DOI QR코드

DOI QR Code

Prediction of Promiscuous Epitopes in the E6 Protein of Three High Risk Human Papilloma Viruses: A Computational Approach

  • Published : 2013.07.30

Abstract

A najor current challenge and constraint in cervical cancer research is the development of vaccines against human papilloma virus (HPV) epitopes. Although many studies are done on epitope identification on HPVs, no computational work has been carried out for high risk forms which are considered to cause cervical cancer. Of all the high risk HPVs, HPV 16, HPV 18 and HPV 45 are responsible for 94% of cervical cancers in women worldwide. In this work, we computationally predicted the promiscuous epitopes among the E6 proteins of high risk HPVs. We identified the conserved residues, HLA class I, HLA class II and B-cell epitopes along with their corresponding secondary structure conformations. We used extremely precise bioinformatics tools like ClustalW2, MAPPP, NetMHC, Epi,Jen, EpiTop 1.0, ABCpred, BCpred and PSIPred for achieving this task. Our study identified specific regions 'FAFR(K)DL' followed by 'KLPD(Q)LCTEL' fragments which proved to be promiscuous epitopes present in both human leukocyte antigen (HLA) class I, class II molecules and B cells as well. These fragments also follow every suitable character to be considered as promiscuous epitopes with supporting evidences of previously reported experimental results. Thus, we conclude that these regions should be considered as the important for design of specific therapeutic vaccines for cervical cancer.

