associated with infectivity in patients with COVID-19.

To check if the predicted epitopes overlapping with the sequence involved in SARS-CoV-2 infectivity generate an immune response in patients with COVID-19, we used two synthetic peptides, P3/FUR/x3 and P4/TMP/x3. We designed these peptides by triplicating a sixteen-residue core sequence (Q662-I679) that spans the FCS in P3/FUR/x3 and a nine-residue core sequence (P794-R802) that spans the TMPRSS2 motif in P4/TMP/x3. In each polypeptide, we introduced the five-residue-long flexible sequence GGGGS as a molecular linker between each repeated core sequence (Table 2). This linker brings stability and maintains distance between epitopes for stable and correct folding of the synthetized polypeptide ³⁰. We used these polypeptides to investigate the specific antibody response in patients with COVID-19.

To study this antibody response, we developed an indirect enzyme-linked immunosorbent assay (ELISA) using the P3/FUR/x3 and P4/TMP/x3 polypeptides as antigens and examined the cohort of 500 patients with COVID as described in Table 1, as well as 100 negative control serum specimens collected before the pandemic. The data showed that none of the control sera reacted with P3/FUR/x3 (Figure 3-A1) and P4/TMP/x3 (Figure 3-A3), while 80% and 86% of the COVID-19 patients’ sera specifically recognized P3/FUR/x3 (Figure 3-A2) and P4/TMP/x3 (Figure 3-A4), respectively. We detected this specific antibody response in numerous patients’ sera up to 50 weeks after infection. These data show that the S protein S1/S2 junction involved in SARS-CoV-2 infectivity elicits a natural robust and persistent antibody response.

To investigate the nature of this antibody response, we analysed the ELISA data in the context of disease status. We observed a highly significant difference (p value = 0.0001) between patients’ antibody response to P3/FUR/x3 based on their disease status. All patients with the asymptomatic or mild form of the disease developed antibodies to epitopes in or around the furin cleavage site sequence (Figure 3B). Therefore, the antibody response to the S protein epitopes covering the furin proteolytic cleavage site in patients with COVID-19 significantly correlates with protective immunity to SARS-CoV-2.

Furthermore, the antibody response we observed did not cause antibody-dependent enhancement (ADE) of infection ^31,32. Indeed, the patients who developed this type of immunity experienced rapid and full recovery, and no COVID-19 was recorded among them ten months after this study.

Engineering and production of recombinant multiepitope vaccinating protein subunit candidates:

Based on the data from the patient-centred study that showed strong and persistent natural immunity against the epitopes of the S protein covering the furin and TMPRSS2 cleavage sites, we developed two protein subunits, P3-L and SJ/FT, that encompassed the epitopes identified from the S protein S1/S2 junction. P3-L is 107 amino acids long, 11.1 kDa protein engineered by sequentially adding 3 core sequences of 32 amino acids that comprise the furin proteolytic site separated by a GGGGS molecular linker (Figure 4A). To design the SJ/FT protein subunit, we selected a sequence of 230 amino acids (24.8 kDa) from the S1/S2 junction that comprises both the furin and TMPRSS2 proteolytic motifs and includes the epitopes predicted earlier by computational analysis.

We selected these two subunits based on their predicted good antigenicity, nonallergenicity and stability. Indeed, we used the VaxiJen server to predict the immunogenicity of S1/S2 junction-derived antigens P3/FUR/x3, P4/TMP/x3, P3-L and SJ/FT. The predicted immunogenicity data suggest that these polypeptides and protein subunits are immunogenic (Supplementary Table 6). In addition, the prediction of allergenicity using the AlgPred and AllerTOP computational tools showed that P3/FUR/x3, P4/TMP/x3 and subunits P3-L and SJ/FT are nonallergenic (Table 3), which makes them suitable for vaccination. Furthermore, we determined the physicochemical properties of P3-L and SJ/FT using the ProtParam tool of the EXPASY database server. The relatively low predicted instability index, good solubility values, and estimated half-life (Supplementary Table 7) suggest that the physicochemical properties of the engineered antigens are suitable for production as recombinant vaccine subunits. We produced P3-L and SJ/FT as recombinant proteins in E. coli (Figure 4B) and purified them as endotoxin-free proteins for use in immunization studies.

Antibody and cellular immune responses to engineered vaccinating antigen candidates of patients with COVID-19.

To further investigate the vaccinating potential of the engineered recombinant subunits, we analysed patients with COVID-19 antibodies and T-cell responses to these antigens. ELISA experiments using P3-L and SJ/FT protein subunits as antigens and the serum of the 500 patients in the COVID-19 cohort showed specific antibody responses in 84.7% and 97%, respectively (Figure 5A). To investigate the T-cell response, we studied IFN-g production upon stimulation of the patients’ immune cells using P3-L and SJ/FT. We used PBMCs from COVID-19 patients with mild disease (MD) or severe disease (SD) and healthy controls (HCs) as defined by the WHO³³ to perform ELISpot experiments. The results showed that the number of cells producing IFN-g in response to stimulation with the P3-L and SJ/FT proteins, compared to unstimulated cells (NS), was highest for patients with MD (p<0.001) (Figure 5B). These observations show that the P3-L and SJ/FT protein subunits contain epitopes that generate antibody and T-cell responses in patients with COVID-19.

