Modern Developments in Short Peptide Viral Vaccine Design

  • Christina Nilofer
  • Mohanapriya Arumugam
  • Pandjassarame KangueaneEmail author


Vaccine design and development against viral diseases is multifaceted. Classical vaccines (live-attenuated vaccines, inactivated vaccines, subunit, recombinant, polysaccharide, conjugate vaccines, and toxoid vaccines) are often less effective for many viral diseases. Hence, short peptide (10–20 amino acid residues) vaccine components exploiting T-cell mediated immunity have been recognized as alternative solutions. This involves the specific binding of short antigen peptides to allele (gene variant) specific host human leukocyte antigens (HLA). Allele-specific HLA typing among different ethnic groups has gained momentum in recent years through advances in sequencing (Next Generation Sequencing (NGS)), High Performance Computing (HPC), machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques. More than 20,000 HLA alleles have been typed, defined, named, and made available at the IMGT®/HLA database (Immuno Polymorphism Database – ImMunoGeneTics Database/Human Leukocyte Antigen) for public access. Identification of short peptide antigens capable of binding specifically to the human host HLA alleles is now possible using computer-aided HLA-peptide binding prediction methods. This is achieved using three dimensional HLA structure based molecular modeling, and known HLA-peptide binding data enabled machine learning techniques like ANN and SVM. The former provides broad coverage across HLA alleles and the later offers high accuracy with high specificity for limited HLA alleles. Thus, the combined use of structural features, molecular modeling, machine learning techniques, and other applied mathematical models including Quantitative matrices (QM), Bayesian Networks (BN), and Hidden Markov Models (HMM) help in the effective design of short peptide vaccine components and immune therapeutics for the prevention and control of diseases caused by viruses. Hence, we outline recent advances in HLA-peptide binding prediction for short peptide vaccine design.


Epitope T-cell receptor NGS HPC SVM HMM ANN Quantitative matrix HLA typing Sequence Nomenclature ANN Vaccine Short vaccine peptide Vaccine design Alleles Polymorphism Molecular modeling Structure 



Artificial Neural Network


Evolutionary Algorithm


Human Leukocyte Antigens


Hidden Markov Model


High Performance Computing


the international ImMunoGeneTics information systemdatabase


Next Generation Sequencing


Quantitative Matrices


Quantitative Structure Activity Relationship


Support Vector Machine



We wish to express our sincere appreciation to all members of Biomedical Informatics (P) Ltd. and Department of Biotechnology, Anna University, Chennai for discussion on the subject of this chapter for Global Virology III. We thank Paul Shapshak, PhD, for his critical comments, suggestions and useful edits of the content of this chapter.


