Int J Pharm Pharm Sci, Vol 9, Issue 11, 175-182Original Article



Department of Biotechnology, Kalasalingam University, Krishnankoil 626126, Tamilnadu, India

Received: 21 Jul 2017 Revised and Accepted: 21 Sep 2017


Objective: To predict the immunogenic epitopes from human papillomavirus (HPV) virus using matrix based computational tools.

Methods: In the present study, three matrix based algorithms, SYFPETHI, BIMAS and RANKPEP were used to predict the cytotoxic T lymphocyte (CTL) epitopes of HPV 16 and 18. The ability of the peptides to bind HLA A_0201, a most common allele, was evaluated using these algorithms. High scoring peptides were considered as potential binders.

Results: Evaluation of HPV 16 proteome resulted in the prediction of 249 peptides as potential binders. Out of these only 25 peptides were predicted as binders by all three algorithms. Analysis of HPV 18 predicted 215 peptides, as potential binders. Among the 215 peptides only 20 peptides were predicted as binders by all three algorithms.

Conclusion: The efficacy of these peptides in inducing a stronger immune response needs to be tested using in vitro and in vivo assays. The identified epitopes could be used in designing a novel epitope vaccine for HPV.

Keywords: Epitope prediction, CTL epitopes, Human papilloma virus, BIMAS, SYFPEITHI, RANKPEP.


Cervical cancer is the second most common cancer in women worldwide. HPV is the causative agent of cervical cancer and the highest infection rate was reported among young women aged between 15-19 y [1, 2, 47]. The greatest burden of HPV infection occurs in developing countries due to the lack of organized screening programs [3, 4, 48]. In India, it has been reported that 130,000 cases and 70-75,000 death occurred annually, suggesting that the cervical cancer is one of the major cancers in India [5]. Based on the carcinogenicity, HPV can be divided into two groups: high-risk types such as HPV 16 and 18 and low-risk types such as HPV type 6 and 11 [6]. More than 70% of cervical cancer is caused by both HPV type 16 and 18. Currently, two vaccines (Ceravix and Gardasil) are commercially administrated to prevent HPV infection [46]. These preventative vaccines are mainly used for protection against HPV type 16, 18, 6 and 11 [7]. Administration of these vaccines in some cases leads to severe side effects [8]. Few studies have focused on the development of therapeutic epitope based vaccines using E5, E6, L1 and L2 proteins [9, 10].

CD4 and CD8 T cell responses play an important role in controlling the pathogenesis of HPV in human [11]. The accurate identification of CTL epitopes is a critical step towards the development of peptide vaccine [12]. The identification of CTL epitopes could be accelerated using in silico prediction methods [13]. Major histo-compatability complex (MHC) molecules play a major role in the activation of T-cell mediated immune response [14]. Processing and presentation of epitopes via MHC to CTL are an important process for immuno surveillance against various pathogens [15]. Antigenic proteins are cleaved in the proteosomes into shorter peptides, which are loaded on to class I MHC molecules [13] and exported to the cell surface for presentation to the T-cell receptor [16, 43]. TAP proteins also play a role in this antigen presentation [17]. It was estimated that only one peptide out of 200 peptides could bind to the MHC class I MHC molecules and elicit CTL response [18]. The development of the multivalent vaccine that enhances cytotoxic T cell immunity is a major direction of research in current vaccine development [19].

Many computational algorithms have been developed for predicting the binding of peptides to MHC molecules [20, 21] including quantitative matrices [22, 23], artificial neural networks [24], hidden-markov models [25] and molecular modelling [26, 27]. These approaches could be used for prediction of antigenic epitopes. Few of them are open source algorithms such as BIMAS [28], SYFPEITHI [22], RANKPEP [29], SVMHC [30] and MHCPRED [31]. In the present study, the specificity and sensitivity of some of the tools in predicting epitopes were evaluated and the combinations of tools were used for predicting the immunogenic CTL epitopes in HPV proteome.


Source data

In the present study, the sensitivity and the specificity of the algorithms were evaluated by known binders and non-binding peptides. A set of 311 known binders were obtained from the HIV epitope database of Los Alamos National Laboratory and immune epitope database (IEDB). Totally 222 non-binding peptides were derived from MHCBN and IEDB. The complete set of HPV type 16 and 18 proteins (Early proteins E1, E2, E5, E6 and E7; Late proteins L1 and L2) were retrieved from Gen Bank (http://www. ncbi. nlm. nih. gov/genbank/) database and the details are provided in table 1.

Tools used for prediction of HPV 16 and 18 CTL epitopes

The complete set of HPV type 16 and 18 proteomes were analyzed for the MHC class I HLA A_0201 binding peptides using three matrix based prediction algorithms namely BIMAS (, SYFPEITHI (http://www. and RANKPEP ( /rankpep.html). All individual protein sequences of HPV serotypes 16 and 18 were parsed into the algorithms, and the binding efficiencies of the nine amino acid peptides were calculated.

