Immunome Research
Open Access

Immunoinformatics Approach for Designing an Epitope-Based Peptide Vaccine against Treponema pallidum Outer Membrane Beta-Barrel Protein

Mustafa Elhag^1,5*, Moaaz Mohammed Saadaldin^2,5, Abdelrahman Hamza Abdelmoneim^3,5, Taha Salah Taha^2,5, Fatima Abdelrahman^4,5 and Mohammed A Hassan^{4,5 *}Correspondence: Mustafa Elhag, Department of Bioinformatics, Africa City of Technology, Sudan, Faculty of Medicine, University of Seychelles-American Institute of Medicine, Seychelles,

Author info »

Introduction

Syphilis is chronic inflammatory discompose antecedent transmitted sexually by the spirochete Treponema pallidum [1-8]. There are four subspecies for Treponema (i.e., subsp. pallidum [syphilis], subsp. endemicum [bejel], and subsp. pertenue [yaws]) and T. carateum (pinta) [9,10]. T. pallidum is vastly motile extracellular bacterium distinguished for its invasiveness, immuno-evasiveness and persistence [11]. Based on data from 2016, the WHO assessments showed that approximately 90 million people who were from China [12], Africa, Asia and the Western Pacific are currently at risk of yaws. Globally, around eleven million [2,10,11,13] rates among commercial sex workers are between 23% and 32%. In the United States [2], people were diagnosed with syphilis in 2008, with mother-tochild conduction occurring in nearly two million pregnancies [14]. Syphilis influence in statement progress to affect the cardiovascular and central nervous systems of infected individuals, potentially leading to aortic aneurism, hearing or visual loss, dementia, paralysis, and stroke-like syndrome. T. pallidum has the facility to stimulate corresponding innate and adaptive immune procedures in blood and tissue that could set the period for the bidirectional transmission of HIV [3,15]. The diagnosis of syphilis principally relies on serological testing for reactivity to treponemal and non-treponemal (cardiolipin) antigens. Indirect detection or examination of cerebrospinal fluid (CSF) of the patients can be used. However, they are often insensitive and difficult to enter [13]. Direct detection methods like dark-field microscopy, direct fluorescent antibody testing and nucleic acid amplification testing [16].

The hidden mechanism within syphilis remains ambiguous due to the main causative agent of the disease which cannot be cultured in the laboratory. So manipulation of genetic approach is important [17,18]. Gram negative bacteria are characterized by a unique membrane of integral protein that is divided into β-barrel structure consisting of eight to twenty six antiparallel amphipathic strands [19,20]. The main function of the β–barrel outer membrane (OMPs) dynamic for membrane biogenesis and transport signaling, enzyme and receptor [21,22]. β -Barrel outer membrane protein is critical target to conserved domain region and contains N-and C-terminal domains (TprCN and TprCC, respectively) [11].

Currently, the medical research has improved by immunoinformatics technology and usage of such technologies has provided epitopes for creating chimeric, multi-epitope vaccine for several organisms like Hepatitis B Virus or Escherichia coli [23]. Proof of identity of B-cell epitopes acting an important role in development of epitopebased vaccines, therapeutic antibodies, and diagnostic tools and T-cell epitopes are shares of intracellular dealing out antigens that are presented to T lymphocytes in association with molecules of the key histocompatibility complex [23,24].

Syphilis vaccination studies are still in the elementary/preclinical stage. The aim of this study is to apply immunoinformatics on β-barrel outer membrane protein to design a safe and effective vaccine. No previous reports were found so this may be considered the first study to use in silico approach to design an epitope-based vaccine [25].

Materials and Methods

Protein sequence retrieval

A total of 66 Treponema pallidum outer membrane beta-barrel protein strains were retrieved from National Center for Biotechnology Information (NCBI) database on July 2019 in FASTA format. These strains were obtained from different parts of the world for immunoinformatics analysis. The retrieved protein strains had length of 219 amino acid.