  1. 1.
    Kangueane P, Viswapoorani K, Nilofer C, Manimegalai S, Sivagamy M, Kangueane U, Sowmya G, Sakharkar MK. In: Shapshak P, Levine A, Foley B, Somboonwit C, editors. Chapter 35: short oligo-peptide T-cell epitopes in HIV-1/AIDS vaccine development: current status, design, promises and challenges, book chapter in global virology II – HIV and NeuroAIDS. 2017 1st ed. New York: Springer; 2018. 978-1-4939-7288-3 (ISBN).Google Scholar
  2. 2.
    Kangueane P, Sowmya G, Anupriya S, Dangeti S, Mathura VS, Sakharkar MK. Short peptide vaccine design: promises and challenges in book titled “Global virology I – identifying and investigating viral diseases”, vol. 1. New York: Springer; 2015. p. 1–14. ISBN 978-1-4939-2410-3.CrossRefGoogle Scholar
  3. 3.
    Kangueane P, Kayathri R, Sakharkar MK, Flower DR, Sadler K, et al. Designing HIV gp120 peptide vaccines: rhetoric or reality for NeuroAIDS, Chapter 9. In: Goodkin K, Shapshak P, Verma A, editors. The spectrum of Neuro-AIDS disorders: pathophysiology, diagnosis, and treatment. ISBN: 9781555813697; ISBN10: 1555813690. Washington, DC: ASM Press; 2008. p. 105–19.Google Scholar
  4. 4.
    Shapshak P, Kangueane P, Fujimura RK, Commins D, Chiappelli F, Singer E, Levine AJ, Minagar A, Novembre FJ, Somboonwit C, Nath A, Sinnott JT. Editorial NeuroAIDS review. AIDS. 2011;25:123–41.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Sowmya G, Vaishnai A, Kangueane P. Structure modeling based computer aided T-cell epitope design. Bio-Algorithms and Med-Systems. 2008;4:5–13.Google Scholar
  6. 6.
    Kangueane P, Sakharkar M. HLA-peptide binding prediction using structural and modeling principles. Methods Mol Biol. 2007b;409:293–9.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Kangueane P, Sakharkar M, Lim K, Hao H, Lin K, Chee R, Kolatkar P. Knowledge-based grouping of modeled HLA peptide complexes. Hum Immunol. 2000;61:460–6.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Zhao B, Mathura V, Rajaseger G, Moochhala S, Sakharkar M, Kangueane P. A novel MHCp binding prediction model. Hum Immunol. 2003b;64:1123–43.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Mohanapriya A, Lulu S, Kayathri R, Kangueane P. Class II HLA-peptide binding prediction using structural principles. Hum Immunol. 2009;70:159–69.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kangueane P, Sakharkar M. T-Epitope Designer: a HLA-peptide binding prediction server. Bioinformation. 2005b;1:21–4.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Zhao B, Sakharkar KR, Lim CS, Kangueane P, Sakharkar MK. MHC-peptide binding prediction for epitope based vaccine design. Int J Integr Biol. 2007;1(2):127–40.Google Scholar
  12. 12.
  13. 13.
    Blackwell JM, Jamieson SE, Burgner D. HLA and infectious diseases. Clin Microbiol Rev. 2009;22:370–85.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Sheldon S, Poulton K. HLA typing and its influence on organ transplantation. Methods Mol Biol. 2006;333:157–74.PubMedPubMedCentralGoogle Scholar
  15. 15.
    Berger A. HLA typing. BMJ. 2001;322:218.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Ren E, Kangueane P, Kolatkar P, Lin M, Tseng L, Hansen J. Molecular modeling of the minor histocompatibility antigen HA-1 peptides binding to HLA-A alleles. Tissue Antigens. 2000;55:24–30.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
  18. 18.
    Kangueane P, Sakharkar M, Kolatkar P, Ren E. Towards the MHC-peptide combinatorics. Hum Immunol. 2001;62:539–56.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
  20. 20.
    Govindarajan K, Kangueane P, Tan T, Ranganathan S. MPID: MHC-Peptide Interaction Database for sequence-structure-function information on peptides binding to MHC molecules. Bioinformatics. 2003;19:309–10.CrossRefGoogle Scholar
  21. 21.
    Sukhwal A, Sowdhamini R. Oligomerization status and evolutionary conservation of interfaces of protein structural domain superfamilies. Mol BioSyst. 2013;9:1652–61.CrossRefGoogle Scholar
  22. 22.
    Sukhwal A, Sowdhamini R. PPCheck: a webserver for the quantitative analysis of protein-protein interfaces and prediction of residue hotspot. Bioinform Biol Insights. 2015;9:141–51.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Adrian P, Rajaseger G, Mathura V, Sakharkar M, Kangueane P. Types of inter-atomic interactions at the MHC-peptide interface: identifying commonality from accumulated data. BMC Struct Biol. 2002;2:2.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Brusic V, Rudy G, Harrison LC. MHCPEP: a database of MHC-binding peptides. Nucleic Acids Res. 1994;22(17):3663–5. PubMed PMID: 7937075; PubMed Central PMCID: PMC308338.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Bhasin M, Singh H, Raghava GP. MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics. 2003;19(5):665–6.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Rammensee HG, Friede T, Stevanovic S. MHC ligands and peptide motifs: 1st listing. Immunogenetics. 1995;41:178–228.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Zhao B, Png A, Ren E, Kolatkar P, Mathura V, Sakharkar M, Kangueane P. Compression of functional space in HLA-A sequence diversity. Hum Immunol. 2003a;64:718–28.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Kangueane P, Sakharkar M. Grouping of class-I HLA alleles using electrostatic distribution maps of the peptide binding grooves. Methods Mol Biol. 2007a;409:175–81.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Mohanapriya A, Nandagond S, Shapshak P, Kangueane U, Kangueane P. A HLA-DRB supertype chart with potential overlapping peptide binding function. Bioinformation. 2010;4:300–9.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Kangueane P, Sakharkar M, Rajaseger G, Bolisetty S, Sivasekari B, Zhao B, Ravichandran M, Shapshak P, Subbiah S. A framework to sub-type HLA supertypes. Front Biosci. 2005a;10:879–86.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Kangueane P, Sakharkar M. Structural basis for HLA-A2 supertypes. Methods Mol Biol. 2007c;409:155–62.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Agatonovic KS, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22:717–27.CrossRefGoogle Scholar
