Vaccine Design against Coronavirus Spike (S) Glycoprotein in Chicken: Immunoinformatic and Computational Approaches

Abstract

Background: Infectious bronchitis (IB) is a highly contagious respiratory disease in chickens and produces economic loss within the poultry industry. It is caused by a single stranded RNA virus belonging to Cronaviridae family. 

Methods: The present study used various tools in Immune Epitope Database (IEDB) to predict conserved B and T cell epitopes against IBV spike (S) protein that may perform a significant role in provoking the resistance response to IBV infection.

Results: In B cell prediction methods, three epitopes (₁₁₃₉KKSSYY₁₁₄₄, ₁₁₄₀KSSYYT₁₁₄₅, ₁₁₄₁SSYYT₁₁₄₅) were selected as surface, linear and antigenic epitopes. 

Many MHCI and MHCII epitopes were predicted for IBV S protein. Among them ₉₈₂YYITARDMY₉₉₀ and ₉₈₃YITARDMYM₉₉₁ epitopes displayed high antigenicity, no allergenicity and no toxicity as well as great linkage with MHCI and MHCII alleles. Moreover, docking analysis of MHCI epitope produced strong binding affinity with BF₂ alleles.  

Conclusion: Five conserved epitopes were expected from spike glycoprotein of IBV as the best B cell and T cell epitopes due to high antigenicity, no allergenicity and no toxicity. In addition, MHC epitopes showed great linkage with MHC alleles as well as strong interaction with BF2 alleles. These epitopes should be designed and incorporated and then tested as multi-epitope vaccine against IBV.

Introduction

Infectious bronchitis virus (IBV) is a single positive stranded RNA that belonging to coronavirus of the chicken (Gallus gallus). It is a highly contagious respiratory disease in chickens that is mainly severe for very young chicks. The signs of illness include tracheal rales, coughing, sneezing, nasal discharge and some strains may cause kidney damage [1, 2]. The disease can be transmitted by infected chickens in respiratory discharges and feces, and it can be spread by aerosol, ingestion of contaminated feed and water, and contact with contaminated equipment or clothing. The virus cannot be transmitted via eggs [3]. The disease causes economic loss within the poultry industry, affecting the performance of meat-type and egg-laying birds. The disease can affect all ages, but the clinical disease is more severe in young chicks. Chicks become more resistant to IBV-induced mortality with increasing age [4].

There are four structural proteins associated with the envelope, the spike (S), membrane (M), envelope (E), and nucleocapsid (N) protein [5]. The spike 'S' glycoprotein which located at the surface of the virion. The membrane 'M' glycoprotein is partially exposed at the surface of the virion and the nucleocapsid 'N' protein that located internally. The spike glycoprotein of IBV induces virus neutralizing (VN) and HI antibodies and has been considered as the most likely inducer of protection [2, 4]. The S protein is a dimer or trimer. It has two known functions; to attach the virus to receptor molecules on host cells, and to activate fusion of the virion membrane with host cell membranes, to release the viral genome into the cell [2]. The spike gene is highly variable, especially the S1 part, due to insertions, deletions, substitutions and recombination events [6]. Application of vaccine is the most effective way of protective against pathogenic, specifically when these pathogens have a high mortality rate such as IBV and virus in general. On the other hand, the large number of serotypes and strains (genotype) of IBV make control process complicated precisely. IBV has shift and drift property [7].

Vaccination with inactivated vaccines and live-attenuated vaccines is used to control the disease. However, inactivated vaccines frequently fail to induce strong cellular immunity, while live-attenuated vaccines can contribute to the emergence of antigenic variant viruses [5]. The increasing number of new serotypes of IBV, which were caused by frequent gene mutation and recombination, are a major challenge for the prevention and control of IB disease [8].

RNA viruses have high mutational rates, such as IBV. So the most important step in the design of cross-protective peptide vaccine against IBV is to target the conserved epitopes of different serotypes of IBV [5].

There is an essential need for the development of safer and more effective vaccines to control IBV. Therefore, the aim of this study is to analyze strains of spike (S) glycoprotein of infectious bronchitis virus reported in NCBI database using immunoinformatics and computational approaches to select all possible epitopes that can be used as multi-epitopes vaccine.

2. Material And Method

2.2 Structural Analysis

Reference sequence of spike S protein (NP_040831.1) was analyzed to identify the chemicals and physical properties including GRAVY (Grand average of hydropathicity), half-life, molecular weight, stability index and amino acid atomic composition using an online tool Protparam [9].