Determination of conserved regions

The retrieved sequences of Treponema pallidum outer membrane beta-barrel protein were subjected to multiple sequence alignment (MSA) by blasting them against reference sequence (WP_010882178.1) using ClustalW tool of BioEdit Sequence Alignment Editor Software version 7.2-0.5 to determine the conserved regions. Peptides allocated at highly conserved regions will most likely develop in to stronger vaccine that covers more populations. The molecular weight and amino acid composition of the protein were also retrieved [26].

Sequenced-based method

The reference sequence of Treponema pallidum outer membrane beta-barrel protein was submitted to different prediction tools at the Immune Epitope Database (IEDB) analysis resource (http://www.iedb.org/) to predict various B and T cell epitopes. Conserved epitopes would be considered as candidate epitopes for B and T cells [27].

B cell epitope prediction

B cell epitopes is the part of the vaccine that interacts with B-lymphocytes. Candidate epitopes were analysed using several B cell prediction methods from IEDB (http://tools.iedb.org/bcell/), to identify the surface accessibility, antigenicity and hydrophilicity with the aid of computerized algorithm. Peptides with length larger than eight peptides were spliced to increase the possibility of obtaining peptides with higher scores in the tests. The Bepipred Linear Epitope Prediction 2 was used to predict linear B cell epitope with default threshold value 0.500 (http://tools.iedb.org/bcell/ result/). The Emini Surface Accessibility Prediction tool was used to detect the surface accessibility with default threshold value 1.000 (http://tools.iedb.org/bcell/result/). The kolaskar and tongaonkar antigenicity method was used to identify the antigenicity sites of candidate epitope with default threshold value 1.018 (http://tools. iedb.org/bcell/result/). Parker Hydrophilicity Prediction tool was used to identify the hydrophilic, accessible, or mobile regions with default threshold value 1.447 [28-31].

T cell epitope prediction MHC Class I binding

T cell epitopes is the part of the vaccine that interacts with T lymphocytes. Analysis of peptide binding to the MHC (Major Histocompatibility complex) class I molecule was assessed by the IEDB MHC I prediction tool (http://tools.iedb.org/mhci/). Artificial Neural Network (ANN) 4.0 prediction method was used to predict the binding affinity. Before the prediction, all human allele lengths were selected and set to nine aminoacids. The halfmaximal inhibitory concentration (IC50) value required for all conserved epitopes to bind at score less than or equal to 100 were selected [32-34].

T cell epitope prediction MHC Class II binding

Prediction of T cell epitopes interacting with MHC Class II was assessed by the IEDB MHC II prediction tool (http://tools.iedb. org/mhcii/). There are six available tools for prediction: SMM_ align, NN-align, Combinatorial Libraries, Sturniolo's method, Net MHCII pan and consensus method. Human allele references set were used to determine the interaction potentials of T cell epitopes and MHC Class II allele (HLA DR, DP and DQ). NN-align method was used to predict the binding affinity. All human allele lengths were set to the standard length. IC50 values at score less than or equal to 500 were selected [35,36].

Population coverage

In IEDB, the population coverage link was selected to analyse the epitopes. This tool calculates the fraction of individuals predicted to respond to a given set of epitopes with known MHC restrictions (http://tools.iedb.org/population/iedbinput). The appropriate checkbox for calculation was checked based on MHC I, MHC II separately and combination of both which is set against the whole world population [37].

AllerTOP v. 2.0

This method is used for allergenicity predictions; it is based on auto cross covariance (ACC) transformation of protein sequences into uniform equal-length vectors. The reference protein sequence (WP_010882178.1) was used. The principal Characteristics of the amino acids were represented by five E descriptors, which indicate amino acid hydrophobicity, molecular size, helix-forming propensity, relative abundance of amino acids, and β-strand forming propensity.

Homology modeling

The 3D structure was obtained using raptorX which is a protein structure prediction server developed by Xu group, excelling at predicting secondary and tertiary structure for protein sequences without close homologs in the Protein Data Bank (PDB). Obtained 3D protein structure was visualized by USCF chimera (version 1.8) which was also used for visualization and analysis of molecular structure of the promising epitopes and it’s binding to MHC class I, MHC class II [38,39].