Modelling of the engineered candidate vaccinating antigen structures

To further assess the vaccinating potential of the P3-L and SJ/FT protein subunits and the P3/FUR/x3 polypeptide, we carried out a computational study to check whether these antigens exhibit a conserved or mimicked native 3D structure of the SARS-CoV-2 S protein S1/S2 junction. We constructed 3D models of their structures and compared them with the native 3D structure of the S protein monomer. The predicted structural models show an alpha coil/helix fold that is consistent with the structure of immunogenic epitopes and solvent-accessible protein sequences. A repetitive 3-fold display of the alpha coil/helix fold is observed in the P3/FUR/x3 peptide and protein subunit P3-L (Figure 6A), which shows that the unit peptide sequence containing the PRRAR epitope folds independent of the GGGGS linker. Superimposition of the SJ/FT and S protein 3D models (Figure 6B) shows that the overall conformation of the sequence containing the immunogenic epitopes is very well conserved with an observed RMSD of 0.912 angstrom and 171 amino acids out of 230 superimposing perfectly on the native S protein corresponding sequence. Taken together, the data indicate that antigen-presenting cells process the SARS-CoV-2 native S protein and P3-L, SJ/FT and P3/FUR/x 3 in the same way. This observation supports their vaccinating potential.

BALB/c mouse IgG response to synthetic polypeptides P3/FUR/x3 and P4/TMP/x3 and protein subunits P3-L and SJ/FT:

To assess the vaccination potential of the engineered antigens in vivo, we immunized BALB/c mice with different concentrations of P3-L, SJ/FT, P3/FUR/x3 and P4/TMP/x3 ranging from 25 µg to 200 mg. We also used various adjuvants alone or in combination to determine the best adjuvant to use in vaccination. We immunized different groups of 5 mice each by administering 2 doses of the antigens within an interval of two weeks (Supplementary Table 8). We collected sera from week 4 to week 14 following immunization. The results of ELISA performed with the sera collected following immunization of mice with 200 µg of the polypeptide P3/FUR/x3 showed the highest antibody response in the group of mice administered the CpG TLR9 ligand adjuvant (Figure 7A). In mice immunized using the P3-L and SJ/FT protein subunits, we observed the highest IgG titres with a combination of CpG and either one of the adjuvants alhydrogel or MPLA (Figure 7B). P4/TMP/x3 did not induce a significant IgG response repeatedly.

Through the development of this new approach for designing precision viral vaccines, we used computational tools to generate a data set that identified SARS-CoV-2 immunogenic epitopes in the virus structural region associated with infectivity. We experimentally linked these epitopes to protective immunity in patients with COVID-19. Based on these findings, we engineered vaccinating subunit antigens that we thoroughly tested in vivo by immunizing mice. Through this vaccination study, we determined the best antigen concentration and adjuvants that elicited a robust antibody response.

In this report, we describe the different steps of a new approach to design precision vaccines through its application to SARS-CoV-2. Knowing that most of the highly pathogenic human zoonotic viruses that can cause pandemics have structural features associated with infectivity¹⁷, we based this approach on the engineering of vaccinating antigens that mimic the viral surface structure involved in infectivity. For SARS-CoV-2, we engineered polypeptides and recombinant protein subunits that mimic predicted immunogenic sequences present at the S protein S1/S2 junction. This region contains the sites of furin and TMPRSS2 proteolytic cleavage that activate the virus and enhance infectivity²³. We found that the engineered antigens were targeted by the natural immune response in patients with COVID-19 who developed protective immunity. Mouse immunization assays showed that these antigens elicited a strong IgG response. These findings support their vaccinating potential.

The prediction of immunogenic epitopes in the viral surface protein associated with infectivity is an important step of this approach to designing precision vaccines. Moreover, the use of a patient-centred study is crucial to determine whether the predicted epitopes generate natural IgG and T-cell natural immunity. As observed in this work, the patient-centred study was instrumental in uncovering a precise protective immunity elicited by the predicted epitopes, which validates their vaccinating potential in humans. Focusing the search for immunogenic epitopes on the viral structure associated with infectivity is the hallmark of precision by immune targeting that distinguishes this new approach from conventional approaches for vaccine development. Indeed, all the first COVID-19 vaccines use a full-length S protein as a vaccinating antigen and generate non-precision immunity mainly directed to the immunodominant although genetically variable RBD epitopes^34-36^, which explains their decreased efficiency with SARS-CoV-2 genetic variants. Furthermore, the S protein is a biologically active protein that binds the ACE2 receptor and can interfere with associated physiological processes, particularly the renin/angiotensin system (RAS) ³⁷, causing unwanted side effects such as platelet aggregation, thrombosis and exacerbated inflammation ². In addition, immunization using a full-length protein-based approach is more likely to lead to ADE, as reported for MERS-CoV³⁸.

