In silico prediction of b-cell epitopes of dengue virus – A reverse vaccinology approach

INTRODUCTION

The dengue virus (DV) is a member of the flaviviridae family which is transmitted by two mosquitoes, namely, Aedes aegypti and Aedes albopictus. Nowadays, DV has been endemic in more than 100 countries, including the Americas, Africa, the Western Pacific, Southeast Asia, and Eastern Mediterranean (Yauch et al., 2009). It was estimated that 390 million dengue infection occurs every year and 500,000 dengue-related cases have been hospitalized. Therefore, at present, dengue is often considered as a major arboviral disease in the world (Priyamvada et al., 2016). There are four serotypes (DV-1, 2, 3, and 4), all of which cause classical dengue fever to severe forms of dengue hemorrhagic fever and dengue shock syndrome (DHF/DSS) (McBurney et al., 2016). Most of the studies indicate that DV infection can provoke not only neutralizing antibody response but also non-neutralizing antibodies (Duan et al., 2015). The DV has unique antibody epitopes, which are more specific to each serotypes. Individuals who have recovered from primary infection have developed long-term protective immune response against particular homologues serotypes (Wahala et al., 2010). Such antibodies are capable of preventing the cell entry into the host cell, which is extensively demonstrated in vitro either by prevention of conformational change in the protein or blockade of viral binding to host cells (Amorim et al., 2016). Non-neutralizing antibodies produced during primary infection can weakly cross-react with the heterologous serotypes and enhance the viral infectivity rate in FcγR⁺ cells. These phenomena are known as antibody-dependent enhancements, which lead to DHF/DSS (Dejnirattisai et al., 2010). Therefore, identification of B-cell epitopes for DV is important as it can contribute to the understanding of immunological responses in DV infection, as well as in providing information for vaccine development.

B-cell epitopes are peptides composed of hydrophilic amino acids present on the protein antigen or other biomolecules which are recognized by soluble or membrane-bound immunoglobulin molecules. These epitopes are focused on not only for pathogenesis and immunological research but also as a main target for vaccine and diagnostic reagent development (Jiang et al., 2010). At the outset, B-cell epitopes can be classified into continuous and discontinuous. Continuous epitopes are linear epitopes formed by 3–8 amino acid residues representing continuously on the primary structure of the parental protein. Discontinuous epitopes otherwise known as conformational epitopes consist of more than 10 amino acid residues and occur in a discrete manner, but assemble to exhibit an antigenic form in the tertiary structure of the parental protein (Laver et al., 1990). Prediction of linear B-cell epitopes with high accuracy is of paramount importantance for epitope-based immunotherapy. Several bioinformatics algorithms and servers are available for the predicition of B-cell epitopes. Most of the B-cell epitope prediction algorithms are focused on linear epitopes because it is believed that linear epitopes are capable of eliciting an antibody response that can cross-react with the parental antigen (Saha and Raghava, 2007a; 2007b). Since 1981, many B-cell epitope prediction algorithms have been developed based on various amino acid properties (Hopp and Woods,1981; Hopp and Woods, 1983; Ponomarenko and Van Regenmortel, 2009) such as hydrophilicity (Parker et al., 1986), hydrophobicity (Eisenberg et al., 1984), antigenicity (Welling et al., 1985), solvent accessibility (Emini et al., 1985), secondary structure (Chou and Fasman, 1974), flexibility (Karplus and Schulz, 1985), and many others. Few more algorithms based on machine learning techniques like BepiPred, APCpred, ABCpred, LBEEP, and LBtope have been developed recently for the prediction of linear B-cell epitopes (Dhanda et al., 2017). Currently, many of the B-cell prediction tools are freely available online. Most of the previous studies on dengue B-cell epitope prediction have employed a single DV protein and algorithm. In the current study, multiple prediction tools, such as ABCpred and BCPREDS, were applied to analyze all the structural and non-structural proteins of DV proteome for the prediction of B-cell epitopes covering all four DV serotypes.

METHODS

Toxicity prediction

The predicted putative candidates must not provoke any toxic effects on humans while administration. Hence, the toxic nature of the predicted epiotpes was evaluated using a web-based Toxinpred server (http://www.imtech.res.in/raghava/toxinpred/design.php). The consensus epitopes predicted by each tool were further filtered based on the results of toxicity prediction. The default parameters provided in the server were selected for this analysis.