Keywords

References

  1. Ackerman AL, Cresswell P (2004). Cellular mechanisms governing cross-presentation of exogenous antigens. Nature Immunology, 5, 678-84. https://doi.org/10.1038/ni1082
  2. Aidan M, Ulrich K, Charles, et al (2009). T-cell epitope prediction: rescaling can mask biological variation between MHC molecules. PLoS Comput Biol, 5, 1000327. https://doi.org/10.1371/journal.pcbi.1000327
  3. Agarwal SM, Raghav D, Singh H, et al (2011). CCDB: a curated database of genes involved in cervix cancer. Nucleic Acids Res, 39, 975-9. https://doi.org/10.1093/nar/gkq1024
  4. Agnieszka K, Grabowska, Riemer AB (2012). The invisible enemy-how human papillomaviruses avoid recognition and clearance by the host immune system. Open Virol J, 6, 249-56. https://doi.org/10.2174/1874357901206010249
  5. Alessia M, Marco T, Nello C (2009). Support vector machines. wiley interdisciplinary reviews: computation stat. Computational Statistics, 1, 283-9. https://doi.org/10.1002/wics.49
  6. Altschul S, Madden T, Schaffer A, et al (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res, 25, 3389-402. https://doi.org/10.1093/nar/25.17.3389
  7. Androphyl EJ, Hubbert NL, Schiller JT, et al (1987). Identification of the HPV-16 E6 protein from transformed mouse cells and human cervical carcinoma cell lines. EMBO J, 6, 989-92.
  8. Bernard HU, Burk RD, Chen Z, et al (2010). Classification of Papillomaviruses (PVs) based on 189 PV types and proposal of taxonomic amendments. Virology, 401, 70-9. https://doi.org/10.1016/j.virol.2010.02.002
  9. Bian H, Olson JFR, Hammer J (2003). The use of bioinformatics for identifying class II-restricted T-cell epitopes. Methods, 29, 299-309. https://doi.org/10.1016/S1046-2023(02)00352-3
  10. Boccardo E, Lepique AP, Luisa L (2010). The role of inflammation in HPV carcinogenesis. Carcin, 31, 1905-12. https://doi.org/10.1093/carcin/bgq176
  11. Brusic V, Bajic VB, Petrovsky N (2004). Computational methods for prediction of T-cell epitopes-a framework for modeling, testing, and applications. Method, 34, 436-43. https://doi.org/10.1016/j.ymeth.2004.06.006
  12. Chandra S, Singh TR (2012). Linear B cell epitope prediction for epitope vaccine design against meningococcal disease and their computational validations through physicochemical properties. Netw Model Anal Health Inform Bioinforma, 1, 153-9. https://doi.org/10.1007/s13721-012-0019-1
  13. Cole ST, Danos O (1987). Nucleotide sequence and comparative analysis of the human papillomavirus type 18 genome. Phylogeny of papillomavirus and repeated structure of the E6 and E7 gene products. J Mol Bio, 193, 599-608. https://doi.org/10.1016/0022-2836(87)90343-3
  14. De Groot A S (2004). Immunome derived vaccine. Expert Opin Biol Ther, 4, 767-72. https://doi.org/10.1517/14712598.4.6.767
  15. Delius H, Hofmann B (1994). Primer-directed sequencing of human papillomavirus types. Curr Top Microbial Immunol, 186, 13-31.
  16. De Sanjose S, Quint WGV, Alemany L, et al (2010). Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study. Lancet Oncol, 11, 1048-56. https://doi.org/10.1016/S1470-2045(10)70230-8
  17. Dimitrov I, Garnev P, Flower DR (2010). EpiTop-a proteochemometric tool for MHC class II binding prediction. Bioinformatics, 26, 2066-8. https://doi.org/10.1093/bioinformatics/btq324
  18. Doytchinova IA, Guan P, Flower DR (2006). EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics, 7, 131. https://doi.org/10.1186/1471-2105-7-131
  19. Engelhard VH (1994). How cells process antigens. Sci Am, 271, 54-61.
  20. Fakhry C, Gillison ML (2006). Clinical implications of human papillomavirus in head and neck cancers. J Clin Oncol, 24, 2606-11. https://doi.org/10.1200/JCO.2006.06.1291
  21. Flower DR, Phadwal K, Macdonald IK, et al (2010). T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges. Immunome Res, 6, 2-4. https://doi.org/10.1186/1745-7580-6-2
  22. Frazer IH (2004). Prevention of cervical cancer through papillomavirus vaccination. Nature Rev Immun, 4, 46-54. https://doi.org/10.1038/nri1260
  23. Gallagher KME, Man S (2007). Identification of HLA-DR1- and HLA-DR15-restricted human papillomavirus type 16 (HPV16) and HPV18 E6 epitopes recognized by CD4+ T cells from healthy young women. J General Virology, 88, 1470-8. https://doi.org/10.1099/vir.0.82558-0
  24. Gnanamony M, Peedicayil A, Abraham P (2007). An overview of Human Papillomavirus and current vaccine strategies. Indian J Med Microbiol, 25, 10-17. https://doi.org/10.4103/0255-0857.31055
  25. Govan VA (2008). A novel vaccine for cervical cancer: quadrivalent human papillomavirus (types 6, 11, 16 and 18) recombinant vaccine (Gardasil). Ther Clin Risk Manage, 4, 65-70.
  26. Hakenberg J, Nussbaum A, Schild H, et al (2003). MAPPP-MHC-I antigenic peptide processing prediction. Appl Bioinformatics, 2, 155-8.