Secondary structure of spike S protein of IBV was investigated through PSIPRED [10]. The secondary structure of protein including helix, sheet, turn, and coil parameters was predicted using GOR IV server at https://npsa-prabi.ibcp.fr/cgi-bin/secpred_sopma.pl.

TMHMM an online tool (http://www.cbs.dtu.dk/servi ces/TMHMM/), used to examine the trans-membrane topology of S protein. Presence of disulphide-bonds were predicted through an online tool DIANNA v1.1. It makes prediction based on trained neural system [11, 12]. CDD-BLAST (http://www.ncbi.nlm.nih.gov/BLAST/) [13–15] and PFAM (http://www.pfam.sanger.ac.uk/) 229E [16] were used to search the defined conserved domains in the targeted protein sequences. Blastp in NCBI database (https://blast.ncbi.nlm.nih.gov/Blast.cgi) with default parameters for conservation analysis was used to compare homologous spike reference sequences of different coronaviruses in human and animals against IBV spike protein sequence. Phylogenetic Tree was also constructed based on constraint-based Multiple alignment Tool (COBALY). (https://www.ncbi.nlm.nih.gov/blast/treeview/treeView.cgi) [12, 17].

2.3 Multiple Sequence Alignment And Epitope Conservancy Assessment

The retrieved sequences of IBV S protein were aligned using Clustal program and consensus sequence was generated using the multiple sequence alignment (MSA) tool, Jalview version 2.10.5. (http://www.jalview.org/about/jalview-scientific-advisory-committee) [18]. Epitope conservancy analysis in Immune Epitope Database (IEDB) was used to detect potential epitope conservancyn(http://tools.iedb.org/conservancy/) [19]. For calculating the conservancy score, the sequence identity threshold was kept at 80%.

2.4 Phylogeny Analysis

Phylogenetic tree of the retrieved sequences of spike (S) protein was performed using MEGA7.0.26 (7170509) software using maximum likelihood parameter [20].

2.5 B Cell Epitope Prediction

The Immune Epitope Database (IEDB) (http://tools.iedb.org/mhci/) was used to predict B and T cell epitopes of IBV reference sequence of S proteins (NP_040831.1) [21]. Linear B-cell epitopes were predicted using BepiPred from IEDB [22]. Emini surface accessibility prediction tool was used to predict epitopes located on the surface [23]. Whereas, the antigenic epitopes were investigated using kolaskar and Tongaonkar antigenicity method [24].

Discontinuous epitopes was predicted using DiscoTope server [25]. Parameter was set at ≥ 0.5 which indicated 90% specificity and 23% sensitivity. This method based on surface accessibility and amino acid statistics in a collected form dataset of discontinuous epitopes found out by X-ray crystallography of antigen/antibody protein buildings. Chimera software was used to display the position of predicted epitopes clusters on 3D structure of S protein [26].

2.6 T-cell Epitope Prediction

The T cell epitopes were predicted in human among different alleles of major histocompatibility complex class I (MHCI) and class II (MHCII). MHC-I binding epitopes were predicted using artificial neural networks [27, 28]. Peptide length was set as 9 amino acids. The half maximal inhibitory concentration (IC50) values of the peptides binding to MHC-I molecule was calculated and the epitope that had IC50 with binding affinity less or equal to 300 nm were suggested as promising candidate epitopes. MHC class II molecules was performed by the IEDB MHCII prediction tool at (http://tools.iedb.org/mhcii/) [21]. Human MHC class II alleles (HLA DR, HLADP and HLADQ) were used for MHCII binding predication. NN-align method was used with IC50 less or equal to 1000 nM [29].

2.7 Antigenicity, Allergenicity And Toxicity Of Epitopes

VaxiJen v2.0 server was used to predict the antigenicity of the conserved regions (http://www.jenner.ac.uk/VaxiJen) [30]. It was used with the default prediction parameters and a threshold value of 0.4. The in silico allergenicity prediction of epitopes was investigated using AllerTop v .2.0 (http://www.ddg-pharmfac.net/AllerTOP) [31]. While ToxiPred server was used to predict the toxicity of predicted epitopes. (http://crdd.osdd.net/raghava/toxinpred/) [32].

2.8 Homology Modeling:

IBV reference sequences was submitted to SWISS- MODEL (http:// swiss-model.expasy.org/) for protein structure homology modelling [33–37]. The protein sequences of BF alleles (BF2 *2101 and BF2*0401) were submitted to Raptor X server (http://raptorx.uchicago.edu/) to design their three D structures [38–40]. PEPFOLD3 server was used for homology modelling of MHCI epitopes (http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/) from amino acid sequences [41–43]. Chimera software 1.8 was used to visualize the 3D structures of the IBV reference sequences and BF alleles [26].