Table 1. Dengue viral proteins and their accession numbers. [Click here to view]

Accessibility and antigenic propensity analysis

It was observed that antigenic and accessibility regions of antigens interact with the binding site of the antibody. All the consensus epitopes predicted in the present study were further analyzed using the BcePred tool for anlyzing accessibility and antigenicity regions. The default threshold values of 2 (accessibility) and 1.8 (antigenic propensity) were selected.

Figure 1. Epitope prediction by ABCpred, BCpred, and AAP methods. The maximum numbers of epitopes were identified in ABCpred analysis. [Click here to view]

RESULTS

Toxicity prediction of B-cell epitopes

All the consensus epitopes were further analyzed by Toxinpred tool for prediction of toxic peptides. Out of the 251 consensus epitopes, 244 were predicted to be non-toxic (Supplementary Material 4). The 7 toxic peptides were excluded and the rest of the 244 non-toxic epitopes were used for further analysis.

Figure 2. Identification of consensus epitopes among predicted epitopes. The highest numbers of consensus peptides predicted were of 12 mer in length. [Click here to view]

Conformational epitope prediction

A total of 150 conformational epitopes were identified out of 244 consensus epitopes predicted in this study. The highest numbers of conformational epitopes (96 out of 244) were predicted in ABCpred analysis, followed by 29 and 25 conformational epitopes identified using BCpred and AAP methods, respectively. Most of the conformational epitopes (54) were found to occur as 12-mer in length which is 36% of the total epitopes; this is followed by 32.67% (49), 12,67% (19), 9.33% (14), and 9.33% (14) of 16-mer, 14-mer, 18-mer, and 20-mer, respectively.

Figure 3. Consensus epitopes predicted among the four DV serotypes. [Click here to view]

Table 2. Overlapping epitopes predicted by more than one prediction method. [Click here to view]

Experimental validation of B-cell epitopes

The consensus epitopes predicted by all three prediction methods were further analyzed using the IEDB tool for exact and partial matching with experimentally proven B-cell epitopes. Interestingly, although none of the consensus epitopes were shown as experimentally proven in exact match search, 59 of the 244 consensus epitopes were shown as experimentally proven with 90% similarity in the IEDB-BLAST search (Supplementary Material 8). A total of 164 consensus epitopes were identified in ABCpred, and among this 26.83% (44/164) of the epitopes were experimentally proven as B-cell epitopes, which belong to various DV serotypes; 17.02% (8/47) and 21.21% (7/33) were identified by BCpred and AAP method, respectively, and they were already proven experimentally.

Table 3. Overlapping epitopes containing both accessibility and antigenicity regions. [Click here to view]

DISCUSSION

A large portion of the population are infected with any one of DV serotypes every year and a significant amount of them develop severe forms of DSS/DHF (Amorim et al., 2016). This underscores the development of a safe and effective vaccine against DV which is a challenging task. In December 2015, the first dengue vaccine, Dengvaxia® (CYD-TDV) licensed by Sanofi Pasteur, entered into the market and it has been approved in a few countries, including Mexico, Brazil, and Philippines (Vannice et al., 2016). This vaccine could be administrated to people between 9 and 45 years of age (Carvalho et al., 2016). In a phase IIb study in Thailand, the vaccine showed lower efficacy in younger children (Schwartz et al., 2015). It was suggested that CYD-TDV may increase the risk of hospitalization when administered to children below nine years of age (Carvalho et al., 2016).

Humoral immunity also plays a significant role in the prevention of viral infections along with cell-mediated immunity and hence identification of B-cell epitopes is important in the understanding of viral pathogenesis and in vaccine development (Barlow et al., 1986). The ability of epitope-based vaccines in stimulating a specific immune response without any side effect make them as a good choice for vaccine development (Oany et al., 2013).