Other vaccines in development are also using the entire S protein in the prefusion state ^39,40 or the RBD for immunization. Such vaccines are not precision vaccines, and even though they are expected to be at least as efficient as the current vaccines, they will probably share their limitations. By targeting selected epitopes, the approach we developed in this study achieves precision and overcomes the genetic variation issue. Furthermore, our findings show that protection from the disease is conferred by the natural immunity that targets the viral structure involved in infectivity. This highlights the importance of careful selection of vaccinating antigens for the development of efficient precision vaccines. This is in line with the Zinkernagel theory stating that protective immunity is antigen-driven and not due to so-called "memory" B and T cells ⁴¹

The engineering of vaccinating antigens that include the identified epitopes is a pivotal step of this approach for designing precision vaccines. We designed the vaccinating antigens as recombinant proteins that mimic the original viral antigenic structure by keeping the protein native scaffold. In addition, their biological and physicochemical properties, particularly allergenicity ⁴² and molecular stability, were suitable for use in vaccine preparations. We also used homopolymerization of the selected epitopes, which is an engineering approach known to enhance immunogenicity ⁴³. At this step of this approach to designing precision viral vaccines, a large array of engineering techniques is available ^44,45. Multiple advanced delivery platforms can also be used to develop vaccinating antigens, particularly recombinant viral shuttle vectors or mRNA-mediated vaccination^46,47, depending on the ease and cost of production, storage conditions and safety profile.

Animal immunization of the engineered antigens and testing of adjuvants are carried out in the final and mandatory step of the approach. In our study, we used mice and observed a robust IgG response, which demonstrates that the selected epitopes are processed properly by the animal immune system. For SARS-CoV-2, few animal models, such as ferrets, are available ⁴⁸. The ultimate testing of the vaccinating potential of engineered antigens is a challenge using live virus. This necessitates access to an adequate high-confinement animal facility, which can be a serious limitation on the testing of antiviral precision vaccines. Another limitation of this approach is the necessity to know the precise molecular basis of infectivity and pathogenicity before designing a precision vaccine for a given virus. This condition was fulfilled for the SARS-CoV-2 virus.

While precision vaccines are the best alternative to currently available COVID-19 vaccines, the new approach described in this report offers a way to develop efficient and precise antiviral vaccines for a number of highly pathogenic viruses in a shorter timeframe and at reasonable cost.