Calculation of sensitivity and specificity of the algorithms

For the calculation of sensitivity and specificity, each binding and non-binding peptides were individually analyzed by using three matrix based algorithms (SYFPEITHI, BIMAS, and RANKPEP) and the results were computed. The cut-off score for binding of these peptides to the HLA A_0201 was fixed as ≥ 20, ≥ 50 and ≥ 60 for SYFPEITHI, BIMAS and RANKPEP respectively. A peptide scoring less than this was considered as a non-binder.

The sensitivity of the computational algorithms [13, 34] was calculated using the formula:


The specificity of the computational algorithms [32, 44] was calculated using the formula:


Overlapping epitope prediction

Instead of using a single prediction tools for MHC-peptide binding prediction, using a combination of prediction tools could improve the efficiency of epitope prediction. A peptide predicted as an epitope in more than one tool was considered to be an overlapping epitope. The binders predicted in all three prediction tools were further manually compared with one another for the prediction of overlapping epitopes.

Identification of consensus epitopes

A peptide which is present in more than one genotype was considered to be a consensus epitope. Based on the occurrence, all predicted binders of HPV 16 and 18 were compared with one another for prediction of consensus epitopes. The level of conservation (single amino acid variation) in predicted epitopes was also assessed among the HPV 16 and 18 genotypes.

Molecular docking

Molecular docking studies were carried out using AutoDock4.2. The crystal structure of human HLA-A2 (PDB ID: 4NO3) was downloaded from the Protein Data Bank. A known CTL epitope from influenza virus, GILGFVFTL, was taken as a reference peptide. Two predicted binders from this study, QLFVTVVDT (QLF) and KLPQLCTEL (KLP) along with the reference peptide were docked against HLA-A2.


Sensitivity and specificity of the algorithm

When the known binders for HLA A_0201 were analyzed, BIMAS could predict only 176 out of 311 with a sensitivity of 57.56%. The sensitivity of SYFPEITHI and RANKPEP was calculated as 77.49% and 67.52% respectively. The combination of more than one algorithm improved the sensitivity; SYFPEITHI and BIMAS when combined together could predict 252 of the 311 peptides with a sensitivity of 81.02%; However, combining all the three programs increased the sensitivity from 57.56%to 81.99% (255 out of 311) (fig. 1).

Table 1: Overview of epitope prediction analysis in HPV 16 and 18 proteomes

S. No. Protein Total number of amino acids Number of peptides analyzed
HPV 16 HPV 18 HPV 16 HPV 18
1 E1 649 657 641 649
2 E2 365 365 357 357
3 E4 95 88 87 80
4 E5 83 73 75 65
5 E6 158 158 150 158
6 E7 98 105 90 97
7 L1 531 568 523 560
8 L2 473 462 465 454

Fig. 1: Sensitivity of the selected algorithms in the prediction of CTL epitopes. The sensitivity of individual algorithms (SYFPEITHI, BIMAS and RANKPEP) and the combination were analyzed. Sensitivity increased (81.99%) when all the three algorithms were combined with a minimal error rate

Based on the cut-off criteria, each of the non-binders was tested using all three algorithms and based on the results the specificity was calculated based on the formula described in method’s section. When 222 non-binders were analyzed, the specificities of BIMAS, SYFPEITHI and RANKPEP were 93.69%, 77.03% and 74.78% respectively. The specificity were improved when a combination of two or more algorithms were used (fig. 2).

Fig. 2: Specificity of the selected algorithms in the prediction of CTL epitopes. Specificity of individual algorithms (SYFPEITHI, BIMAS and RANKPEP) and the combination was calculated using known non-binders. Improved specificity was observed when two or more of the algorithms were used in combination

HPV 16 and 18 epitope mapping

The proteomes of HPV type 16 and 18 serotypes were analyzed for the prediction of CTL epitopes using all the three algorithms. In HPV 16, a total of 2388 peptides were analyzed, and 249 of them were predicted as binders by all three algorithms together (fig. 3D). SYFPEITHI alone could predict 115 peptides as binders (fig. 3A), where as 45 and 89 binders were predicted by BIMAS and RANKPEP respectively (fig. 3B and 3C).

When 2412 peptides were analyzed in HPV 18 proteome, all the three algorithms together predicted 215 peptides as binders (fig. 4D). In which, 102, 44 and 69 binders were predicted by SYFPEITHI (fig. 4A), BIMAS (fig. 4B) and RANKPEP (fig. 4C) respectively.