Results

Multiple sequence alignment

The conserved regions and amino acid composition for the reference sequence of Treponeman Pallidum Outer Membrane Beta Barrel Protein are illustrated in Figures 1 and 2 respectively. Glycine, serine and leucine were the most abundant amino acids (Table 1).

Table 1: Molecular weight and amino acid frequency distribution of the protein.

Figure 1: Multiple Sequence Alignment using BioEdit software.

Figure 2: Aminoacid composition for Treponeman Pallidum Outer Membrane Beta Barrel Protein using BioEdit software.

B-cell epitope prediction

The reference sequence of Treponeman Pallidum Outer Membrane Beta Barrel Protein was subjected to Bepipred linear epitope 2, Emini surface accessibility, Kolaskar and Tongaonkar antigenicity and Parker hydrophilicity prediction methods to test for various immunogenicity parameters (Table 2 and Figures 3-8). Two epitopes have successfully passed the three tests. Three dimensional structure of the proposed B cell epitopes is shown in Figures 7 and 8.

Table 2: List of conserved epitopes that has successfully passed the four tests along with their relative scores. *Proposed epitopes.

Figure 3: Bepipred Linear Epitope Prediction; Yellow areas above threshold (red line) are proposed to be a part of B cell epitopes and the green areas are not.

Figure 4: EMINI surface accessibility prediction; Yellow areas above the threshold (red line) are proposed to be a part of B cell epitopes and the green areas are not.

Figure 5: Kolaskar and Tonganokar antigenicity prediction; Yellow areas above the threshold (red line) are proposed to be a part of B cell epitopes and green areas are not.

Figure 6: Parker Hydrophilicity prediction; Yellow areas above the threshold (red line) are proposed to be a part of B cell epitopes and green areas are not.

Figure 7: Proposed B cell epitope. The arrow shows position of (GVGLNIQSYLSKKAPP) with yellow colour at structural level using Chimera software.

Figure 8: Proposed B cell epitope. The arrow shows position of (SAGLYYQYTPD) with yellow colour at structural level using Chimera software.

Prediction of cytotoxic T-lymphocyte epitopes and modeling

The reference sequence was analyzed using (IEDB) MHC-1 binding prediction tool to predict T cell epitopes interacting with different types of MHC Class I alleles, based on Artificial Neural Network (ANN) with half-maximal inhibitory concentration (IC50)<100 nm. 36 peptides were predicted to interact with different MHC-1alleles. The most promising epitopes and their corresponding MHC-1 alleles are shown in Table 3. The tertiary structure of the promising epitopes was missing in the homology model.

Table 3: The most promising T cell epitopes and their corresponding MHC-1 alleles.

Prediction of the helper T-lymphocyte epitopes and modeling

Reference sequence was analyzed using (IEDB) MHC-II binding prediction tool based on NN-align with half-maximal inhibitory concentration (IC50)<500 nm; there were 135 predicted epitopes found to interact with MHC-II alleles. The most promising epitopes and their corresponding alleles are shown in Table 4 along with the 3D structure of the proposed epitope (Figure 9).

Table 4: The most promising T cell epitopes and their corresponding MHC-2 alleles.

Figure 9: Proposed T cell epitopes that interact with MHC2. The arrow shows position of (SNYYFSVPITVNPTY) with yellow colour at structural level using Chimera software.

Population coverage analysis

All MHC I and MHC II epitopes were assessed for population coverage against the whole world using IEDB population coverage tool. For MHC 1, epitopes with highest population coverage were YQYTPDWSI (52.63) and YYFSVPITV (50.62) (Figure 10 and Table 5). For MHC class II, the epitopes that showed highest population coverage were SNYYFSVPITVNPTY (73.11), GSNYYFSVPIT VNPT and EYFFTKNFSLGGQVS (71.88%) (Tables 6-9). When combined together, the epitopes that showed highest population coverage were SNYYFSVPITVNPTY (73.11), GSNYYFSVPITVNPT and EYFFTKNFSLGGQVS (71.88%) (Figure 11, Tables 7 and 8).

