Identification of T-Cell Epitopes in Proteins of Novel Human Coronavirus, SARS-Cov-2 for Vaccine Development

Indroduction

Coronaviruses (CoV) belong to the family Coronaviridae [1]. Recombination rates of CoVs are very high because of constantly developing transcription errors and RNA Dependent RNA Polymerase (RdRP) jumps [2]. Initially CoVs were not considered pathogenic for humans until the severe acute respiratory syndrome (SARS) was found in the Guangdong state of China (2002 – 2003). two types of CoV such as CoV OC43, CoV 229E that have mostly caused mild infections in people with a responsive immune system [3, 4]. Approximately ten years after SARS, another highly pathogenic CoV, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) had emerged in the Middle East countries [5]. In December 2019, the novel Coronavirus (nCoV), which caused another public health problem, has emerged in the Huanan Seafood Market, where livestock animals are generally traded in Wuhan State of Hubei Province in China and has been the focus of global attention due to a pneumonia epidemic of unknown etiology [6]. Primarily, an unknown pneumonia case was detected on December 12, 2019. Aftermath, Chinese authorities announced on January 7, 2020 that a new type of Coronavirus (novel Coronavirus, nCoV) was isolated [7]. This virus was named as 2019-nCoV by WHO on January 12 and COVID-19 on 11 February 2020. According to WHO, after quick spread in china in very short time span; globally 1,056,159 peoples are Corona positive with 57,206 deaths across 207 countries up to 4^th April 2020. The probable initial infection was transmitted by zoonotic agent (from animal to human). The increase in the number of cases in Wuhan city of China and across world has showed a second transmission from human-to-human and no COVID-19 vaccine has been successfully developed yet. In the meantime, WHO declared it as pandemic on 11^th March 2020.

Corona virus’s genome structure is best among all RNA viruses. Two-thirds of RNA encodes viral polymerase (RdRp), RNA synthesis materials, and two large non-structural polyproteins, that are not reported involved in host response modulation (ORF1a-ORF1b). The other one-third of the genome encodes four structural proteins (spike (S), envelope (E), membrane (M) nucleocapsid (N)), and the other helper proteins. Although the length of the CoV genome shows high variability for ORF1a/ORF1b with four structural proteins, it is mostly associated with the number and size of accessory proteins [8, 9]. The first step in virus infection is the interaction of Spike Protein with human cells. The coronavirus spike protein is a multifunctional molecular machine that mediates coronavirus entry into host cells. It first binds to a receptor on the host cell surface through its S1 subunit and then fuses viral and host membranes through its S2 subunit. It's been reported that 2019-nCoV can infect the human respiratory epithelial cells through interaction with the human ACE2 receptor [10]. Genome encoding occurs after entering to the cell and facilitates the expression of the genes that encodes useful accessory proteins, which advances the adaptation of CoVs to their human host [9].

Immunoinformatics is a branch of bioinformatics primarily concerned with in silico analysis and modelling of immunological data and problems. It stresses the research mostly on the design and study of algorithms for mapping potential B- and T-cell epitopes, which shorten the time and lowers the cost needed for laboratory analysis of pathogen gene products. Several immunoinformatics tools are available for prediction and mapping of antigenic epitopes in protein sequence. It assists in designing subunit vaccines that starts from prediction of antigenic epitope through in silico techniques from protein sequence of pathogens independent of their abundance [11, 12]. Evaluation of synthetic peptides as potential vaccine candidate for flavivirus has been investigated. Using the computational tools for prediction of epitopes and synthetic peptides from E glycoprotein of Murray Valley encephalitis (MVE) and DEN 2 viruses were prepared and their immunogenicity was evaluated in mice [13]. The identification of significant T-cells epitopes from secretory and cell surface proteins virulent proteins of M. tuberculosis H37Rv strain was done. The promiscuous nanomer candidate epitopes from HTL and CTL were recognized [14]. T-cell analyses of synthetic peptides to other viruses have correlated the association between T- and B-cell responses [15]. A new approach for vaccine design in immunology and the development of bioinformatics tools for T cell epitope prediction from primary protein sequences is essential. The primary focus of present study is to identify and map of the specific epitopes from five different proteins of SARS-CoV-2.