2.9 Molecular Docking

To perform molecular docking, 3D structure of MHCI epitopes and 3D modeled of both BF alleles were submitted simultaneously to PatchDock online autodock tools; an automatic server for molecular docking (https://bioinfo3d.cs.tau.ac.il/PatchDock/) [44]. Firedock was used to select the five top models [44]. Visualization of the result was performed using UCSF-Chimera software 1.8 [26].

3. Results

3.2 Multiple Sequence Alignment

Jalview was used to visualize the multiple sequence alignment of the retrieved sequences. In alignment, several areas have been shown to have mutation see Fig. 3.

3.3 Phylogeny

Phylogenetic trees for targeted proteins were constricted using MEGA7.0.26 (7170509) software using maximum likelihood parameter see Fig. 4.

3.4 B-cell Epitopes

In B cell prediction methods, several epitopes using Bepipred Linear Epitope Prediction method were predicted. The conservancy percentages of these epitopes were presented in Table 3. Twenty one linear conserved epitopes were identified after shortened of predicted epitopes. Of these, seven epitopes with different length between the positions 1139–1146 were identified as linear, surface and antigenic epitopes (see Table 4). These epitopes were ₁₁₃₉KKSSYY₁₁₄₄, ₁₁₄₀KSSYYT₁₁₄₅, ₁₁₄₁SSYYTT₁₁₄₆, ₁₁₄₁SSYYT₁₁₄₅, ₁₁₄₂SYYTT₁₁₄₆, ₁₁₄₂SYYT_1145, and ₁₁₄₃YYTT₁₁₄₆. Three epitopes (₁₁₃₉KKSSYY₁₁₄₄, ₁₁₄₀KSSYYT₁₁₄₅, ₁₁₄₁SSYYT₁₁₄₅) were selected as top B cell epitopes based on the length and antigenicity score.

Discotope 2.0 server was used to predict the discontinuous epitopes from the 3D structure of S protein (PDB ID: 6CV0), 90% specificity, − 3.700 threshold and 22.000 Angstroms propensity score radius [45]. Total 30 discontinuous epitopes were recognized at different exposed surface areas (Table 5). Position of each predicted epitope on surface of 3D structure of IBV S protein displayed in Fig. 5 using Chimera visualization tool [26].

3.5 Prediction Of MHC Class I Epitopes

In this study, Human MHC class-I HLA alleles were used to explore the interaction of epitopes with MHCI alleles using epitope prediction softwares as a result of the nonexistence of chicken MHC alleles in IEDB database. MHC-1 binding prediction tool using IEDB database expected thirteen conserved epitopes of Spike protein (S) which were interacted with many alleles in cytotoxic T cell. These epitopes were ₁₁₁₅FFMTGCCGC₁₁₂₃, ₅₉₀FNLTVTDEY₅₉₈, ₇₃₄GLLVLPPII₇₄₂, ₁₁₀₅IIFILILGW₁₁₁₃, ₁₁₃₉KKSSYYTTF₁₁₄₇, ₁₀₈₇KTYIKWPWY₁₀₉₅, ₁₆₆SVYLNGDLV₁₇₄, ₉₈₅TARDMYMPR₉₉₃, ₁₁₄₅TTFDNDVVT₁₁₅₃, ₉₈₃YITARDMYM₉₉₁, ₁₁₄₄YTTFDNDVV₁₁₅₂, ₉₈₂YYITARDMY₉₉₀, ₁₁₄₃YYTTFDNDV₁₁₅₁.