Studies conducted in recent years, on B-cell epitope prediction, involved the analysis of a single serotype or protein of DV mainly focusing NS1 and E protein (Gromowski et al., 2008; Jiang et al., 2010; Matsui et al., 2009). In these investigations, only a limited number of epitopes were reported. But the ideal dengue vaccine must be tetravalent and should provoke immune response against all four dengue serotype and the vaccines might not increase the risk of DHF/DSS (Guy et al., 2016). In the present study, a total of 10,083 linear B-cell epitopes were predicted from all 4 DV proteomes using ABCpred, BCpred, and AAP methods. The B-cell epitopes predicted in each serotype were manually compared with one another for consensus epitopes. A total of 251 consensus epitopes were predicted; among this, 214 and 34 epitopes were present in DV-2 and DV-3 serotypes, respectively. Surprisingly, three epitopes, viz., NS5-DLGCGRGGWSYY (predicted by ABCpred), EPE-DRGWGNGCGLFG, and NS4b- IIGPGLQAKATREAQK (predicted by BCpred) were found to occur in all four DV serotypes. Two of these peptides, NS5-DLGCGRGGWSYY and EPE-DRGWGNGCGLFG, were already identified as conserved in all four DV serotypes (Khan et al., 2008). Here, we report a novel epitope, NS4b-IIGPGLQAKATREAQK, which is conserved in all four DV serotypes. The consensus epitopes predicted in this study may be useful for the preparation of a multivalent vaccine.

Prediction of toxic peptides is an important step in developing a potent epitope-based vaccine. Therefore, the 251 consensus epitopes were further analyzed using the Toxinpred tool in which 7 epitopes were predicted as toxic and were excluded from further study. To further strengthen the prediction, the VaxiJen tool server was used to analyze the protective antigens based on the overall antigenicity score (Mehla and Ramana 2016). The non-toxic epitopes were further analyzed using VaxiJen server and the peptides with high antigenicity values were considered to be potent B-cell epitopes. Forty-six potent B-cell epitopes were predicted by all three prediction methods with their VaxiJen score of more than 1. Interestingly, few epitopes from ABCpred [PrM-HRRDKRSVALAP (1.5287), NS5-VMDIISRKDQRG NS3-AIALDFKPGTSGSP (2.046), and NS3-RNLTIMDLHPGSGKTR (1.6313)], BCpred [EPE-CVGVGNRDFVEG (1.6726), NS5-KREKKLGEFGKA (1.6453), and NS5-SLMYFHRRDLRL (1.5446)], and AAP [NS3-IMDLHPGSGKTR (1.6043) and NS5-MDIISRKDQRGS (1.5131)] showed antigenicity scores of more than 1.5.

It is believed that the combination of more than one property provides a better accuracy in epitope prediction (Saha and Raghava, 2004). Therefore, the predicted consensus epitopes were further analyzed for identification of accessibility and antigenic propensity using BcePred tool. Intrestingly, 52.04% (127 out of 244) of consensus epitopes have an accessibility region, whereas only 20.90% (51 out of 244) of the consensus epitopses were found to have an antigenic propensity region. The entire region of the following peptides had accessibility: NS5-TPFGQQRVFKEK and NS3-DEERDIPERSWNSG predicted by BCpred; NS5-VRNPLSRNSTHEMY and GKREKKLGEFGKAKGSRA predicted by AAP method; NS5-MMGKREKKLGEF, TPFGQQRVFKEKVDTR, and NS3-LRKNGKKVIQLSRKTF. Furthermore, 19 epitopes were found to contain both accessibility and antigenicity properties out of 244 epitopes analyzed in this study. Interestingly, all the 19 epitopes were found to be non-toxic in nature. Hence, these epitopes may be useful for the preparation of a DV-specific vaccine preparation.

Conformational epitopes play an important role in peptide-based vaccine development. It is also known that ~90 of B-cell epitopes were confomational epitopes (Ansari and Raghava, 2010). In this study, the antibody interaction region identified in DV proteome was further compared with consensus epitopes for the identification of accurate B-cell epitopes. Majority of the linear epitopes (150 out of 251) were predicted as conformational epitopes. Intrestingly, the entire region of NS5 epitope YYCAGLKKVTEV predicted by AAP method and ABCpred and the EPE epitope DRGWGNGCGLFG predicted by BCpred method was found to be conformational epitopes.