  27. Hellberg S, Sjostrom M, Skagerberg B, et al (1987). Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem, 30, 1126-35. https://doi.org/10.1021/jm00390a003
  28. Holzhutter HG, Frommel C, Kloetzel PM (1999). A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome. J Mol Bio, 286, 1251-65. https://doi.org/10.1006/jmbi.1998.2530
  29. Iurescia S, Fioretti D, Fazio VM, et al (2012). Epitope-driven DNA vaccine design employing immunoinformatics against B-cell lymphoma: a biotech's challenge. Biotechnol Adv, 30, 372-83. https://doi.org/10.1016/j.biotechadv.2011.06.020
  30. Jacob AG, Hansen NC, Vinther J, et al. (2003). A promoter within the E6 ORF of human papillomavirus type 16 contributes to the expression of the E7 oncoprotein from a monocistronic mRNA. J Gen Virol, 84, 3429-41. https://doi.org/10.1099/vir.0.19250-0
  31. Jones DT (1999). Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol, 292, 195-202. https://doi.org/10.1006/jmbi.1999.3091
  32. Kavitha KV, Saritha R, Vinod Chandra SS (2013). Computational methods in linear b-cell epitope prediction. Int J Comput Appl, 63, 28-32.
  33. Khan HA, Arif IA, Bahkali AH, et al (2008). Bayesian, maximum parsimony and UPGMA models for inferring the phylogenies of antelopes using mitochondrial markers. Evol Bioinform Online, 4, 263-70.
  34. Koichiro T, Joel D, Masatoshi N, et al (2007). MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol, 24, 1596-9. https://doi.org/10.1093/molbev/msm092
  35. Larkin MA, Blackshields G, Brown NP, et al (2007). Clustal w and clustal X version 2.0. Bioinformatics, 23, 2947-48. https://doi.org/10.1093/bioinformatics/btm404
  36. Lapinsh M, Prusis P, Gutcaits A, et al (2001). Development of proteochemometrics: A novel technology of use for analysis of drug-receptor interactions. Biochim Biophys Acta, 1525, 180-90. https://doi.org/10.1016/S0304-4165(00)00187-2
  37. Lin SY, Cheng CW, Su ECY (2013). Prediction of B-cell epitopes using evolutionary information and propensity scales. BMC Bioinformatics, 14, 2-10. https://doi.org/10.1186/1471-2105-14-2
  38. Longworth MS, Laimins LA (2004). Pathogenesis of human papillomaviruses in differentiating epithelia. Microbiol Mol Biol R, 68, 362-72. https://doi.org/10.1128/MMBR.68.2.362-372.2004
  39. Lundegaard C, Lamberth K, Harndah M, et al (2008). NetMHC-3.0: Accurate web accessible predictions of human, mouse, and monkey MHC class i affinities for peptides of length 8-11. Nucleic Acids Res, 36, 509-12. https://doi.org/10.1093/nar/gkn202
  40. Ma B, Xu Y, Hung CF, et al (2010). HPV and Therapeutic Vaccines: Where are we in 2010? Curr Cancer Ther Rev, 6, 81-103. https://doi.org/10.2174/157339410791202583
  41. Ma B, Roden R, Wu TC (2010). Current status of HPV vaccines. J Formos Med Assoc, 109, 481-3. https://doi.org/10.1016/S0929-6646(10)60081-2
  42. Mark HE, Suzanne L, Lydia GC (2009). Genetic variants in TAP are associated with high-grade cervical neoplasia. Clin Cancer Res, 15, 1019-23. https://doi.org/10.1158/1078-0432.CCR-08-1207
  43. Morrow MP, Yan J, Sardesai NY (2013). Human papillomavirus therapeutic vaccines: targeting viral antigens as immunotherapy for precancerous disease and cancer. Expert Rev Vaccines, 12, 271-83. https://doi.org/10.1586/erv.13.23
  44. Munger K, Howley PM (2002). Human papillomavirus immortalization and transformation functions. Virus Res, 89, 213-28. https://doi.org/10.1016/S0168-1702(02)00190-9
  45. Nakagawa M, Kim KH, Moscicki AB (2004). Different methods of identifying new antigenic epitopes of human papillomavirus type 16 E6 and E7 Proteins. Clin Diagn Lab Immun, 11, 889-96.
  46. Nakagawa M, Kim KH, Gillam TM, et al (2007). HLA class I binding promiscuity of the CD8 T-Cell epitopes of human papillomavirus type 16 E6 protein. J Virol, 81, 1412-23. https://doi.org/10.1128/JVI.01768-06
  47. Nielsen M, Lundegaard C, Woming P, et al (2003). Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci, 12, 1007-17. https://doi.org/10.1110/ps.0239403
  48. Nielsen M, Lundegaard C, Woming P, et al (2004). Improved prediction of MHC class I and II epitopes using a novel Gibbs sampling approach. Bioinformatics, 20, 1388-97. https://doi.org/10.1093/bioinformatics/bth100
  49. Oyarzun P, Ellis JJ, Boden M, et al (2013). PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity. BMC Bioinformatics, 14, 52. https://doi.org/10.1186/1471-2105-14-52
  50. Patronov A, Doytchinova I (2013). T-cell epitope vaccine design by immunoinformatics. Open Biol, 3, 120139. https://doi.org/10.1098/rsob.120139
  51. Peters B, Sidney J, Bourne P, et al (2005). The design and implementation of the immune epitope database and analysis resource. Immunogenetics, 57, 326-36. https://doi.org/10.1007/s00251-005-0803-5
  52. Pingping S, Wenhan C, Yanxin H, et al (2011). Epitope prediction based on random peptide library screening: benchmark dataset and prediction tools evaluation. Molecules, 16, 4971-94. https://doi.org/10.3390/molecules16064971