Patient selection and serum preparation: A cohort of 500 COVID-19 patient volunteers was obtained from Asry Medical Center and Awali Hospital in Manama, Bahrain. The selection was based on a positive SARS-CoV-2 PCR test, age range between 18 and 75 years and a clear clinical status defined by the patients’ attending physicians according to WHO COVID-19 classification as asymptomatic, mild, intermediate and severe forms ³³. Information on this cohort is compiled in Table 1. The patient’s sera were prepared and stored at -80°C. One hundred prepandemic historical sera from age-matched healthy individuals were also retrieved and used as negative controls for this study. Informed consent was obtained from all patients and control individuals, and approval was obtained from the Arabian Gulf University Institutional Research and Ethics Committee on December 24, 2020, reference E031-PI-12/20. All methods were performed in accordance with the principles of Helsinki declaration for Medical Research Involving Human Subjects.
Animals: Purpose-bred BALB/c mice used in this study were housed in stainless steel ventilated cages under a 12-hour light/12-hour dark cycle at room temperature ranging from 18 to 22°C and a humidity between 50 and 60%. Animals were fed rodent chow and water ad libitum. Experiments were conducted according to national and international guidelines, such as those issued by the US National Institute of Health Office of Laboratory Animal Welfare (OLAW) https://olaw.nih.gov/home.htm the study is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org). The university Research and Ethics committee approved the protocols (reference E031-PI-12/20).
Retrieval of the S protein monomer structure and analysis of the SARS-CoV-2 S protein S1/S2 junction sequence surface: The coordinates of the SARS-CoV-2 S protein trimeric form (PDB ID: 6VXX) were downloaded from the RCSB database (https://www.rcsb.org). Water and heteroatoms were removed from the coordinates, and the coordinates of the complex were split into separate PDB monomer files using PyMOL software⁴⁹ from the Schrodinger software packageR. Because the PDB ID: 6VXX model is missing the furin and TMPRSS2 cleavage site loops, the S protein monomer was modelled to include these loops using the Phyre2 web server (http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index) ⁵⁰, and the results were saved on PyMOL software to visualize exposed residues.
B-cell epitope prediction. The S protein amino acid sequence was submitted to the BepiPred-2.0 Sequential B-Cell Epitope Predictor server (http://www.cbs.dtu.dk/services/BepiPred/cite.php) using the default parameters to predict putative immunogenic epitopes in the S protein monomer surface-exposed regions. BepiPred-2.0 is based on a random forest algorithm trained on epitopes and nonepitopes annotated from antibody-antigen protein crystal structures ⁵¹.
Prediction of proteasome and immunoproteasome cleavage sites: The S protein S1/S2 junction sequence (residues E648-L809) containing the furin and TMPRSS2 proteolysis sites was submitted to the improved Proteasome Cleavage Prediction Server (http://imed.med.ucm.es/Tools/pcps/). This server predicted the cleavage sites generated by the constitutive proteasome/immunoproteasome within the protein sequence using different Ngram models ⁵² and displayed the predicted epitopes.
Prediction of T-cell epitopes and binding to MHC class 1 and class 2: To predict MHC class 1-binding peptides, we used the NetMHCpan 4.1 (https://services.healthtech.dtu.dk/services/NetMHCpan-4.1/) and NetMHC - 4.0 (https://services.healthtech.dtu.dk/services/NetMHC-4.0/) servers. NetMHCpan and NetMHC servers operate on artificial neural networks (ANNs) to predict the binding of peptides to MHC class I molecules of known sequences. To predict MHC class 2 peptides, NetMHCII 2.3 (https://services.healthtech.dtu.dk/services/NetMHCII-2.3/) ⁵³, which allows for the prediction of peptides binding to HLA-DR, HLA-DQ, and HLA-DP, was used. The S protein S1/S2 junction sequence was analysed using the default settings of all servers. Peptides were then sorted according to their binding affinity level to MHC molecules. Binding affinity prediction methods for NetMHCII were constructed using an extended data set of quantitative MHC–peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 molecules ⁵³.
Conservation of the predicted epitopes: We used BioEdit, a multiple sequence alignment ⁵⁴ software program, to analyse the conservation of the identified epitopes. We used the Epitope Conservancy Analysis tool from the freely accessible Immune-Epitope Database (IEDB; http://tools.iedb.org/conservancy/) to assess the variability of the epitopes based on the sequence alignment of 25 SARS-CoV-2 variants that have circulated or are still circulating since the start of the pandemic, along with SARS-CoV and MERS-CoV sequences. The amino acid sequences of the extracellular domain of SARS-CoV, MERS-CoV, and SARS-CoV-2 spike protein were acquired from the National Center for Biological Information (NCBI) protein ID: YP_009825051.1, YP_009047204.1 and YP_009724390.1, respectively. SARS-CoV-2 variant sequences were created by introducing specific mutations to the Wuhan sequence to generate different variants using published mutations in the CoVariants (https://covariants.org/) and Stanford University SARS-CoV-2 Variants databases (https://covdb.stanford.edu/variants/omicron_ba_1_3/)
Prediction of IFN-γ epitopes: We used the interferon epitope server https://webs.iiitd.edu.in/raghava/ifnepitope/predict.php to identify IFN-γ-inducing epitopes in the designed polypeptide P3/Fur/x3 and protein subunits P3-L and SJ/FT.
3D Modelling of immunogenic polypeptides and protein subunits: For ab initio modelling, we used Iterative Threading Assembly Refinement (ITASSER) (https://zhanglab.dcmb.med.umich.edu). All designed peptide sequences were submitted to the ITASSER server using the default settings. The server identified structural templates from the Template Library by using the multiple threading approach LOMETS. Functional insights regarding the target were then derived by rethreading the 3D models through the protein function database BioLiP⁵⁵. The obtained models were visually inspected with PyMOL.
Polypeptide synthesis: The polypeptides P3/FUR/x3 and P4/TMP/x3 were chemically synthesized by GeneCust (GeneCust, Boynes, France) at a 5 mg scale and 85% minimum purity, as verified by mass spectrometry analysis and high-performance liquid chromatography (HPLC). For the polypeptides used in the indirect ELISA, a biotin tag was alternatively added to allow antigen binding to streptavidin-coated plates.