Combination of immune dominant epitopes in a vaccine can elicite a broader immune response to heterologous serotypes (Schussek et al., 2014). The cluster analysis indicate that cluster 7 contains 9 epitopes of the DV-NS1 protein: TWTEQYKFQADSPK, CGSGIFVTNEVHTWTE, SGIFVTNEVHTWTEQYKF, LKCGSGIFVTNEVHTWTEQY, and VHTWTEQYKFQA predicted by ABCpred; FVTNEVHTWTEQYK and SGIFVTNEVHTWTEQY predicted by AAP method; VTNEVHTWTEQY and VTNEVHTWTEQYKFQADS predicted by BCpred. These epitopes have common amino acid sequence VHTWTEQYK. Previous studies have reported that VHTWTEQYK epitope could enhance the antibody response against DV in mice and human (Falconar, 2007; 2012). Similarly, cluster 17 contains 12 epitopes of NS2b protein. Of these, a 16-mer epitope IIGPGLQAKATREAQK predicted by BCpred was found to occur in all four DV serotypes. Interestingly, part of this epitope IIGPGLQAKATREA was identified as epitope by all 3 prediction methods. Likewise, cluster 24 contains 6 epitopes of NS5 protein and was found to occur in DV 1, 2, and 3 serotypes. Out of 6 epitopes, 5 epitopes have common amino acid sequence KREKKLGEFGKA which is predicted by all three prediction methods.

The possibility of a predicted epitope to be a conformational epitope is more if the epitope is predicted by more than one tool. Hence, the predicted consensus epitopes were further analyzed by overlapping epitope prediction method. Only 6 overlapping epitopes were predicted out of 244 consensus epitopes analyzed. Incidently, all the six peptides were predicted as epitopes by more than one method. EPE-GWGNGCGLFGKG, and NS1-VTNEVHTWTEQY epitopes were predicted as non-antigenic by VaxiJen tool, whereas the epitopes NS3- DEAHFTDPASIAAR, NS5-YYCAGLKKVTEV, NS5-GKREKKLGEFGKAKGS, and NS5-DVVPMVTQMAMTDTTP exhibited an antigenic score of 0.7001, 0.8325, 1.0836, and 0.9119, respectively.

In addition, all 244 consensus epitopes were searched against IEDB server which provides the information about experimentally validated B-cell epitopes studied in human and non-human primates and in other animal species (Kim et al., 2012). None of the epitopes showed as experimentally proved in exact match blast analysis. Whereas, 24.18% (59/244) of epitopes were shown as experimentally proved in 90% similarity blast analysis. The EPE epitope FKNPHAKKQDVVVLGSQEGAMHT and PEVVVLGSQEGAMHT were experimentally proved as potent B-cell epitopes against DV tested in mice (da Silva, 2009) and human (Innis et al., 1989). The sequence, SGATWVD, present in the predcited DV EPE epitope (SGATWVDVVLEH) is also reported to be present in Murray valley encephalitis virus epitope, EGASGATWVDLVLEGDSCITI, that was already reported as B-cell epitope in mice (Mathews et al., 1991; Roehrig et al., 1989). Though 59 of the 151 antigenic epitopes are experimentally proven as potent B-cell epitopes, the remaining 92 peptides are potential novel epitopes that if confirmed experimentally could pave way for a more potent DV vaccine.

CONCLUSION

A universal vaccine combining many antigenic epitopes that can elicit a broader neutralizing antibody response to all four DV serotypes is needed to combat the virus. To our knowledge, this is the first report on genome-wide mapping of linear B-cell epitopes for all four DV proteomes. Ninty-two potential novel epitopes have been predicted in this study. These epitopes, after experimental analysis, could form a base for the development of a multivalent vaccine against DV.

CONFLICT OF INTEREST

The authors declared that they have no conflict of interests associated with this publication.

ACKNOWLEDGMENTS

The work was supported by a grant from Science and Engineering Research Board, India (SR/SO/HS-0248/2012) to KS. MM thanks the Indian Council of Medical Research, India, for a Senior Research Fellowship (45/18/2011-IMM-BMS).

REFERENCES

Amorim JH, dos Santos Alves RP, Bizerra R, Pereira SA, Pereira LR, Fabris DLN, Santos RA, Romano CM, de Souza Ferreira LC. Antibodies are not required to a protective immune response against dengue virus elicited in a mouse encephalitis model. Virology, 2016; 487:41–9. CrossRef

Ansari HR, Raghava GP. Identification of conformational B-cell Epitopes in an antigen from its primary sequence. Immunome Res, 2010; 6(1):6. CrossRef

Barlow D, Edwards M, Thornton J. Continuous and discontinuous protein antigenic determinants. Nature, 1986; 322(6081):747. CrossRef