3.6 Prediction Of MHC Class II Epitopes

MHC-II binding prediction tool based on NN-align with half-maximal inhibitory concentration (IC50) ≤ 1000 was used. Thirty conserved core sequences were predicted to interact with MHCII alleles. These cores were ₆₉₄EDLLFTSVE₇₀₂, ₁₁₄₇FDNDVVTEQ₁₁₅₅, ₁₁₁₅FFMTGCCGC₁₁₂₃, ₁₁₁₆FMTGCCGCC₁₁₂₄, ₅₉₀FNLTVTDEY₅₉₈, ₇₃₄GLLVLPPII₇₄₂, ₁₁₀₅IIFILILGW₁₁₁₃, ₉₀₂INECVKSQS₉₁₀, ₉₈₄ITARDMYMP₉₉₂, ₉₀₁KINECVKSQ₉₀₉, ₁₁₃₉KKSSYYTTF₁₁₄₇, ₁₁₄₀KSSYYTTFD₁₁₄₈, ₁₁₈₇KTYIKWPWY₁₁₉₅, ₇₃₅LLVLPPIIT₇₄₃, ₅₉₂LTVTDEYIQ₆₀₀, ₁₀₁₄NKTVITTFV₁₀₂₂, ₈₉₃QQRELATQK₉₀₁, ₈₉₄QRELATQKI₉₀₂, ₈₉₅RELATQKIN₉₀₃, ₅₈₉SFNLTVTDE₅₉₇, ₁₁₄₁SSYYTTFDN₁₁₄₉, ₁₆₆SVYLNGDLV₁₇₄, ₁₁₄₂SYYTTFDND₁₁₅₀, ₉₈₅TARDMYMPR₉₉₃, ₁₁₄₆TFDNDVVTE₁₁₅₄, ₅₉₃TVTDEYIQT₆₀₁, ₁₀₁₃VNKTVITTF₁₀₂₁, ₉₈₃YITARDMYM₉₉₁, ₁₁₄₄YTTFDNDVV₁₁₅₂, ₉₈₂YYITARDMY₉₉₀ and ₁₁₄₃YYTTFDNDV₁₁₅₁.

3.7 Antigenicity, allergenicity and toxicity of MHCI and MHCII epitopes:

The predicted MHCI and MHCII epitopes were subjected to VaxiJen v2.0 server, AllerJen v2.0. and ToxiPred to predict the antigenicity, allergenicity and toxicity of predicted epitopes. Five MHCI epitopes were identified as antigenic, non-allergic and non-toxic, but only 3 epitopes showed high linkage with MHCI alleles which were ₉₈₅TARDMYMPR₉₉₃, ₉₈₃YITARDMYM₉₉₁ and ₉₈₂YYITARDMY₉₉₀ (Table 6). While six MHCII epitopes were predicted as antigenic, non-allergic and non-toxic epitopes (Table 7). However, ₉₈₃YITARDMYM₉₉₁ and ₉₈₂YYITARDMY₉₉₀ epitopes which were also presented in MHCII prediction methods, showed high antigenicity, no allergenicity and no toxicity. These epitopes were interacted with 52 and 38 alleles in MHCII see Fig. 6.

3.8 Molecular Docking

Molecular docking was achieved using peptide-binding groove affinity by docking MHCI alleles with chicken BF alleles (BF2*2101 & BF2*0401). The chicken alleles were used a receptors, and the top MHCI epitopes ₉₈₂YYITARDMY₉₉₀, ₉₈₃YITARDMYM₉₉₁ and ₉₈₅TARDMYMPR₉₉₃ were used as ligands. Docking of ₉₈₃YITARDMYM₉₉₁ epitope with BF2*2101 and BF₂*0401 alleles showed – 72.11 and – 37.39 global energy respectively which indicates the strong binding affinity between the ligands and the receptors compared to other epitopes (Figs. 7, 8 and 9). In general, the global binding affinity of ligands with the receptor BF2*2101 alleles was found to be lower compared to BF2*0401, which indicates strong interaction between the receptor and ligands.

Discussion:

Acquired immunity results in the activation of antigen-specific effector mechanisms including B-cells (humoral), T-cells (cellular) and macrophages, and the production of memory cells [4]. The use of B cells and T cells epitopes as a means to induce cellular and humoral immunity is likely to lead to broad based vaccines that could reduce the challenges in using of conventional attenuated vaccine [46]. There are several potential advantages offered by peptide vaccine over traditional organism vaccines. Most importantly, it allows the immune response to focus only on relevant epitopes and avoid those that lead to non-protective responses, immune evasion, or unwanted side effects, such as autoimmunity [47].

Vaccination studies with IBV have always focused on humoral immune responses in relation to protection [4]. Acquired immunity results in the activation of antigen-specific effector mechanisms including B-cells (humoral), T-cells (cellular) and macrophages, and the production of memory cells [4]. Chickens develop a good humoral response to IBV infections, which measured by ELISA, virus neutralizing (VN) and haemagglutination-inhibition HI antibodies tests [48]. It is known that S1 glycoprotein of IBV responsible of virus neutralizing (VN) and haemagglutination-inhibition HI antibodies and has been considered as the most likely inducer of protection [4].

Recently multi peptide vaccines using immunoinformatics tools was performed in Sudan for several viral diseases in chicken such as ILTV, fowlpox, Newcastle and marek's disease virus [49–52].