Indirect ELISA development: Ninety-six-well ELISA plates (Nunc MaxiSorp, Thermo Fisher Scientific) were coated with 200 ng per well of streptavidin in carbonate-bicarbonate buffer, pH 9.6 (C3041, Sigma‒Aldrich), overnight at 4°C. The coated plates were then washed three times with 200 µl of wash buffer (0.1 M phosphate, 0.15 M sodium chloride, pH 7.2, 0.05% Tween 20) and blocked with wash buffer containing 5% nonfat milk “Régilait” powder at room temperature for 1 hour under shaking. Then, the plates were washed as previously described, and 100 µl of biotinylated peptides (200 ng) was added to each well. After peptide coating, the plates were washed again, 100 µl of serum sample diluted to 1:100 in dilution buffer was added to each well, and the plates were incubated at room temperature for 1 h. The plates were then extensively washed 3 times, 10000-fold-diluted horseradish peroxidase (HRP)-conjugated goat anti-human IgG (Abcam) was added, and the plates were incubated for 1 hour at room temperature. TMB substrate (TMB, Sigma‒Aldrich) was added to each well, and the plates were then incubated for 20 minutes in the dark. The reaction was stopped by adding 100 μL of 2 N H2SO4 solution. The optical density (OD) was measured at 450 nm using a FLUOstar Omega (BMG LABTECH) absorbance microplate reader. We defined a sample as IgG positive if the OD value was equal to 3 standard deviations (SDs) above the mean of the negative control values (n=100).
Production and purification of the recombinant multiepitope protein subunits: The multiepitope subunit P3-L was designed to include a homopolymerized (3 times) 32-amino-acid core sequence containing the FCS. The SJ/FT subunit was designed to contain both the furin and TMPRSS2 cleavage motifs and the FP sequence. The two protein subunits were produced as recombinant proteins. The cDNA nucleotide sequences encoding these multiepitope subunits were chemically synthetized by GeneCust (GeneCust, Boynes, France). Each cDNA was inserted into the pET-22b (+) plasmid (Novagen Sigma‒Aldrich, USA) NdeI- XhoI multiple cloning site, and the recombinant expression vector was transformed into Escherichia coli BL21 (DE3) cells. Positive colonies were selected on 50 µg/ml ampicillin agarose plates. For each recombinant cDNA, a single positive colony was grown overnight at 37°C on LB medium, the expression of the recombinant protein was induced by adding 0.5 mm IPTG, and bacteria were cultured at 220 rpm for 16 hours at 37°C for P3-L and at 15°C for SJ-FT. The expressed protein was purified from the sonicated bacterial pellet using a Ni-NTA column. The purified protein was dialysed in 20 mM Tris, 4 M urea, pH 8.0, at 4℃. Endotoxin was removed using endotoxin removal beads from Milteryi Biotec (Bergischgladbach/Germany).
Immunogenicity and allergenicity prediction: We used the VaxiJen server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) and ANTIGENpro module of the SCRATCH protein predictor (http://scratch.proteomics.ics.uci.edu/) to evaluate the immunogenicity of the multiepitope subunits. We used the AllerTOP v. 2.0 (http://www.ddg-pharmfac.net/AllerTOP/) and AlgPred servers (http://crdd.osdd.net/raghava/algpred/) to check for potential allergenicity.
Determination of physicochemical characteristics: We used the ProtParam tool of the EXPASY database server (http://web.expasy.org/protparam/) to determine the physicochemical parameters (molecular weight, half-life, atomic composition, stability index and mean hydrophilicity) of the vaccine antigens.
Study of interferon-γ production by ELISpot and ELISA: Cell culture and stimulation: Peripheral blood mononuclear cells (PBMCs) were isolated from heparinized blood from 10 healthy donors and 10 patients with COVID-19 by density centrifugation through Ficoll-Hypaque (GE Healthcare Bio-Sciences AB, Uppsala, Sweden). PBMCs were cultured in 96-well plates at a concentration of 1 × 10⁶ cells/ml in a final volume of 200 𝜇l of RPMI 1640 medium (Sigma, St. Louis, MO) supplemented with 2 mM L-glutamine (Sigma, St. Louis, MO), 100 U/ml penicillin (Sigma, St. Louis, MO), 100 𝜇g/ml streptomycin (Sigma, St. Louis, MO), 10% (v/v) heat-inactivated human AB serum (Sigma, St. Louis, MO), 1% HEPES (0.01 M), (Invitrogen, Cergy-Pontoise, France), 1% sodium pyruvate (1 mM) (Invitrogen, Cergy-Pontoise, France), 1% MEM nonessential amino acids (Invitrogen, Cergy-Pontoise, France), 2-mercaptoethanol (2−10 M), (Invitrogen, Cergy-Pontoise, France), and 0.2% gentamicin (20 𝜇g/ml) (Invitrogen, Cergy-Pontoise, France). The cells were stimulated with individual proteins or with peptide at a final concentration of 10 μg/ml for 10 days at 37°C in 5% CO₂. Recombinant human IL-2 (BD Bioscience, San Diego, CA) (10 IU/well) was added at days 2, 4 and 7 during culture. For positive controls, cells were stimulated with phytohemagglutinin (PHA) (Sigma‒Aldrich, St Louis, MO) (10 μg/ml) for 3 days. Negative control cells were not stimulated. Culture supernatants were then harvested and frozen at -80°C until use for ELISA, whereas cells were used for the ELISpot test.
Enzyme-linked immunospot assay (ELISpot): The ELISpot assay was performed with PBMCs stimulated for 10 days with proteins or peptides (10 μg/ml) or for 5 days with PHA (10 μg/ml) using a human IFN-g immunospot assay. Briefly, 96-well PVDF filter plates (Millipore) were coated with 100 µl of anti-human IFN-γ monoclonal antibody (mAb) (2 µg/ml) (1-D1K, Mabtech) overnight at 4°C, and free binding sites were blocked with X-VIVO for 1 hour at room temperature. The cells were washed twice with phosphate-buffered saline (PBS 1X), resuspended in X-VIVO™ 15 medium (Lonza, Sweden), restimulated with protein or peptide (10 µg/ml) for 30 min at 37°C, and then transferred to coated ELISpot plates. The cells were incubated for 16 hours in a 5% CO2 humidified atmosphere at 37°C. Biotinylated anti-IFN-γ detection antibody (7-B6-1, Mabtech) (1 µg/ml) was added, and the plates were incubated for 2 hours at room temperature. After washing, ExtrAvidin-Alkaline Phosphatase (Sigma) diluted 1/2000 was added, and the plates were incubated for 30 min at room temperature. The spots were revealed by adding the substrate (BCIP/NBT)-containing buffer. Spots were counted using a CTL ImmunoSpot reader (CTL Analysers, Shaker Heights, OH). The results are expressed as spot-forming units (SFU) per 106 PBMCs.
Mouse immunization: Female BALB/c mice aged 6 to 8 weeks were used for the immunogenicity study. Each group of mice consisted of five animals. For peptides (P3/FUR/x3, P4/TMP/x3), animals were immunized via the subcutaneous or intramuscular route and received three injections at day 0, day 14, and day 28 with 200 μg of P3/FUR/x3 or P4/TMP/x3 mixed with 25 μg of CpG (ODN 2395 VacciGrade, InvivoGen, USA) adjuvant. PBS was used for the immunization of the control group. Blood samples were collected before immunization, then at days 28 and 42 and up to day 100 for some groups. For the recombinant protein subunits (P3-L, SJ/FT), we evaluated the effect of escalating doses (10, 25, and 50 μg), as well as the effect of adjuvants (2 μg of CpG, 5 μg of MPLA, and 50 μg of alhydrogel). Mice received two successive shots at day 0 and day 14, and blood samples were collected 14 days after the second administration. Serum samples were analysed for specific IgG against antigens with an in-house indirect ELISA. Briefly, mouse sera were diluted at 1:100 and added to the coated plate with the specific antigen. After incubation, HRP-conjugated goat anti-mouse IgG diluted 1:2000 (Abcam) was used as the secondary antibody.