Material And Methods

Retrieve Target Sequence: The FASTA formatted amino acid sequences of SARS-CoV-2 were retrieved from the NCBI GenBank (http://www.ncbi.nlm.nih.gov/genbank/) in this study. We have screened the specific proteins such as ORF1ab polyprotein, surface glycoprotein, membrane glycoprotein and nucleocapsid phosphoprotein nucleoprotein sequences were primarily selected for antigenicity prediction.

Physical properties of Protein Identification: To determine physical protein of SARS-CoV-2, the FASTA formatted amino acid sequences of total structural proteins were submitted to Generunner and ExPaSy (http://www.expasy.org ). The expected molecular weight and isoelectric point (pI) values were calculated.

Identification of T-Cell Epitope: The T-cell epitopes are typically peptide fragments which are immunodominant and can elicit specific immune responses, important for epitope-based peptide vaccine design. Due to the importance of T-cell epitopes, we used Propred (https://webs.iiitd.edu.in/raghava/propred/index.html) and Propred1 (https://webs.iiitd.edu.in/raghava/propred1/index.html) immunoinformatics tools which are available for prediction of epitopes in the protein primary sequences.

The server uses to read the input sequence, thus it can accept most commonly used standard sequence formats (FASTA). The sequence can be uploaded, from a file by using the cut and paste option. Users can customize these servers by selecting single/multiple allele, threshold and other parameter in order to achieve desirable results. The server analyses sequence data and generates output as text or graphics. These tools cover maximum number of human leukocyte antigen (HLA) comparison to other epitopes prediction tools. We have considered the parameters during epitopes prediction such as 3% threshold with maximum binding score to HLA molecules [16,17].

Results:

In the present study, four proteins of novel human coronavirus, SARS-CoV-2 were used for the physicochemical analysis such as molecular weight, isoelectric point (pI value) and antigenic nature. Nucleocapsid proteins showed highest molecular weight with 13.13 kDa and the lowest molecular weight with 3.93 kDa of surface glycoprotein. Isoelectric point of proteins was ranged between 4.46 to 9.98. The physicochemical properties of four proteins were given (Table 1). The pI value of protein is indicated the stability of protein at that particular pI.

Table 1: Physicochemical properties of different proteins of SARS-CoV-2

In this study, putative epitopes of ORF1ab polypeptide, surface glycoprotein, membrane glycoprotein and nucleocapsid phosphoprotein in SARS-CoV-2 were identified. Total 36 epitopes were predicted for class I MHC and 25 epitopes for class II MHC molecules in these proteins. (Table 2).

Table 2: Most potential 36 T-cell epitopes with interacting MHC-I alleles and 25 T-cell epitopes with interacting MHC-II alleles epitope of SARS-CoV-2

Discussion

In recent years, many diseases have emerged due to the occurrence of several outbreaks through the different types of newer viruses. So, vaccine development against these emerging diseases within a short time is very crucial to protecting the people from the rising viral attacks. Vaccines are the pharmacological products which can provide the finest cost-benefit ratio in the prevention or treatment of diseases. However, an effective vaccine progression and production are costly and can take years to be completed. So, the researchers have tried for many years to minimize the cost and time for the development of vaccines. At this time, there are different strategies available for the design and development of effective and safe new-generation vaccines, based on the Bioinformatics approaches [18, 19]. The next-generation sequencing and progressive genomics and proteomics technologies have brought about a great change in computational immunology. However, the advancement of newer immunoinformatic tools has made a broader way in developing the vaccine or vaccine candidates through the satisfactory understanding of the immune response of the human body against an organism within a short time [20–22].