In the present study, various prediction servers were used to analyze spike protein of IBV. Protein characterization of IBV spike S protein using protparam confirmed that it is positive in nature, stable with good antigenic nature.

Corona-S1 and Corona S2 were identified in refseq of IBV spike glycoprotein as the main conserved domains. Conserved Domain (CDD) BLAST search revealed that corona-S1 (pfam01600) is the only member of the superfamily cl03276 and corona-S2 domain (pfam01601) is the only member of the superfamily cl20218. The main related sequences in both domains were Feline infectious peritonitis virus (strain 79-1146), Avian infectious bronchitis virus (strain Beaudette), and Human coronavirus 229E while Severe acute respiratory syndrome-related coronavirus sequences was only associated to corona-S2 domain [53]. BLASTP was also used to find the association between different coronaviruses in human and animals. It revealed that the close homologue was the Turkey coronavirus S protein followed by Murine hepatitis virus strain JHM.

Prediction of B-cell epitopes is essential to design vaccine components and immuno-diagnostic reagents. B-cell antigenic epitopes are classified as either continuous or discontinuous. The majority of available epitope prediction methods focus on continuous epitopes [54]. Linear B cell epitopes have been reported to play a role in virus neutralization [46]. IEDB prediction tool was used to predict linear, surface and antigenic epitopes based on the properties of amino acids such as hydrophilicity, surface accessibility, flexibility, and antigenicity [49].

In this study, seven shortened conserved epitopes (₁₁₃₉KKSSYY₁₁₄₄, ₁₁₄₀KSSYYT₁₁₄₅, ₁₁₄₁SSYYTT₁₁₄₆, ₁₁₄₁SSYYT₁₁₄₅, ₁₁₄₂SYYTT₁₁₄₆, ₁₁₄₂SYYT_1145, and ₁₁₄₃YYTT₁₁₄₆) were predicted from B cell prediction methods as surface, linear and antigenic epitopes. These epitopes were adjacent to each other from the position 1139–1146. In a similar study, using BepiPred epitope prediction server version 1, only one epitope (YTSNETTDVTS^175–185) was predicted within the S1 glycoprotein of M41 IBV strains and three such epitopes (VSNASPNSGGVD^279–290, HPKCNFRPENI^328–338, NETNNAGSVSDCTAGT^54–69 ) were predicted in CR88 IBV strains [46].

About a 90% of B-cell epitopes are conformational and only a minority of native antigens contains linear B-cell epitopes [55]. Discotope server was used to predict discontinuous epitopes from the 3D structure of the spike IBV reference sequence. The server expected 30 discontinuous epitopes that were recognized at different exposed surface areas with 90% specificity. These epitopes have a major advantage in identifying the native well-structured protein Ag [56].

Cytotoxic T lymphocytes (CTL) provide a critical arm of the immune system in eliminating autologous cells expressing foreign antigen. Unlike humoral immunity, the specificity of CTL activation depends on membrane receptors rather than secreted molecules, and antigen receptors of CTL interact with peptide determinants only in association with matched major histocompatibility complex (MHC) molecules. Virus-specific CTL have been shown to be important, if not critical, for resolution of infection and elimination of viral shedding [1].

It is stated that, the major histocompatibility complex MHC restricted CTL response can be associated with decreases in viral load, CD8⁺ lymphocytes were mostly responsible for the observed protection [1, 57]. Cytotoxic T-lymphocyte (CTL) responses to infectious bronchitis virus (IBV) were determined at regular intervals between 3 and 30 days post infection [1].

However, MHCI prediction methods showed three conserved CTL epitopes ₉₈₅TARDMYMPR₉₉₃, ₉₈₃YITARDMYM₉₉₁ and ₉₈₂YYITARDMY₉₉₀ as they linkage with 7 and 3 human MHCI alleles respectively and showed high antigenicity, no allergenicity and no toxicity. Recent studies showed that vigorous cytotoxic T lymphocyte (CTL) responses that correlate with initial decrease in infection and illness can be detected after IBV infection [57]. It was established that the CD8⁺ T cells were exhausted without CD4⁺ helper T cells. CD4 ⁺ T cells do not appear to be important in initially resolving IBV infection in chickens [57].

In MHCII prediction method, several core peptides were predicted to interact with MHCII alleles, but surprisingly the top core peptides were also ₉₈₃YITARDMYM₉₉₁ and ₉₈₂YYITARDMY₉₉₀ which were presented in MHCI prediction methods. They linked with 52 and 38 human alleles respectively. These epitopes were showed high antigenicity, no allergincity and no toxicity.