Carvalho A, Van Roy R, Andrus J. International dengue vaccine communication and advocacy: challenges and way forward. Expert Rev Vaccines, 2016; 15(4):539–45. CrossRef

Chou PY, Fasman GD. Conformational parameters for amino acids in helical, β-sheet, and random coil regions calculated from proteins. Biochemistry, 1974; 13(2):211–22. CrossRef

da Silva AN, Nascimento EJ, Cordeiro MT, Gil LH, Abath FG, Montenegro SM, Marques ET. Identification of continuous human B-cell epitopes in the envelope glycoprotein of dengue virus type 3 (DENV-3). PLoS One, 2009; 4(10):e7425. CrossRef

Dejnirattisai W, Jumnainsong A, Onsirisakul N, Fitton P, Vasanawathana S, Limpitikul W, Puttikhunt C, Edwards C, Duangchinda T, Supasa S. Cross-reacting antibodies enhance dengue virus infection in humans. Science, 2010; 328(5979):745–8. CrossRef

Dhanda SK, Usmani SS, Agrawal P, Nagpal G, Gautam A, Raghava GPS. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief Bioinform, 2017; 18(3):467–78. CrossRef

Duan Z, Guo J, Huang X, Liu H, Chen X, Jiang M, Wen J. Identification of cytotoxic T lymphocyte epitopes in dengue virus serotype 1. J Med Virol, 2015; 87(7):1077–89. CrossRef

Eisenberg D, Weiss RM, Terwilliger TC. The hydrophobic moment detects periodicity in protein hydrophobicity. Proc Natl Acad Sci USA, 1984; 81(1):140–4. CrossRef

Emini EA, Hughes JV, Perlow D, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol, 1985; 55(3):836–9. CrossRef

Falconar AK. Antibody responses are generated to immunodominant ELK/KLE-type motifs on the nonstructural-1 glycoprotein during live dengue virus infections in mice and humans: implications for diagnosis, pathogenesis, and vaccine design. Clin Vaccine Immunol, 2007; 14(5):493–504. CrossRef

Falconar AK. Epitope reactions can be gauged by relative antibody discriminating specificity (RADS) values supported by deletion, substitution and cysteine bridge formation analyses: potential uses in pathogenesis studies. BMC Res Notes, 2012; 5:208. CrossRef

Gromowski GD, Barrett ND, Barrett AD. Characterization of dengue virus complex-specific neutralizing epitopes on envelope protein domain III of dengue 2 virus. J Virol, 2008; 82(17):8828–37. CrossRef

Guy B, Lang J, Saville M, Jackson N. Vaccination against dengue: challenges and current developments. Annu Rev Med, 2016; 67:387–404. CrossRef

Hopp TP, Woods KR. A computer program for predicting protein antigenic determinants. Mol Immunol, 1983; 20(4):483–9. CrossRef

Hopp TP, Woods KR. Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci USA, 1981; 78(6):3824–8. CrossRef

Innis BL, Thirawuth V, Hemachudha C. Identification of continuous epitopes of the envelope glycoprotein of dengue type 2 virus. Am J Trop Med Hyg, 1989; 40(6):676–87. CrossRef

Jiang L, Zhou J-M, Yin Y, Fang D-Y, Tang Y-X, Jiang L-F. Selection and identification of B-cell epitope on NS1 protein of dengue virus type 2. Virus Res, 2010; 150(1–2):49–55. CrossRef

Karplus P, Schulz G. Prediction of chain flexibility in proteins. Naturwissenschaften, 1985; 72(4):212–3. CrossRef

Khan AM, Miotto O, Nascimento EJ, Srinivasan K, Heiny A, Zhang GL, Marques E, Tan TW, Brusic V, Salmon J. Conservation and variability of dengue virus proteins: implications for vaccine design. PLoS Negl Trop Dis, 2008; 2(8):e272. CrossRef

Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, Lundegaard C, Sette A, Lund O, Bourne PE, Nielsen M, Peters B. Immune epitope database analysis resource. Nucleic Acids Res, 2012; 40(Web Server issue):W525–30. CrossRef

Laver WG, Air GM, Webster RG, Smith-Gill SJ. Epitopes on protein antigens: misconceptions and realities. Cell, 1990; 61(4):553–6. CrossRef