Fig. 3: Prediction of CTL epitopes for HPV 16 serotype. H. PV 16 serotype proteins were analyzed by SYFPEITHI, BIMAS and RANKPEP. A. Analysis of the proteome of HPV 16 by SYFPEITHI. B. Analysis of the proteome of HPV 16 by BIMAS analysis C. Analysis of the proteome of HPV 16 by RANKPEP D. Prediction of peptides as binders in HPV 16 proteome using SYFPETHI, BIMAS and RANKPEP algorithms based on the fixed criteria

Fig. 4: Prediction of CTL epitopes for HPV 18 serotype. HPV 18 serotype proteins were analysed by SYFPEITHI, BIMAS and RANKPEP. A. Analysis of the proteome of HPV 18 by SYFPEITHI. B. Analysis of the proteome of HPV 18 by BIMAS analysis. C. Analysis of the proteome of HPV 18 by RANKPEP. D. Prediction of peptides as binders in HPV 18 proteome using SYFPETHI, BIMAS and RANKPEP algorithms based on the fixed criteria

Overlapping epitope prediction

Though 249 peptides were found to be predicted as binders by the three matrix based algorithms viz. BIMAS, SYFPEITHI and RANKPEP, only 25 of them were considered as overlapping peptides in HPV 16 proteome as predicted by all three prediction tools. The highest number of overlapping peptides were predicted in E1 and L1 proteins (table 2). Likewise, 20 overlapping binders were predicted in HPV 18 analysis and L1 protein showed the highest number of overlapping peptides (table 3).

Table 2: Predicted CTL epitopes in HPV 16 proteome

Protein Accession No. Peptide Sequence







E1 P03114 KLLSKLLCV 29 2071.606 93
YLVSPLSDI 25 110.379 89
LLQQYCLYL 24 199.738 81
CLYLHIQSL 27 157.227 72
AMLAKFKEL 24 108.462 69
FLTALKRFL 21 108.094 65
E2 P03120 TLQDVSLEV 24 285.163 97
TLYTAVSST 21 54.847 80
E5 P06927 VLLCVCLLI 22 65.622 78
IILVLLLWI 26 114.142 75
FLLCFCVLL 26 1381.635 68
E6 P03126 KLPQLCTEL 24 74.768 68
E7 P03129 LLMGTLGIV 29 53.631 92
TLHEYMLDL 24 201.447 86
RLCVQSTHV 20 69.552 75
L1 P03101 TLQANKSEV 22 69.552 81
ILVPKVSGL 30 83.527 75
YLRREQMFV 22 133.735 73
GLQYRVFRI 22 139.174 70
QLFVTVVDT 21 63.417 62
RLVWACVGV 23 69.552 62
L2 P03107 SLVEETSFI 24 235.26 96
YLHPSYYML 25 147.401 76
AILDINNTV 26 145.077 77
ILQYGSMGV 24 118.238 64

Table 3: Predicted CTL epitopes in HPV 18 proteome

Protein Accession no. Peptide sequence SYFPEITHI score BIMAS score RANKPEP score
E1 P06789 ILYAHIQCL 27 267.286 75
FLGALKSFL 22 540.469 69
E2 P06790 TLSERLSCV 26 655.875 88
E4 P06791 RLLHDLDTV 28 290.025 70
E5 P06792 VLVFVYIVV 20 72.717 82
MLLLHIHAI 26 150.931 80
LLLHIHAIL 26 55.091 67
WVLVFVYIV 22 371.17 64
E6 P06463 KLPDLCTEL 25 306.55 70
E7 P06788 TLQDIVLHL 26 201.447 94
L1 P06794 SLVDTYRFV 23 470.519 90
CLYTRVLIL 26 64.463 86
TLQDTKCEV 23 285.163 80
ILFLRNVNV 25 437.482 71
YIILFLRNV 27 76.897 68
VLILHYHLL 24 54.474 64
QLFVTVVDT 21 63.417 62
RLVWACAGV 23 69.552 62
L2 P06793 TLIEDSSVV 24 116.917 88
YLWPLYYFI 25 3286.176 69

Table 4: Consensus epitopes predicted in HPV 16 and 18

HPV protein Amino acid position/Peptide Sequence

HPV 16_L1

HPV 18_L1




HPV 16_E6

HPV 18_E6




Letters in BOLD indicates single amino acid variation in HPV 16 and 18.

Identification of consensus peptide

A total of 45 overlapping peptides were predicted in this study, among five peptides were considered as consensus peptides (table 4); 100% sequence similarity was found in L1 peptide-QLFVTVVDT354-662 and four other peptides exhibited a single amino acid variation (HPV 16 E6 peptide-KLPQLCTEL18-29 and L1 peptide-RLVWACVGV123-131; HPV 18 E6 peptide-KLPDLCTEL13-21 and L1 peptide-RLVWACAGV158-66).