  33. 33.
    Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–97.Google Scholar
  34. 34.
  35. 35.
    Adams HP, Koziol JA. Prediction of binding to MHC class I molecules. J Immunol Methods. 1995;18:181.CrossRefGoogle Scholar
  36. 36.
    Gulukota K, Sidney J, Sette A, DeLisi C. Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J Mol Biol. 1997;26:1258.CrossRefGoogle Scholar
  37. 37.
    Trolle T, Metushi IG, Greenbaum JA, Kim Y, Sidney J, Lund O, et al. Automated benchmarking of peptide-MHC class I binding predictions. Bioinformatics. 2015;31:2174–81.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Bhasin M, Raghava GP. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine. 2004;22:3195–204.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Singh H, Raghava GP. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics. 2003;19:1009–14.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Hakenberg J, Nussbaum AK, Schild H, Rammensee HG, Kuttler C, Holzhütter HG, et al. MAPPP: MHC class I antigenic peptide processing prediction. Appl Bioinforma. 2003;2:155–8.Google Scholar
  41. 41.
  42. 42.
  43. 43.
  44. 44.
  45. 45.
  46. 46.
    Guan P, Doytchinova IA, Zygouri C, Flower DR. MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res. 2003;31:3621–4.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Bhasin M, Raghava GP. Prediction of promiscuous and high-affinity mutated MHC binders. Hybrid Hybridomics. 2003;22:229–34.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
  49. 49.
  50. 50.
  51. 51.
    Reche PA, Glutting JP, Reinherz EL. Prediction of MHC class I binding peptides using profile motifs. Hum Immunol. 2002;63:701–9.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
  53. 53.
  54. 54.
  55. 55.
  56. 56.
    Keşmir C, Nussbaum AK, Schild H, Detours V, Brunak S. Prediction of proteasome cleavage motifs by neural networks. Protein Eng. 2002;15:287–96.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Nussbaum AK, Kuttler C, Hadeler KP, Rammensee HG, Schild H. PAProC: a prediction algorithm for proteasomal cleavages available on the WWW. Immunogenetics. 2001;53:87–94.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Brusic V, Zeleznikow J. Artificial neural network applications in immunology. Proceedings of the 1999 International Joint Conference on Neural Networks IJCNN’99. 1999;2034. ISBN:0-7803-5532-6Google Scholar
  59. 59.
    Milik M, Sauer D, Brunmark AP, Yuan L, Vitiello A, Jackson MR, et al. Application of an artificial neural network to predict specific class I MHC binding peptide sequences. Nat Biotechnol. 1998;16:753.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Buus S, Lauemøller SL, Worning P, Kesmir C, Frimurer T, Corbet S, et al. Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens. 2003;62:378–84.CrossRefGoogle Scholar
  61. 61.
    Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. Neural models for predicting viral vaccine targets. J Bioinforma Comput Biol. 2005;3:1207–25.CrossRefGoogle Scholar
  62. 62.
    Brusic V, Rudy G, Honeyman G, Hammer J, Harrison L. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics. 1998;14:121–30.CrossRefGoogle Scholar
  63. 63.
    Choi SE, Park CW, Sohn YH, Ko SY, Oh HB, Kim GH, et al. Artificial neural network weights of residues for the serological specificities of HLA. Int J Immunogenet. 2011;38:269–75.CrossRefGoogle Scholar
  64. 64.
    Bellgard MI, Tay GK, Hiew HL, Witt CS, Ketheesan N, Christiansen FT, et al. MHC haplotype analysis by artificial neural networks. Hum Immunol. 1998;59:56–62.CrossRefGoogle Scholar
  65. 65.
    Honeyman MC, Brusic V, Stone NL, Harrison LC. Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol. 1998;16:966–9.CrossRefGoogle Scholar
  66. 66.
    Brusic V, Bucci K, Schönbach C, Petrovsky N, Zeleznikow J, Kazura JW. Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. J Mol Graph Model. 2001;19:405–11.CrossRefGoogle Scholar
  67. 67.
    Zhang GL, Bozic I, Kwoh CK, August JT, Brusic V. Prediction of supertype-specific HLA class I binding peptides using support vector machines. J Immunol Methods. 2007;320:143–54.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Soam SS, Khan F, Bhasker B, Mishra BN. Prediction of MHC class I binding peptides using probability distribution functions. Bioinformation. 2009;3:403–8.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Astakhov V, Cherkasov A. Prediction of HLA-A2 binding peptides using Bayesian network. Bioinformation. 2005;1:58–63.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Noguchi H, Kato R, Hanai T, Matsubara Y, Honda H, Brusic V, et al. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J Biosci Bioeng. 2002;94(3):264–70.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Singh SP, Mishra BN. Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network. Bioinformation. 2008;3:150–5.CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Bhasin M, Raghava GP. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J Biosci. 2007;32:31–42.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christina Nilofer
    • 1
    • 2
  • Mohanapriya Arumugam
    • 2
  • Pandjassarame Kangueane
    • 1
    Email author
  1. 1.Peptide Vaccine DesignBiomedical Informatics (P) LtdPuducherryIndia
  2. 2.BiotechnologySchool of Bio Sciences and Technology, VIT UniversityKatpadi, VelloreIndia

Personalised recommendations