Data analysis: For the analysis of the ELISA results from the cohort of COVID-19 patients, we used R (v. 4.0.5), readxl (v. 1.3.1), dplyr (v. 1.0.6) and stringi (v. 1.5.3) software packages for data handling and analysis. The “disease status” variable was assigned a numeral (integer) and factor (categorical variable) using the following rules: 0=asymptomatic, 1 = mild, 2 = moderate, and 3 = severe. We transformed the OD values obtained from the ELISA experiments from numeric (float, quantitative variable) to factor (categorical variable) data using the following rule: OD < 0.45 = immune (–) and OD ≥ 0.45 = immune (+) for the epitopes displayed by peptide the P3. For calculation of the chi-square test, we used the chisq.test function from the R Stats Package. We used the Monte Carlo simulation method instead of the asymptotic chi-squared distribution of the test statistic to compute the p value because some of the expected values were below 5. For the analysis of IFN-γ data, statistical analyses were performed with GraphPad Prism 9.2.0 (GraphPad Software, San Diego, CA). The results are expressed as medians (interquartile range, IQR, as variances). The Wilcoxon signed-rank test was used to compare the median IFN-g production between stimulated and unstimulated cells. The Mann‒Whitney test was used for intergroup analysis. p values less than 0.05 were considered to indicate significance. Correlations were estimated using the Spearman rank (rs) correlation coefficient.