Molecular docking

The reference peptide binds with HLA-A2 with a binding energy of-2.37 kcal/mol and the interaction is mediated through two hydrogen bonds. The peptides, QLF and KLP, bind with HLA-A2 with the binding energies of -3.57 kcal/mol and -3.55 kcal/mol respectively, indicating that these two predicted peptides bind efficiently than the reference peptide.

The interaction of QLF with HLA-A2 is through five hydrogen bonds (fig. 5), whereas the interaction of the reference peptide is only by two hydrogen bonds; this confirms that the binding of QLF is stronger than that of the reference peptide. The interacting residues are presented in table 5. The binding poses of the QLF peptide along with the reference peptide is shown in fig. 6. This indicates that the QLF peptide binds at the same binding site (peptide binding groove) where the reference peptide binds.

Fig. 5: Interactions of QLF with HLA-A2

Fig. 6: Binding of QLF peptide and the reference peptide with HLA-A2

Table 5: Interaction of consensus peptides with MHC


Binding energy


No of hydrogen bonds formed

Interacting residues


GILGFVFTL (Reference peptide from influenza) -2.37 2

Arg97 Val6

Trp147 Leu3


Tyr99 Thr9

Arg97 Val7

Lys66 Gln1

Arg97 Thr9

Lys66 Phe3


Arg65 Glu8

Arg97 Leu9


Modern immunoinformatics tools provide the new platform for designing peptide vaccines against pathogenic microorganisms [33]. Though many tools are available for predicting immunogenic CTL epitopes, the accuracy of any of these tools is not appreciative. Hence with a concept that a combination of two or more tools could solve the problem [45]; this study was undertaken with three well-known matrix based algorithms. The specificity and sensitivity of the algorithms were evaluated using a known set of binders and non-binders, and the results indicated that combination of algorithms increased the specificity without affecting the sensitivity of the tested tools.

Based on this approach, a total of 249 (10.42%) binders were predicted out of 2388 peptides in HPV 16. Similarly, 215 (8.91%) binders were predicted out of 2412 peptides analyzed in HPV 18. Among the predicted epitopes, 45 were promiscuous overlapping peptides that were predicted by all three algorithms. Some of the peptides predicted in the study were already reported as CTL epitopes. HPV 16 peptides E1-LLQQYCLYL254-262 [34], E5-FLLCFCVLL15-23, VLLCVCLLI21-29 [35], E6-KLPQLCTEL18-26 [36, 37] and E7-TLHEYMLDL7-15 [38] are known CTL epitopes. E7-LLMGTLGIV82-90 was known to induce the cellular response in HLA A2.1 rabbit model [39] and reduced tumor burden in aged mice [40]. E6-KLPDLCTEL13-22 [41] and E7-TLQDIVLHL 7-15 [42] were proved to be CTL epitopes for HPV 18.

One of the predicted peptides, QLFVTVVDT 354-662 from L1 protein is conserved in both HPV 16 and 18 genotypes. Peptide KLPQLCTEL18-26 from E6 has a single amino acid variation in the fourth position; the variation glutamine (HPV 16) instead of aspartic acid (HPV 18) has already been reported [8]. Similarly, alanine (HPV 16) instead of valine (HPV 18) was observed in L1-RLVWACAGV158-166 at the 7th position. The results were further confirmed using docking studies of the peptide with the MHC.


The results of the present study revealed the use of computational algorithms in the prediction of CTL epitopes based on the binding to MHC Class I MHC molecules. Combination of more than one tool increases the chance to predict potent CTL epitopes against viral diseases. Using this approach few epitopes were predicted for HPV 16 and 18. Further confirmation of the efficacy of these epitopes in inducing a stronger immune response needs to be done based on in vitro and in vivo assays


The work was supported by a grant from Science and Engineering Research Board, New Delhi (SR/SO/HS-0248/2012) to Krishnan Sundar. Manikandan Mohan thank Indian Council of Medical Research, New Delhi for a Senior Research Fellowship (45/18/2011-IMM-BMS). The authors thanks, Mrs. J. Christina Rosy for her help in docking studies.


All the in silico analyses and written part of the manuscript was carried out by the first author Mr. Manikandan Mohan. The study was conceived, designed, correction and communications of the manuscript were done by the corresponding author Prof. Krishnan Sundar.


The authors declared that they have no conflict of interests


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About this article




Epitope prediction, CTL epitopes, Human papilloma virus, BIMAS, SYFPEITHI, RANKPEP





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International Journal of Pharmacy and Pharmaceutical Sciences
Vol 9, Issue 11, 2017 Page: 175-182

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Authors & Affiliations

Manikandan Mohan
Department of Biotechnology, Kalasalingam University, Krishnankoil - 626 126 Tamilnadu, India

Krishnan Sundar
Department of Biotechnology, Kalasalingam University, Krishnankoil - 626 126 Tamilnadu, India

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