Table 5: Population coverage of proposed peptides interaction with MHC class I.

Table 6: Population coverage of proposed peptides interaction with MHC class 2.

Table 7: Population coverage of proposed peptide interaction with MHC class 1 and 2 combined.

Table 8: The population coverage of whole world for the epitope set for MHC I, MHC II and MHC I and II combined.

Table 9: Population coverage for MHC class II epitopes.

Figure 10: Population coverage for MHC class I epitopes.

Figure 11: Population coverage for combined MHC I and II epitopes.

In population coverage analysis of MHC II; 14 alleles were not included in the calculation, therefore the above (*) percentages are for epitope sets excluding: HLA-DQA1*05:01/DQB1*03:01, HLA-DQA1*01:02/DQB1*06:02, HLA-DPA1*01:03/DPB1*02:01, HLA-DPA1*01/DPB1*04:01, HLA-DPA1*02:01 /DPB1*05:01, HLA-DQA1*01:01/DQB1*05:01, HLA-DQA1*03:01/DQB1*03:02, HLA-DRB3*01:01, HLA-DRB4*01:01, HLA-DRB5*01:01, HLADQA1* 04:01/DQB1*04:02, HLA-DPA1*03:01/DPB1*04:02, HLADQA1* 05:01/DQB1*02:01, HLA-DPA1*02:01/DPB1*01:01.

Discussion

In this study, we successfully designed a peptide vaccine for Treponema pallidum against its immunogenic outer membrane beta-barrel protein by immunoinformatics tools. These peptides can be recognized by B cell and T cell to produce antibodies. Predicted peptide vaccine characterized by easy production, stimulating effective immune response and no potential infection possibilities. Peptide vaccines overcome the side effects of conventional vaccines [40].

The reference sequence of Treponema pallidum outer membrane beta-barrel protein was subjected to Bepipred linear epitope prediction 2 test, Emini surface accessibility test, Kolaskar and Tongaonkar antigenicity test and Parker hydrophilicity test in IEDB, to determine the binding to B cell, surface accessibility, antigenicity, the hydrophilicity of the B cell epitope respectively. Out of the ten predicted epitopes using Bepipred 2 test, only two epitopes passed the other three tests (GVGLNIQSYLSKKAPP, SAGLYYQYTPD). The reference sequence was analysed using MHC Iand MHC II binding prediction tools in IEDB to predict T cell epitopes. 36 epitopes were predicted to interact with MHC I alleles with IC50<100. Four of them were most promising and had the affinity to bind the highest number of MHC1 alleles (YEYFFTKNF, YQYTPDWSI, YYFSVPITV, RTFRAWQCV). 135 epitopes predicted to interact with MHC II alleles with IC50<500. Five of them were most promising and had the affinity to bind to the highest number of MHC II alleles (SNYYFSVPITVNPTY, GSNYYFSVPITVNPT, EYFFTKNFSLGGQVS, YFFTKNFSLGGQVSF, LGYEYFFTKNFSLGG).