Mathews JH, Allan J, Roehrig J, Brubaker J, Uren M, Hunt A. T-helper cell and associated antibody response to synthetic peptides of the E glycoprotein of Murray Valley encephalitis virus. J Virol, 1991; 65(10):5141–8. CrossRef

Matsui K, Gromowski GD, Li L, Schuh AJ, Lee JC, Barrett AD. Characterization of dengue complex-reactive epitopes on dengue 3 virus envelope protein domain III. Virology, 2009; 384(1):16–20. CrossRef

McBurney SP, Sunshine JE, Gabriel S, Huynh JP, Sutton WF, Fuller DH, Haigwood NL, Messer WB. Evaluation of protection induced by a dengue virus serotype 2 envelope domain III protein scaffold/DNA vaccine in non-human primates. Vaccine, 2016; 34(30):3500–7. CrossRef

Mehla K, Ramana J. Identification of epitope-based peptide vaccine candidates against enterotoxigenic Escherichia coli: a comparative genomics and immunoinformatics approach. Mol Biosyst, 2016; 12(3):890–901. CrossRef

Oany AR, Ahmad SAI, Hossain MU, Jyoti TP. Identification of highly conserved regions in L-segment of Crimean–Congo hemorrhagic fever virus and immunoinformatic prediction about potential novel vaccine. Adv Appl Bioinform Chem, 2015; 8:1. CrossRef

Parker J, Guo D, Hodges R. 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. Biochemistry, 1986; 25(19):5425–32. CrossRef

Ponomarenko JV, Van Regenmortel MH. B cell epitope prediction. Struct Bioinform, 2009:849–79.

Priyamvada L, Cho A, Onlamoon N, Zheng N-Y, Huang M, Kovalenkov Y, Chokephaibulkit K, Angkasekwinai N, Pattanapanyasat K, Ahmed R. B cell responses during secondary dengue virus infection are dominated by highly cross-reactive, memory-derived plasmablasts. J Virol, 2016; 90(12):5574–85. CrossRef

Roehrig JT, Hunt AR, Johnson AJ, Hawkes RA. Synthetic peptides derived from the deduced amino acid sequence of the E-glycoprotein of Murray Valley encephalitis virus elicit antiviral antibody. Virology, 1989; 171(1):49–60. CrossRef

Saha S, Raghava G. 2004. BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In International Conference on Artificial Immune Systems, Springer, pp 197–204. CrossRef

Saha S, Raghava GP. Prediction methods for B-cell epitopes. In:Immunoinformatics, Springer, Humana press, New Jersey, USA: pp.387–94, 2007a. CrossRef

Saha S, Raghava GP. Searching and mapping of B-cell epitopes in BciPep database. In: Immunoinformatics, Springer, Humana press, New Jersey, USA: pp.113–24, 2007b CrossRef

Schussek S, Trieu A, Doolan DL. Genome- and proteome-wide screening strategies for antigen discovery and immunogen design. Biotechnol Adv, 2014; 32(2):403–14. CrossRef

Schwartz LM, Halloran ME, Durbin AP, Longini Jr IM. The dengue vaccine pipeline: implications for the future of dengue control. Vaccine, 2015; 33(29):3293–8. CrossRef

Vannice KS, Durbin A, Hombach J. Status of vaccine research and development of vaccines for dengue. Vaccine, 2016; 34(26):2934–8. CrossRef

Wahala WM, Donaldson EF, De Alwis R, Accavitti-Loper MA, Baric RS, De Silva AM. Natural strain variation and antibody neutralization of dengue serotype 3 viruses. PLoS Pathog, 2010; 6(3):e1000821. CrossRef

Welling GW, Weijer WJ, van der Zee R, Welling-Wester S. Prediction of sequential antigenic regions in proteins. FEBS Lett, 1985; 188(2):215–8. CrossRef

Yao Y, Huang W, Yang X, Sun W, Liu X, Cun W, Ma Y. HPV-16 E6 and E7 protein T cell epitopes prediction analysis based on distributions of HLA-A loci across populations: an in silico approach. Vaccine, 2013; 31(18):2289–94. CrossRef

Yauch LE, Zellweger RM, Kotturi MF, Qutubuddin A, Sidney J, Peters B, Prestwood TR, Sette A, Shresta S. A protective role for dengue virus-specific CD8+ T cells. J Immunol, 2009; 182(8):4865–73. CrossRef