Molecular docking was performed to display the interaction between BF alleles (BF2*2101 & BF2*0401) and predicting MHCI epitopes (₉₈₂YYITARDMY₉₉₀, ₉₈₃YITARDMYM₉₉₁ and ₉₈₅TARDMYMPR₉₉₃). The 3D structures of MHC class I binding peptides were designed using PEPFOLD and docked with BF alleles via Patchdock server. Docking of ₉₈₃YITARDMYM₉₉₁ epitope with both BF2 alleles produced strong binding affinity (− 72.11 and − 37.97 global energy respectively) followed by ₉₈₂YYITARDMY₉₉₀ (− 64.68 and – 37.57 global energy respectively). This indicates the strong interaction between the ligand and the receptor compared to other epitopes (see Figs. 5, 6 and 7). The interaction of ligands with the receptor BF2*2101 alleles was stronger compared to BF2*0401. The predicted epitopes should be tested for therapeutic potency in future studies to prove their safety and efficacy.

Conclusion:

Peptide vaccine was found to be an effective and powerful approach to a variety of pathogens. Peptides may have the potential to act as safe, non-infective, well-specific, stable vaccines. Peptide-based vaccine can correspond to highly conserved regions required for the pathogen's function and can elicit both humoral and cellular immune responses.

In this study, five epitopes were predicted from spike glycoprotein of IBV as the best B cell (₁₁₃₉KKSSYY₁₁₄₄, ₁₁₄₀KSSYYT₁₁₄₅ and ₁₁₄₁SSYYT₁₁₄₅) and T cell epitopes (₉₈₂YYITARDMY₉₉₀ and ₉₈₃YITARDMYM₉₉₁) due to high antigenicity, no allergenicity and no toxicity as well as great linkage of MHC epitopes with MHCI MHCII alleles. These epitopes should be designed and incorporated and then tested as multi-epitope vaccine against IBV and may act as a potential peptide vaccine to control IBV infection in chicken by inducing humoral and cellular responses.

Peptide vaccine against IBV spike protein (S) is strongly supersedes the conventional vaccines, as it designed to cover all strains in different serotypes, which might be reduce the frequent outbreaks and their huge accompanied economical loss to a minimum.

Declarations

Acknowledgements

Not applicable.

Authors’ contributions

Eman, A. Awadelkareem and Sumaia A. Ali designed this study, accomplished the experiments, analyze the results, interpreted the data and wrote the manuscript. All authors read and approved the final manuscript.

Funding

Not applicable.

Availability of data and materials

All the data supporting the findings are contained within the manuscript

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

All manuscripts must contain the following sections under the heading 'Declarations':

Authors' details

¹Faculty of Veterinary Medicine, University of Khartoum, Khartoum, Sudan. ²Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, Sudan University of Science and Technology, Khartoum, Sudan.

Abbreviations

IB: Infectious bronchitis; IBV: Infectious Bronchitis Virus; IEDB: Immune Epitope Database; S: spike; MHC: major histocompatibility complex; BF: The genetic polymorphism of properdin factor B; refseq: reference sequence; NCBI: National Central Biotechnology Information; MSA: multiple sequence alignment; GRAVY; Grand average of hydropathicity; CDD: Conserved Domain Database; IC50: The half maximal inhibitory concentration; ANN: artificial neural networks; NN-align: artificial neural network-based alignment; HLA: The human leukocyte antigen; CTL: Cytotoxic T lymphocytes.