The best epitope with the highest population coverage for MHC I was YQYTPDWSI with 52.63% in five HLA hits, and the coverage of population set for whole MHC I epitopes was 98.2%. Excluding cer-tain alleles for MHC II, the best epitope was SNYYFSVPITVNPTY scoring 73.11% with 16 HLA hits and the coverage of population set was 68.45% for the whole Helper Tlymphocyte epitopes (MHC II). In population coverage analysis of MHC II; 14 alleles were not included in the calculation: HLA-DQA1*05:01/DQB1*03:01, HLA-DQA1*01:02/ DQB1*06:02, HLA-DPA1*01:03/DPB1*02:01, HLA-DPA1*01/ DPB1*04:01,HLA-DPA1*02:01/DPB1*05:01,HLA-DQA1*01:01/ DQB1*05:01, HLA-DQA1*03:01/DQB1*03:02, HLADRB3* 01:01, HLA-DRB4*01:01, HLA-DRB5*01:01, HLADQA1* 04:01/DQB1*04:02,HLA-DPA1*03:01/DPB1*04:02, HLA-DQA1*05:01/DQB1*02:01, HLA-DPA1*02:01/DPB1*01:01. These epitopes could potentially induce a T-cell immune response when interacting strongly with MHC I and MHC II alleles effectively generating a cellular immune response against the invading patho-gen. The peptide SNYYFSVPITVNPTY had the highest popula-tion coverage per cent 73.1% in 16 HLA hits for both MHC I and MHC II. Many studies have used immunoinformatics methods to predict peptide vaccines for various micro-organisms such as HPV, Rubella, Dengue, Zika, Ebola, and mycetoma [41-49]. We hope that the whole world will benefit from this epitope-based vaccine upon its successful development following in vivo and in vitro studies to prove its effectiveness.

Conclusion

The method to protect and minimize the chance of infection is known as vaccination.. Vaccines Design by in silico prediction methods is very appreciated due to the important reduction in cost, effort and time. Peptide vaccines overcome the side effects of conventional vaccines. For the first time we designed different peptides that can create antibodies against β -Barrel outer membrane protein of Treponema pallidum. Two B cell epitopes passed the antigenicity, accessibility and hydrophilicity tests. 36 MHC I epitopes were the most promising ones, while five epitopes for MHC II. Five epitopes were common between MHC I and II. For the population coverage, the epitopes covered 71.88% of the alleles worldwide without certain alleles.

Data Availability

The data which support our findings in this study are available from the corresponding author upon reasonable request.

Competing Interest

The authors declare that they have no competing interests.

Acknowledgement

Many thank goes to National Center for Biotechnology Information (NCBI), BioEdit, Immune Epitope Database Analysis Resource (IEDB), RaptorX server, and UCSF Chimera team. We are grateful to Editor-in-Chief and all the unknown reviewers for their valuable comments on the previous version of this manuscript.