Data Availability:

All data generated or analysed during this study are included in this published article and its supplementary information files.

Aknowledgments:

We are grateful to Dr Khaled Al Ohaly president of the Arabian Gulf University for providing an internal special grant (Reference LS_2021/COVID19) from the institution’s internal research funds. We thank Mr Ammar Marouani for his assistance in animal studies.

Authors Contributions:

KT= Immunizations and ELISA studies, data collection, analysis, recombinant proteins, QA/QC and writing. NBK= Computed epitopes predictions, in silico protein modelization, data analysis and writing. ARR= Patients sample collection and management, ELISA experiments. DA= Sequence retrieval and analysis, protein structure modeling. SC= Statistical analysis. TB= Cell culture, cytokine studies and data analysis. MBA= Cytokine studies and data analysis. MDF= Project conceptualisation and experimental design, supervision QA/QC, data integration and analysis, antigens design/engineering and manuscript writing.

Conflict of intrest:

The authorrs declare no conflict of interest..

Gibson, G. C. et al. Real time monitoring of COVID-19 intervention effectiveness through contact tracing data. Sci Rep 13, 9371 (2023). https://doi.org:10.1038/s41598-023-35892-0
Angeli, F., Spanevello, A., Reboldi, G., Visca, D. & Verdecchia, P. SARS-CoV-2 vaccines: Lights and shadows. Eur J Intern Med 88, 1–8 (2021). https://doi.org:10.1016/j.ejim.2021.04.019
Trovato, M., Sartorius, R., D'Apice, L., Manco, R. & De Berardinis, P. Viral Emerging Diseases: Challenges in Developing Vaccination Strategies. Front Immunol 11, 2130 (2020). https://doi.org:10.3389/fimmu.2020.02130
Vaccine 35, 4470–4474 (2017). https://doi.org:10.1016/j.vaccine.2017.04.082
Maslow, J. N. Vaccines for emerging infectious diseases: Lessons from MERS coronavirus and Zika virus. Hum Vaccin Immunother 13, 2918–2930 (2017). https://doi.org:10.1080/21645515.2017.1358325
Krause, P. R. et al. SARS-CoV-2 Variants and Vaccines. N Engl J Med 385, 179–186 (2021). https://doi.org:10.1056/NEJMsr2105280
Walls, A. C. et al. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 181, 281–292 e286 (2020). https://doi.org:10.1016/j.cell.2020.02.058
Zhao, J. et al. Antibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019. Clin Infect Dis 71, 2027–2034 (2020). https://doi.org:10.1093/cid/ciaa344
Okba, N. M. A. et al. Severe Acute Respiratory Syndrome Coronavirus 2-Specific Antibody Responses in Coronavirus Disease Patients. Emerg Infect Dis 26, 1478–1488 (2020). https://doi.org:10.3201/eid2607.200841
Long, Q. X. et al. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med 26, 845–848 (2020). https://doi.org:10.1038/s41591-020-0897-1
Earle, K. A. et al. Evidence for antibody as a protective correlate for COVID-19 vaccines. Vaccine 39, 4423–4428 (2021). https://doi.org:10.1016/j.vaccine.2021.05.063
Menni, C. et al. Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study. Lancet Infect Dis 21, 939–949 (2021). https://doi.org:10.1016/S1473-3099(21)00224-3
Thompson, D., Delorme, C. M., White, R. F. & Honer, W. G. Elevated clozapine levels and toxic effects after SARS-CoV-2 vaccination. J Psychiatry Neurosci 46, E210-E211 (2021). https://doi.org:10.1503/jpn.210027
Trogen, B., Oshinsky, D. & Caplan, A. Adverse Consequences of Rushing a SARS-CoV-2 Vaccine: Implications for Public Trust. JAMA 323, 2460–2461 (2020). https://doi.org:10.1001/jama.2020.8917
Föhse, F. K. et al. The BNT162b2 mRNA vaccine against SARS-CoV-2 reprograms both adaptive and innate immune responses. 2021.2005.2003.21256520 (2021). https://doi.org:10.1101/2021.05.03.21256520%J medRxiv
Babe, L. M. & Craik, C. S. Viral proteases: evolution of diverse structural motifs to optimize function. Cell 91, 427–430 (1997). https://doi.org:10.1016/s0092-8674(00)80426-2
Izaguirre, G. The Proteolytic Regulation of Virus Cell Entry by Furin and Other Proprotein Convertases. Viruses 11 (2019). https://doi.org:10.3390/v11090837
Walls, A. C. et al. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 183, 1735 (2020). https://doi.org:10.1016/j.cell.2020.11.032
Coutard, B. et al. The spike glycoprotein of the new coronavirus 2019-nCoV contains a furin-like cleavage site absent in CoV of the same clade. Antiviral Res 176, 104742 (2020). https://doi.org:10.1016/j.antiviral.2020.104742
Li, W. Delving deep into the structural aspects of a furin cleavage site inserted into the spike protein of SARS-CoV-2: A structural biophysical perspective. Biophys Chem 264, 106420 (2020). https://doi.org:10.1016/j.bpc.2020.106420
Vankadari, N. Structure of Furin Protease Binding to SARS-CoV-2 Spike Glycoprotein and Implications for Potential Targets and Virulence. J Phys Chem Lett 11, 6655–6663 (2020). https://doi.org:10.1021/acs.jpclett.0c01698
Jackson, C. B., Farzan, M., Chen, B. & Choe, H. Mechanisms of SARS-CoV-2 entry into cells. Nat Rev Mol Cell Biol 23, 3–20 (2022). https://doi.org:10.1038/s41580-021-00418-x
Fuentes-Prior, P. Priming of SARS-CoV-2 S protein by several membrane-bound serine proteinases could explain enhanced viral infectivity and systemic COVID-19 infection. J Biol Chem 296, 100135 (2021). https://doi.org:10.1074/jbc.REV120.015980
Lavie, M., Dubuisson, J. & Belouzard, S. SARS-CoV-2 Spike Furin Cleavage Site and S2' Basic Residues Modulate the Entry Process in a Host Cell-Dependent Manner. J Virol 96, e0047422 (2022). https://doi.org:10.1128/jvi.00474-22
Johnson, B. A. et al. Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis. Nature 591, 293–299 (2021). https://doi.org:10.1038/s41586-021-03237-4
Yousefi, Z. et al. Long-Term Persistence of Anti-SARS-COV-2 IgG Antibodies. Curr Microbiol 79, 96 (2022). https://doi.org:10.1007/s00284-022-02800-0
Khoury, D. S. et al. Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection. Nat Med 27, 1205–1211 (2021). https://doi.org:10.1038/s41591-021-01377-8
Focosi, D., Maggi, F. & Casadevall, A. Mucosal Vaccines, Sterilizing Immunity, and the Future of SARS-CoV-2 Virulence. Viruses 14 (2022). https://doi.org:10.3390/v14020187
Dhanda, S. K., Vir, P. & Raghava, G. P. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct 8, 30 (2013). https://doi.org:10.1186/1745-6150-8-30
Patel, D. K., Menon, D. V., Patel, D. H. & Dave, G. Linkers: A synergistic way for the synthesis of chimeric proteins. Protein Expr Purif 191, 106012 (2022). https://doi.org:10.1016/j.pep.2021.106012
Lee, W. S., Wheatley, A. K., Kent, S. J. & DeKosky, B. J. Antibody-dependent enhancement and SARS-CoV-2 vaccines and therapies. Nat Microbiol 5, 1185–1191 (2020). https://doi.org:10.1038/s41564-020-00789-5
Karthik, K., Senthilkumar, T. M. A., Udhayavel, S. & Raj, G. D. Role of antibody-dependent enhancement (ADE) in the virulence of SARS-CoV-2 and its mitigation strategies for the development of vaccines and immunotherapies to counter COVID-19. Hum Vaccin Immunother 16, 3055–3060 (2020). https://doi.org:10.1080/21645515.2020.1796425
Organization, W. H. COVID-19 Clinical management: Living guidance. (2021).
Chen, Z. et al. T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research. J Cell Mol Med 25, 1274–1289 (2021). https://doi.org:10.1111/jcmm.16200
Piccoli, L. et al. Mapping Neutralizing and Immunodominant Sites on the SARS-CoV-2 Spike Receptor-Binding Domain by Structure-Guided High-Resolution Serology. Cell 183, 1024–1042 e1021 (2020). https://doi.org:10.1016/j.cell.2020.09.037
Bestle, D. et al. TMPRSS2 and furin are both essential for proteolytic activation of SARS-CoV-2 in human airway cells. Life Sci Alliance 3 (2020). https://doi.org:10.26508/lsa.202000786
Wiese, O. J., Allwood, B. W. & Zemlin, A. E. COVID-19 and the renin-angiotensin system (RAS): A spark that sets the forest alight? Med Hypotheses 144, 110231 (2020). https://doi.org:10.1016/j.mehy.2020.110231
Prompetchara, E., Ketloy, C. & Palaga, T. Immune responses in COVID-19 and potential vaccines: Lessons learned from SARS and MERS epidemic. Asian Pac J Allergy Immunol 38, 1–9 (2020). https://doi.org:10.12932/AP-200220-0772
Pallesen, J. et al. Immunogenicity and structures of a rationally designed prefusion MERS-CoV spike antigen. Proc Natl Acad Sci U S A 114, E7348-E7357 (2017). https://doi.org:10.1073/pnas.1707304114
Ciaramella, G. & Himansu, S. Betacoronavirus mRNA vaccine. US10702600B1 (2020).
Zinkernagel, R. M. & Hengartner, H. Protective 'immunity' by pre-existent neutralizing antibody titers and preactivated T cells but not by so-called 'immunological memory'. Immunol Rev 211, 310–319 (2006). https://doi.org:10.1111/j.0105-2896.2006.00402.x
Saha, S. & Raghava, G. P. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 34, W202-209 (2006). https://doi.org:10.1093/nar/gkl343
Borras-Cuesta, F., Fedon, Y. & Petit-Camurdan, A. Enhancement of peptide immunogenicity by linear polymerization. Eur J Immunol 18, 199–202 (1988). https://doi.org:10.1002/eji.1830180203
Iyer, S., Yadav, R., Agarwal, S., Tripathi, S. & Agarwal, R. Bioengineering Strategies for Developing Vaccines against Respiratory Viral Diseases. Clin Microbiol Rev 35, e0012321 (2022). https://doi.org:10.1128/CMR.00123-21
Zhou, X. et al. Engineering Antiviral Vaccines. ACS Nano 14, 12370–12389 (2020). https://doi.org:10.1021/acsnano.0c06109
Polack, F. P. et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N Engl J Med 383, 2603–2615 (2020). https://doi.org:10.1056/NEJMoa2034577
Kremer, E. J. Pros and Cons of Adenovirus-Based SARS-CoV-2 Vaccines. Mol Ther 28, 2303–2304 (2020). https://doi.org:10.1016/j.ymthe.2020.10.002
Gimenes Lima, G., Portilho, A. I. & De Gaspari, E. Animal Models to Test SARS-CoV-2 Vaccines: Which Ones Are in Use and Future Expectations. Pathogens 12 (2022). https://doi.org:10.3390/pathogens12010020
DeLano, W. L. PyMOL: An Open-Source Molecular Graphics Tool. (2002).
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10, 845–858 (2015). https://doi.org:10.1038/nprot.2015.053
Jespersen, M. C., Peters, B., Nielsen, M. & Marcatili, P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 45, W24-W29 (2017). https://doi.org:10.1093/nar/gkx346
Garcia-Boronat, M., Diez-Rivero, C. M., Reinherz, E. L. & Reche, P. A. PVS: a web server for protein sequence variability analysis tuned to facilitate conserved epitope discovery. Nucleic Acids Res 36, W35-41 (2008). https://doi.org:10.1093/nar/gkn211
Jensen, K. K. et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154, 394–406 (2018). https://doi.org:10.1111/imm.12889
Hall, T. A. in Nucleic Acids Symposium Series. 95–98.
Yang, J. & Zhang, Y. I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res 43, W174-181 (2015). https://doi.org:10.1093/nar/gkv342