  53. Rudolf MP, Man S, Melief CJM, et al (2001). Human T-Cell responses to HLA-A-restricted high binding affinity peptides of human papillomavirus type 18 proteins E6 and E7. Clin Cancer Res, 7, 788-95.
  54. Saha S, Raghava GPS (2006). Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins, 65, 40-8. https://doi.org/10.1002/prot.21078
  55. Seedorf K, Krammer G, Durst M, et al (1985). Human papillomavirus type 16 DNA sequence. Virology, 145, 181-5. https://doi.org/10.1016/0042-6822(85)90214-4
  56. Sette A, Sidney J, Oseroff C, et al (1993). HLA DR4w4-binding motifs illustrate the biochemical basis of degeneracy and specificity in peptide-DR interactions. J Immunol, 151, 3163-70.
  57. Shehzadi A, Rehman SU, Idrees M, (2011). Promiscuous prediction and conservancy analysis of CTL binding epitopes of HCV 3a viral proteome from Punjab Pakistan: an In-silico approach. Virol J, 8, 55. https://doi.org/10.1186/1743-422X-8-55
  58. Smith KL, Tristram A, Gallagher KM, et al (2004). Epitope specificity and longevity of a vaccine-induced human T cell response against HPV18. Int Immunol, 17, 167-76. https://doi.org/10.1093/intimm/dxh197
  59. Soliman PT, Slomovitz BM, Wolf JK (2004). Mechanisms of cervical cancer. Drug Discovery today: Disease mechanism, 1, 253-8.
  60. Sirskyj D, Mitoma FD, Golshani A, et al (2011). Innovative bioinformatic approaches for developing peptide-based vaccines against hypervariable viruses. Immunol Cell Biol, 89, 81-9. https://doi.org/10.1038/icb.2010.65
  61. Sudandiradoss C, George Priya Doss C, Rajasekaran R, et al (2008). Analysis of binding residues between scorpion neurotoxins and D2 dopamine receptor: a computational docking study. Comput Biol Med, 38, 1056-67. https://doi.org/10.1016/j.compbiomed.2008.08.003
  62. Stanley MA (2009). Immune responses to human papilloma viruses. Indian J Med Res, 130, 266-76.
  63. The Universal protein resource (Uniprot) in 2010. Nucleic Acids Res, 38, 142-8.
  64. Tomar N, De RK (2010). Immunoinformatics: an integrated scenario. Immunol, 131, 153-68. https://doi.org/10.1111/j.1365-2567.2010.03330.x
  65. Tong JC, Tan TW, Shoba R (2006). Methods and protocols for prediction of immunogenic epitopes. Briefings in Bioinformatics, 8, 96-108. https://doi.org/10.1093/bib/bbl038
  66. Tumban E, Peabody J, Peabody DS, et al (2011). A pan-HPV vaccine based on bacteriophage PP7 VLPs displaying broadly cross-neutralizing epitopes from the HPV minor capsid protein, L2. PLoS One, 6, 23310. https://doi.org/10.1371/journal.pone.0023310
  67. Waldmann TA (2003). Immune responses to human Papilloma viruses. Nat Med, 9, 269-77. https://doi.org/10.1038/nm0303-269
  68. Walshe VA, Hattotuwagama CK, Doytchinova IA, et al (2009). Integrating in silico and in vitro analysis of peptide binding affinity to HLA-$Cw^{\ast}0102$: a bioinformatic approach to the prediction of new epitopes. PLoS One, 4, 8095. https://doi.org/10.1371/journal.pone.0008095
  69. Whelan S, Lio P, Goldman N (2001). Molecular phylogenetics: state-of-art methods for looking into the past. Trends Genet, 17, 262-72. https://doi.org/10.1016/S0168-9525(01)02272-7
  70. Wu CY, Monie A, Pang X, Hung CF, Wu TC (2010). Improving therapeutic HPV peptide-based vaccine potency by enhancing CD4+ T help and dendritic cell activation. J Biomed Sci, 17, 88-97. https://doi.org/10.1186/1423-0127-17-88
  71. Yao B, Zheng D, Liang S, et al (2013). Conformational B-Cell epitope prediction on antigen protein structures: a review of current algorithms and comparison with common binding site prediction methods. PLoS One, 8, 62249 https://doi.org/10.1371/journal.pone.0062249
  72. Yasser ELM, Dobbs D, Honavar V (2008). Predicting linear B-cell epitopes using string kernels. J Mol Recognit, 21, 243-55 https://doi.org/10.1002/jmr.893
  73. Zhang W, Niu Y (2010). Presented at 3rd International conference on biomedical engineering and informatics. BMEI, 10, 5640578.

Cited by

  1. Reversal of Resistance towards Cisplatin by Curcumin in Cervical Cancer Cells vol.15, pp.3, 2014, https://doi.org/10.7314/APJCP.2014.15.3.1403
  2. Pathways and Delayed Cyclin B1 Nuclear Translocation vol.15, pp.9, 2014, https://doi.org/10.7314/APJCP.2014.15.9.4101
  3. In silico prediction of epitopes for Chikungunya viral strains vol.45, pp.6, 2015, https://doi.org/10.1007/s40005-015-0205-0
  4. Analysis of L1/L2 Sequences of Human Papillomaviruses: Implication for Universal Vaccine Design vol.30, pp.3, 2017, https://doi.org/10.1089/vim.2016.0142
  5. In Silico Analysis of Synaptonemal Complex Protein 1 (SYCP1) and Acrosin Binding Protein (ACRBP) Antigens to Design Novel Multiepitope Peptide Cancer Vaccine Against Breast Cancer pp.1573-3904, 2018, https://doi.org/10.1007/s10989-018-9780-z