References

1. Zhao F, Wu Y, Zhang X, Yu J, Gu W, Liu S, et al. Enhanced immune response and protective efficacy of a Treponema pallidum Tp92 DNA vaccine vectored by chitosan nanoparticles and adjuvanted with IL-2. Human vaccines. 2011;7:1083-1089.
2. Salazar JC, Hazlett KR, Radolf JD. The immune response to infection with Treponema pallidum, the stealth pathogen. Micr inf. 2002;4:1133-1140.
3. Salazar JC, Cruz AR, Pope CD, Valderrama L, Trujillo R, Saravia NG, et al. Treponema pallidum elicits innate and adaptive cellular immune responses in skin and blood during secondary syphilis: a flow-cytometric analysis. The Journal of infectious diseases. 2007;195:879-887.
4. Kessler DA, Jimenez A. Syphilis, Human T-Cell Lymphotropic Virus, and Chagas Screening.  Trans Med Hemost. 2019;91-95
5. Hazlett KR, Cox DL, Decaffmeyer M, Bennett MP, Desrosiers DC, La Vake CJ, et al. TP0453, a concealed outer membrane protein of Treponema pallidum, enhances membrane permeability. J bacteriolo. 2005;187:6499-64508.
6. Haynes AM, Godornes C, Ke W, Giacani L. Evaluation of the protective ability of the Treponema pallidum subspecies pallidum Tp0126 OmpW homolog in the rabbit model of syphilis. Inf immu. 2019;23:19.
7. Cameron CE, Lukehart SA. Current status of syphilis vaccine development: need, challenges, prospects. Vaccine. 2014;32:1602-1609.
8. Cameron CE, Brouwer NL, Tisch LM, Kuroiwa JM. Defining the interaction of the Treponema pallidum adhesin Tp0751 with laminin. IAI. 2005;73:7485-7494.
9. Kumar S, Caimano MJ, Anand A, Dey A, Hawley KL, LeDoyt ME, et al. Sequence variation of rare outer membrane protein β-barrel domains in clinical strains provides insights into the evolution of Treponema pallidum subsp. pallidum, the syphilis spirochete. MBio. 2018;9:1006-1008.
10. Centurion-Lara A, Molini BJ, Godornes C, Sun E, Hevner K, Van Voorhis WC, et al. Molecular differentiation of Treponema pallidum subspecies. J Clinl Microbiol. 2006;44:3377-3380.
11. Anand A, LeDoyt M, Karanian C, Luthra A, Koszelak-Rosenblum M, Malkowski MG, et al. Bipartite Topology of Treponema pallidum Repeat Proteins C/D and I: OUTER MEMBRANE INSERTION, TRIMERIZATION, AND PORIN FUNCTION REQUIRE A C-TERMINAL β-BARREL DOMAIN. J Biol Chem. 2015;290:12313-12331.
12. Zhang R-L, Wang Q-Q, Zheng Z-J, Liang G-J, Yang L-J, Yang L-G, et al. Relationship Between the High Frequency of 23S rRNA Point Mutations in Treponema pallidum and Low Serological Response Rate to Azithromycin Treatment in China. International J Dermatol Venereol. 2019;2):6-14.
13. Radolf JD, Deka RK, Anand A, Šmajs D, Norgard MV, Yang XF. Treponema pallidum, the syphilis spirochete: making a living as a stealth pathogen. Nature Rev Microbiol. 2016;14:744.
14. Trujillo R, Cervantes J, Hawley KL, Cruz AR, Babapoor S, Murphy M, et al. Inflammation and immune evasion coexist in Treponema pallidum-infected skin. JAAD. 2018;4:462-464.
15. Van Den Heuvel A, Smet H, Prat I, Sands A, Urassa W, Fransen K, et al. Laboratory evaluation of four HIV/syphilis rapid diagnostic tests. BMC. 2019;19:1.
16. Kojima N, Siebert J, Maecker H, Rosenberg-Hasson Y, Leon S, Vargas S, et al. Cytokine expression in Treponema pallidum infection. J transl med. 2019;17:196.
17. Luthra A, Anand A, Hawley KL, LeDoyt M, La Vake CJ, Caimano MJ, et al. A homology model reveals novel structural features and an immunodominant surface loop/opsonic target in the Treponema pallidum BamA ortholog TP_0326. J bacteriol. 2015;197:1906-1920.
18. Cox DL. Culture of Treponema pallidum.  Meth enzymol. 1994;236:390-405.
19. Fairman JW, Noinaj N, Buchanan SK. The structural biology of β-barrel membrane proteins: a summary of recent reports. Curr Opin Struct Biol. 2011;21:523-531.
20. Bos MP, Robert V, Tommassen J. Biogenesis of the gram-negative bacterial outer membrane. Annu Rev Microbiol. 2007;61:191-214.
21. Walther DM, Rapaport D, Tommassen J. Biogenesis of β-barrel membrane proteins in bacteria and eukaryotes: evolutionary conservation and divergence. Cell Mol Life Sci. 2009;66:2789-2804.
22. Galdiero S, Galdiero M, Pedone C. β-barrel membrane bacterial proteins: structure, function, assembly and interaction with lipids. Curr Protein Pept Sci. 2007;8:63-82.