TABLE 1: SARS-CoV-2-INFECTED PATIENT AND HEALTHY DONOR COHORT CHARACTERISTICS

		HEALTHY DONORS	COVID-19 PATIENTS
AGE [years]
Average		40.7	43.0
Range		[21-66]	[21-68]
SEX
Male		69	470
Female		31	30
PCR POSITIVITY		NA	500
Range of sampling time from date of positive PCR testing		NA	2.5 to 55 weeks
DISEASE STATUS	Code
Asymptomatic	0	NA	310
Mild	1	NA	124
Moderate	2	NA	29
Severe/critical	3	NA	37
SAMPLE COLLECTION PERIOD		January–May 2021

Table 2: Design of antigenic polypeptides: Amino acid sequence of polypeptides (P3/FUR/x3) and (P4/TMP/x3). The triplicate core sequence for each polypeptide is shown in red. The GGGGS sequence is introduced at the NH2 terminus of each core sequence. These polypeptides can be structurally modified to introduce subtle conformational changes, including modifications of MHC-anchor substitutions, which increase their stability, protease resistance and immunogenicity and enhance their vaccinating potential.

Peptide Name	Peptide Sequence
P3/FUR/x3	GGGGSQTNSPRRARSVASQSIGGGGSQTNSPRRARSVASQSIGGGGSQTNSPRRARSVASQSI
P4/TMP/x3	GGGGSPDPSKPSKRGGGGSPDPSKPSKRGGGGSPDPSKPSKR

TABLE 3: Predicted antigenicity and allergenicity of SARS-CoV-2 polypeptide and protein subunit vaccines.

A-Immunogenicity Prediction
Subunit	P3/FUR/x3	P4/TMP/x3	P3-L	SJ/FT
VAXIJEN	0.4331	0.6850	0.4436	0.4512

B- Allergenicity Prediction
Subunit	P3/FUR/x3	P4/TMP/x3	P3-L	SJ/FT
AllerTOP	(-)	(+)	(+)	(-)
AlgPred	(-) ^#*0.5130	(-)*	(-) ^# *0.7027	(-) ®*0.5310

# Prediction by SVM method based on amino acid composition [score threshold=-0.4]

® Prediction based on SVM method based on dipeptide composition [score threshold= -0.2]

*The protein sequence does not contain an experimentally proven IgE epitope

No competing interests reported.

SUPPFILESAMENDEDMDF.11823docx1.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

A novel approach to designing viral precision vaccines applied to SARS-CoV-2

Status:

Version 1

Abstract

Figures

Introduction

Results

Discussion

Materials And Methods

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1