References

1. Seo SH, Collisson EW. Specific cytotoxic T lymphocytes are involved in in vivo clearance of infectious bronchitis virus. Journal of virology. 1997;71(7):5173-7.
2. Cavanagh D. Coronavirus avian infectious bronchitis virus. Veterinary research. 2007;38(2):281-97.
3. Ignjatovic J, Sapats S. Avian infectious bronchitis virus. Revue Scientifique et Technique-Office International des Epizooties. 2000;19(2):493-501.
4. Raj GD, Jones R. Infectious bronchitis virus: immunopathogenesis of infection in the chicken. Avian Pathology. 1997;26(4):677-706.
5. Yang T, Wang H-N, Wang X, Tang J-N, Lu D, Zhang Y-F, et al. The protective immune response against infectious bronchitis virus induced by multi-epitope based peptide vaccines. Bioscience, biotechnology, and biochemistry. 2009:0906031485-.
6. Abro SH, Ullman K, Belák S, Baule C. Bioinformatics and evolutionary insight on the spike glycoprotein gene of QX-like and Massachusetts strains of infectious bronchitis virus. Virology journal. 2012;9(1):211.
7. Abdel-Moneim A, Madbouly H, Gelb J, Ladman B. Isolation and identification of Egypt/Beni-Seuf/01 a novel genotype of infectious bronchitis virus. VETERINARY MEDICAL JOURNAL-GIZA-. 2002;50(4):1065-78.
8. Mo M-L, Hong S-M, Kwon H-J, Kim I-H, Song C-S, Kim J-H. Genetic diversity of spike, 3a, 3b and e genes of infectious bronchitis viruses and emergence of new recombinants in Korea. Viruses. 2013;5(2):550-67.
9. Gasteiger E GA, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 2003;31:3784–8.
10. Buchan DW MF, Nugent TC, Bryson K, Jones DT. . Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res 2013;41:W349–57.
11. F. Ferre and P. Clote. Disulfide connectivity prediction using secondary structure information and diresidue frequencies. Bioinformatics 2005;;21(10):2336-46.
12. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research. 1997;25(17):3389-402.
13. Altschul SF1 MT, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25((17):):3389-402.
14. Marchler-Bauer A1 AJ, Derbyshire MK, DeWeese-Scott C, Gonzales NR, Gwadz M, Hao L, He S, Hurwitz DI, Jackson JD, Ke Z, Krylov D, Lanczycki CJ, Liebert CA, Liu C, Lu F, Lu S, Marchler GH, Mullokandov M, Song JS, Thanki N, Yamashita RA, Yin JJ, Zhang D, Bryant SH. CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res. 2007;35:(Database issue):D237-40. Epub 2006 Nov 29.
15. SR1. E. Profile hidden Markov models. Bioinformatics.1998;14(9):755-63.
16. Bateman A1 BE, Cerruti L, Durbin R, Etwiller L, Eddy SR, Griffiths-Jones S, Howe KL, Marshall M, Sonnhammer EL. The Pfam protein families database. Nucleic Acids Res. 2002; 30(1):276-80.
17. Altschul SF, Wootton JC, Gertz EM, Agarwala R, Morgulis A, Schäffer AA, et al. Protein database searches using compositionally adjusted substitution matrices. The FEBS journal. 2005;272(20):5101-9.
18. Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25(9):1189-91.
19. Bui H-H, Sidney J, Li W, Fusseder N, Sette A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC bioinformatics. 2007;8(1):361.
20. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Molecular biology and evolution. 2016;33(7):1870-4.
21. Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD, Cantrell JR, et al. The immune epitope database (IEDB) 3.0. Nucleic acids research. 2014;43(D1):D405-D12.
22. Larsen JE, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome research. 2006;2(1):2.
23. Emini EA, Hughes JV, Perlow D, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of virology. 1985;55(3):836-9.
24. Kolaskar A, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS letters. 1990;276(1-2):172-4.
25. Sun P, Ju H, Liu Z, Ning Q, Zhang J, Zhao X, et al. Bioinformatics resources and tools for conformational B-cell epitope prediction. Computational and mathematical methods in medicine. 2013;2013.
26. Chan WM, Rogers SE, Nash SM, Buning PG, Meakin R. User’s manual for Chimera grid tools, version 1.8. NASA Ames Research Center, URL: http://people nas nasa gov/~ rogers/cgt/doc/man html [cited 19 July 2006]. 2003.
27. Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open biology. 2013;3(1):120139.
28. Abdelbagi M, Hassan T, Shihabeldin M, Bashir S, Ahmed E. Immunoinformatics Prediction of Peptide-Based Vaccine Against African Horse Sickness Virus. Immunome Res. 2017;13(135):2.
29. Nielsen M, Lund O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC bioinformatics. 2009;10(1):296.
30. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics. 2007;8(1):4.
31. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v. 2—a server for in silico prediction of allergens. Journal of molecular modeling. 2014;20(6):2278.
32. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP, et al. In silico approach for predicting toxicity of peptides and proteins. PloS one. 2013;8(9).