The epitope is recognizable by the immune system as a part of the antigen, and in particular by antibodies, B cells or T cells. The epitopes may belong to both foreign and self-proteins, and they can be categorized as conformational or linear, depending on their structure and integration with the paratope [23]. T-cell epitopes are presented on the surface of an antigen presenting cell (APC), where they are bound to major histocompatibility (MHC) molecules in order to induce immune response [24]. MHC class I molecules usually present peptides between 8 to 11 amino acids in length, whereas the peptides binding to MHC class II may have length from 12 to 25 amino acids [25]. If sufficient quantities of the epitope are presented, the T cell may trigger an adaptive immune response specific for the pathogen. Class II MHCs are expressed on specialized cell types, including professional APCs such as B cells, macrophages and dendritic cells, whereas class I MHCs are found on every nucleated cell of the body [26]. The recognition of epitopes by T cells and the induction of immune response have a key role for the individual’s immune system. Even the slightest deviation from the normal functioning can have a grave impact on the organism. Knowledge about the peptide’s epitopes has a key role for manufacturing epitope-based vaccines. One of the key issues in T-cell epitope prediction is the prediction of MHC binding, as it is considered a prerequisite for T cell recognition. All T-cell epitopes are good MHC binders, but all good MHC binders are not T-cell epitopes. In the present study, an immunoinformatic-driven approach was incorporated to screen emergent immunogen against SARS-CoV-2 proteome. The results revealed that the SARS-CoV-2 of total 36 epitopes were predicted for class I MHC and 25 epitopes for class II MHC molecules in these proteins. Till date, no effective immunoinformatics study for the SARS-CoV-2 polyprotein has been performed for the identification of a potential vaccine target. However, we identified the potential T-cell epitopes from the all antigenic protein of SARS-CoV-2, as they play a key role in the creation of a defensive immune response against different pathogenic infections [27]. Various successful studies have been performed for the epitope based peptide vaccine design against West Nile virus [28], Zika virus [29], dengue virus [30], Chikungunya virus [31], Rift valley fever virus [32], shigellosis [33], and so on.

Conclusion

Immunoinformatics is a newer strategy for identification and mapping of epitopes in the protein sequences of novel human coronavirus, SARS-CoV-2 exclusive of the virus culture. The predicted SARS-CoV-2 nanomer epitopes for T-cell is recognized against MHC Class II and MHC class I may be useful for development of sensitive, rapid and cost effective diagnosis. Further, these epitopes of SARS-CoV-2 may be served as vaccines candidates for prevention of disease.

Funding

This work is not supported by any funding.

Conflict Of Interest

The authors report no conflicts of interest in this work.

References:

1. Woo PC, Huang Y, Lau SK, et al. Coronavirus genomics and bioinformatics analysis. Viruses 2 (2010): 1804-1820.
2. Drexler JF, Gloza-Rausch F, Glende J, et al. Genomic characterization of severe acute respiratory syndrome-related coronavirus in European bats and classification of coronaviruses based on partial RNA-dependent RNA polymerase gene sequences. Journal of virology 84 (2010): 11336-11349.
3. Yin Y, Wunderink RG. MERS, SARS and other coronaviruses as causes of pneumonia. Respirology 23 (2018): 130-137.
4. Peiris JS, Lai ST, Poon LL, et al. Coronavirus as a possible cause of severe acute respiratory syndrome. The Lancet 361 (2003): 1319-1325.
5. Zaki AM, Van Boheemen S, Bestebroer TM, et al. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. New England Journal of Medicine 367 (2012): 1814-1820.
6. Seven days in medicine. BMJ 368 (2020).
7. Imperial College London. Report 2: estimating the potential total number of novel coronavirus cases in Wuhan City, China. Jan 2020.

 https://www.imperial.ac.uk/mrc-globalinfectiousdisease-analysis/news--wuhan-coronavirus.