23. Kazi A, Chuah C, Majeed ABA, Leow CH, Lim BH, Leow CY. Current progress of immunoinformatics approach harnessed for cellular-and antibody-dependent vaccine design. Pathog Glob Health. 2018;112:123-131.
24. Rohn TA, Reitz A, Paschen A, Nguyen XD, Schadendorf D, Vogt AB, et al. A novel strategy for the discovery of MHC class II–restricted tumor antigens: identification of a melanotransferrin helper T-cell epitope. Cancer res. 2005;65:10068-10078.
25. Schmidt R, Carson PJ, Jansen RJ. Resurgence of Syphilis in the United States: An Assessment of Contributing Factors. Infect Dis. 2019;12:1178633719883282.
26. Islam SU, Qasim M, Lin W, Islam W, Arif M, Ali H, et al. Genetic interaction and diversity of the families Libellulidae and Gomphidae through COI gene from China and Pakistan. Acta tropica. 2018;182:92-99.
27. Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic acids res. 2019;47:D339-343.
28. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic acids res. 2017;45:24-29.
29. Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J virol. 1985;55:836-839.
30. Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS letters. 1990;276:172-174.
31. Parker JM, Guo D, Hodges RS. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochem. 1986;25:5425-5432.
32. Jorgensen KW, Rasmussen M, Buus S, Nielsen M. NetMHCstab-predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery. Immunol. 2014;141:18-26.
33. Lundegaard C, Nielsen M, Lund O. The validity of predicted T-cell epitopes. Trends biotechnol. 2006;24:537-538.
34. Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein sci. 2003;12:1007-1017.
35. Nielsen M, Lund O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC bioinformatics. 2009;10:296.
36. Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, et al. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC bioinformatics. 2010;11:568.
37. Bui H-H, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC bioinformatics. 2006;7:153.
38. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera-a visualization system for exploratory research and analysis. J compu chem. 2004;25:1605-1612.
39. Peng J, Xu J. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins. 2011;79:161-171.
40. Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol. 2013;3:120139.
41. Mohammed AA, ALnaby AM, Sabeel SM, AbdElmarouf FM, Dirar AI, Ali MM, et al. Epitope-Based Peptide Vaccine Against Fructose-Bisphosphate Aldolase of Madurella mycetomatis Using Immunoinformatics Approaches. Bioinform Biol Insights. 2018;12:1177932218809703.
42. Mohammed A, Hashim O, Elrahman K, Hamdi A, Hassan MJIR. Epitope-based peptide vaccine design against Mokola rabies virus glycoprotein G utilizing in silico approaches. Immunome Res. 2017;13:2.
43. Albagi S, Ahmed O, Gumaa M, Abd_elrahman K, Abu-Haraz AJJCCI. Immunoinformatics-Peptide Driven Vaccine and In silico Modeling for Duvenhage Rabies Virus Glycoprotein G. J Clin Cell Immunol. 2017;8:2.
44. Elgenaid S, Al-Hajj E, Ibrahim A, Essa M, Abu-haraz AJIR. Prediction of Multiple Peptide Based Vaccine from E1, E2 and Capsid Proteins of Rubella Virus: An In-Silico Approach. Immunome res. 2018;14:2.
45. 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 infect dise microbiol. 2016;4:61-71.
46. Badawi MM, Osman MM, Alla AF, Ahmedani AM, Abdalla M, Gasemelseed MM, et al. Highly conserved epitopes of Zika envelope glycoprotein may act as a novel peptide vaccine with high coverage: immunoinformatics approach. Am J infect dise microbiol. 2016;4:46-60.
47. Ahmed O, Abdelhalim A, Obi S, Elrahman K, Hamdi AJIR. Immunoinformatic approach for epitope-based peptide vaccine against Lagos rabies virus glycoprotein G. J Immunol Tech Infect Diseases. 2017;13:2.
48. Abu-haraz A, Abd-elrahman KA, Ibrahim MS, Hussien WH, Mohammed MS, Badawi MM, et al. Multi epitope peptide vaccine prediction against Sudan Ebola virus using immuno-informatics approaches. J Vacc Vaccinat. 2017;5:1764-2379.
49. Mohamed SS, MMO SMS, Elgailany HA, Bushara MK, Hassan HA, Mustafa GF, et al. Immunoinformatics Approach for Designing Epitope-Based Peptides Vaccine of L1 Major Capsid Protein against HPV Type 16. Biology. 2016.