33. Waterhouse A, Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F.T., de Beer, T.A.P., Rempfer, C., Bordoli, L., Lepore, R., Schwede, . T. SWISS-MODEL: homology modelling of protein structures and complexes. . Nucleic Acids Res 2018;46(W1):W296-W303
34. Bienert S, Waterhouse, A., de Beer, T.A.P., Tauriello, G., Studer, G., Bordoli, L., Schwede, T. . The SWISS-MODEL Repository - new features and functionality. . Nucleic Acids Res. 2017;45: D313-D9. .
35. Guex N, Peitsch, M.C., Schwede, T. . Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. . Electrophoresis,. 2009;30: S162-S73.
36. Benkert P, Biasini, M., Schwede, T. . Toward the estimation of the absolute quality of individual protein structure models. . Bioinformatics,. 2011;27, :343-50.
37. Bertoni M, Kiefer, F., Biasini, M., Bordoli, L., Schwede, T. . Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. . Scientific Reports. 2017;7.
38. Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, et al. Template-based protein structure modeling using the RaptorX web server. Nature protocols. 2012;7(8):1511.
39. Peng J, Xu J. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins: Structure, Function, and Bioinformatics. 2011;79(S10):161-71.
40. Peng J, Xu J. A multiple‐template approach to protein threading. Proteins: Structure, Function, and Bioinformatics. 2011;79(6):1930-9.
41. Maupetit J, Derreumaux P, Tufféry P. A fast method for large‐scale De Novo peptide and miniprotein structure prediction. Journal of computational chemistry. 2010;31(4):726-38.
42. Beaufays J, Lins L, Thomas A, Brasseur R. In silico predictions of 3D structures of linear and cyclic peptides with natural and non‐proteinogenic residues. Journal of Peptide Science. 2012;18(1):17-24.
43. Shen Y, Maupetit J, Derreumaux P, Tufféry P. Improved PEP-FOLD approach for peptide and miniprotein structure prediction. Journal of chemical theory and computation. 2014;10(10):4745-58.
44. Andrusier N, Nussinov R, Wolfson HJ. FireDock: fast interaction refinement in molecular docking. Proteins: Structure, Function, and Bioinformatics. 2007;69(1):139-59.
45. Shang J, Zheng Y, Yang Y, Liu C, Geng Q, Luo C, et al. Cryo-EM structure of infectious bronchitis coronavirus spike protein reveals structural and functional evolution of coronavirus spike proteins. PLoS pathogens. 2018;14(4):e1007009.
46. Bande F, Arshad SS, Hair Bejo M, Kadkhodaei S, Omar AR. Prediction and in silico identification of novel B-cells and T-cells epitopes in the S1-spike glycoprotein of M41 and CR88 (793/B) infectious bronchitis virus serotypes for application in peptide vaccines. Advances in bioinformatics. 2016;2016.
47. Reche PA, Fernandez-Caldas E, Flower DR, Fridkis-Hareli M, Hoshino Y. Peptide-based immunotherapeutics and vaccines. Journal of immunology research. 2014;2014.
48. Sylvester SA, Dhama K, Kataria J, Rahul S, Mahendran M. Avian infectious bronchitis: A review. Indian Journal of Comparative Microbiology Immunology and infectious Diseases. 2005;26:1-14.
49. Ali SA, Almofti YA, Abd-elrahman KA. Immunoinformatics Approach for Multiepitopes Vaccine Prediction against Glycoprotein B of Avian Infectious Laryngotracheitis Virus. Advances in Bioinformatics. 2019;2019.
50. Idris S, Salih S, Basheir M, Elhadi A, Kamel S, Abd-elrahman K, et al. In silico Prediction of Peptide based Vaccine against Fowlpox Virus (FPV). Immunome Research. 2018;14(2):1-11.
51. Badawi MM, Fadl Alla A, Alam SS, Mohamed WA, Osman D, Alrazig Ali S, et al. Immunoinformatics predication and in silico modeling of epitope-based peptide vaccine against virulent Newcastle disease viruses. Am J Infectious Dis Microbiol. 2016;4(3):61-71.
52. Bashir S, Abd-elrahman KA, Hassan MA, Almofti YA. Multi Epitope Based Peptide Vaccine against Marek’s Disease Virus Serotype 1 Glycoprotein H and B. American Journal of Microbiological Research. 2018;6(4):124-39.
53. Gen. J. Cloning and sequencing of the gene encoding the spike protein of the coronavirus IBV. Virol. 1985;66( ( Pt 4)):719-26.
54. Yao B, Zheng D, Liang S, Zhang C. Conformational B-cell epitope prediction on antigen protein structures: a review of current algorithms and comparison with common binding site prediction methods. PloS one. 2013;8(4).
55. Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA. Fundamentals and methods for T-and B-cell epitope prediction. Journal of immunology research. 2017;2017.
56. Demolombe V, de Brevern AG, Felicori L, NGuyen C, de Avila RAM, Valera L, et al. PEPOP 2.0: new approaches to mimic non-continuous epitopes. BMC bioinformatics. 2019;20(1):387.
57. Pei J, Briles WE, Collisson EW. Memory T cells protect chicks from acute infectious bronchitis virus infection. Virology. 2003;306(2):376-84.