8. Luk HK, Li X, Fung J, et al. Molecular epidemiology, evolution and phylogeny of SARS coronavirus. Infection, Genetics and Evolution (2019)
9. Coronavirinae in ViralZone. Available online: https://viralzone. expasy.org/785 (accessed on 05 February 2019).
10. Li F. Structure, function, and evolution of coronavirus spike proteins. Annual review of virology 3 (2016): 237-261.
11. Groot AS, Rappuoli R. Genome-derived vaccines. Expert review of vaccines 3 (2004): 59-76.
12. Rappuoli R. Reverse vaccinology, a genome-based approach to vaccine development. Vaccine 19 (2001): 2688-2691.
13. Gao XM, Liew FY, Tite JP. A dominant Th epitope in influenza nucleoprotein. Analysis of the fine specificity and functional repertoire of T cells recognizing a single determinant. The Journal of Immunology 144 (1990): 2730-2737.
14. Somvanshi P, Singh V, Seth PK. In silico prediction of epitopes in virulence proteins of Mycobacterium tuberculosis H37Rv for diagnostic and subunit vaccine design. J Proteomics Bioinform 1 (2008): 143-153.
15. Hu GJ, Wang RY, Han DS, et al. Characterization of the humoral and cellular immune responses against hepatitis C virus core induced by DNA-based immunization. Vaccine 17 (1999): 3160-3170.
16. Singh H, Raghava GP. ProPred: prediction of HLA-DR binding sites. Bioinformatics 17 (2001): 1236-1237.
17. Singh H, Raghava GP. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 19 (2003): 1009-1014.
18. María RR, Arturo CJ, Alicia JA, et al. The impact of bioinformatics on vaccine design and development. InTech, Rijeka, Croatia; (2017).
19. Seib KL, Zhao X, Rappuoli R. Developing vaccines in the era of genomics: a decade of reverse vaccinology. Clinical Microbiology and Infection 18 (2012): 109-116.
20. Groot AS, Rappuoli R. Genome-derived vaccines. Expert review of vaccines 3 (2004): 59-76.
21. Korber B, LaBute M, Yusim K. Immunoinformatics comes of age. PLoS Computational Biology 2 (2006).
22. Purcell AW, McCluskey J, Rossjohn J. More than one reason to rethink the use of peptides in vaccine design. Nature reviews Drug discovery 6 (2007): 404-414.
23. Huang J, Honda W. CED: a conformational epitope database. BMC immunology 7 (2006): 7.
24. Madden DR. The three-dimensional structure of peptide-MHC complexes. Annual review of immunology 13 (1995): 587-622.
25. Jardetzky TS, Brown JH, Gorga JC, et al. Crystallographic analysis of endogenous peptides associated with HLA-DR1 suggests a common, polyproline II-like conformation for bound peptides. Proceedings of the National Academy of Sciences 93 (1996): 734-738.
26. Janeway CA, Travers P, Walport M, et al. Immunobiology: the immune system in health and disease, 5th edn. Garland Science. New York (2001).
27. Esser MT, Marchese RD, Kierstead LS, et al. Memory T cells and vaccines. Vaccine 21 (2003): 419-430.
28. Larsen MV, Lelic A, Parsons R, et al. Identification of CD8+ T cell epitopes in the West Nile virus polyprotein by reverse-immunology using NetCTL. PloS one 5 (2010).
29. Alam A, Ali S, Ahamad S, et al. From ZikV genome to vaccine: in silico approach for the epitope?based peptide vaccine against Zika virus envelope glycoprotein. Immunology 149 (2016): 386-99.
30. Chakraborty S, Chakravorty R, Ahmed M, et al. A computational approach for identification of epitopes in dengue virus envelope protein: a step towards designing a universal dengue vaccine targeting endemic regions. In silico biology 10 (2010): 235-246.
31. Hasan MA, Khan MA, Datta A, et al. A comprehensive immunoinformatics and target site study revealed the corner-stone toward Chikungunya virus treatment. Molecular immunology 65 (2015): 189-204.
32. Adhikari UK, Rahman MM. Overlapping CD8+ and CD4+ T-cell epitopes identification for the progression of epitope-based peptide vaccine from nucleocapsid and glycoprotein of emerging Rift Valley fever virus using immunoinformatics approach. Infection, Genetics and Evolution 56 (2017): 75-91.
33. Oany AR, Pervin T, Mia M, et al. Vaccinomics approach for designing potential peptide vaccine by targeting Shigella spp. serine protease autotransporter subfamily protein SigA. Journal of immunology research (2017).