Exploration of Immunology

Table 4. Rank measure analysis of MHC class I predictors (PSSM and ANN) based on the NetCTLpan dataset.

S. No.	MHC class I allele	No. of antigen	Average relative rank of epitopes in their source antigen (%)
S. No.	MHC class I allele	No. of antigen	PSSM	ANN	NetMHCpan 4.0 (EL)	NetMHCpan 4.0 (BA)
1	HLA-A*0101	29	0.36	1.84	0.3246	0.3159
2	HLA-A*0201	254	3.30	4.17	3.1785	17.405
3	HLA-A*1101	14	1.72	0.80	0.399	0.4765
4	HLA-A*6801	12	3.05	3.11	0.9291	0.907
5	HLA-A*0301	65	2.77	3.68	1.5704	1.4966
6	HLA-B*0702	25	1.59	4.71	0.6781	0.9225
7	HLA-B*4501	14	2.83	3.66	0.7079	0.8363
Average			2.23	3.13	1.11	3.19

Return to article view

MHC: major histocompatibility complex; PSSM: position-specific scoring matrices; ANN: artificial neural networks; EL: eluted ligands; BA: binding affinity.

Supplementary materials

The supplementary materials for this article are available at: https://www.explorationpub.com/uploads/Article/file/1003215_sup_1.pdf.

Declarations

Acknowledgments

The authors are thankful to Prof. Brijesh Pandey, Mahatma Gandhi Central University, Motihari, for the valuable suggestions in revising the manuscript.

Author contributions

SPS: Conceptualization, Methodology, Investigation, Writing—review & editing. GS: Data curation, Investigation. BNM: Supervision, Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Supplemental information can be found online at https://data.mendeley.com/datasets/dxz3dk3tcm/1 at Mendeley data (DOI: 10.17632/dxz3dk3tcm.1). Data are licensed under an Attribution-NonCommercial 3.0 Unported licence. All figures in the study were created using the MS-Excel program.

Funding

Not applicable.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

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;112:123–31. [DOI] [PubMed] [PMC]

Gilbert SC. T-cell-inducing vaccines - what’s the future. Immunology. 2012;135:19–26. [DOI] [PubMed] [PMC]

Rudolph MG, Stanfield RL, Wilson IA. How TCRs bind MHCs, peptides, and coreceptors. Annu Rev Immunol. 2006;24:419–66. [DOI] [PubMed]

Kägi D, Ledermann B, Bürki K, Seiler P, Odermatt B, Olsen KJ, et al. Cytotoxicity mediated by T cells and natural killer cells is greatly impaired in perforin-deficient mice. Nature. 1994;369:31–7. [DOI] [PubMed]

Lankat-Buttgereit B, Tampé R. The transporter associated with antigen processing: function and implications in human diseases. Physiol Rev. 2002;82:187–204. [DOI] [PubMed]

Craiu A, Akopian T, Goldberg A, Rock KL. Two distinct proteolytic processes in the generation of a major histocompatibility complex class I-presented peptide. Proc Natl Acad Sci U S A. 1997;94:10850–5. [DOI] [PubMed] [PMC]

van Endert PM, Riganelli D, Greco G, Fleischhauer K, Sidney J, Sette A, et al. The peptide-binding motif for the human transporter associated with antigen processing. J Exp Med. 1995;182:1883–95. [DOI] [PubMed] [PMC]

Schatz MM, Peters B, Akkad N, Ullrich N, Martinez AN, Carroll O, et al. Characterizing the N-terminal processing motif of MHC class I ligands. J Immunol. 2008;180:3210–7. [DOI] [PubMed]

Matsumura M, Fremont DH, Peterson PA, Wilson IA. Emerging principles for the recognition of peptide antigens by MHC class I molecules. Science. 1992;257:927–34. [DOI] [PubMed]

10.

Pamer E, Cresswell P. Mechanisms of MHC class I--restricted antigen processing. Annu Rev Immunol. 1998;16:323–58. [DOI] [PubMed]

11.

Yewdell JW, Bennink JR. Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Annu Rev Immunol. 1999;17:51–88. [DOI] [PubMed]

12.

Zhong W, Reche PA, Lai CC, Reinhold B, Reinherz EL. Genome-wide characterization of a viral cytotoxic T lymphocyte epitope repertoire. J Biol Chem. 2003;278:45135–44. [DOI] [PubMed]

13.

Tang Y, Wang H. Construction and immunogenicity prediction of Plasmodium falciparum CTL epitope minigene vaccine. Sci China C Life Sci. 2001;44:207–15. [DOI] [PubMed]

14.

Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res. 2005;33:W172–9. [DOI] [PubMed] [PMC]

15.

Kar P, Ruiz-Perez L, Arooj M, Mancera RL. Current methods for the prediction of T‐cell epitopes. Pept Sci. 2018;110:e24046. [DOI]

16.

Gowthaman U, Agrewala JN. In silico methods for predicting T-cell epitopes: Dr Jekyll or Mr Hyde? Expert Rev Proteomics. 2009;6:527–37. [DOI] [PubMed]

17.

Yang X, Yu X. An introduction to epitope prediction methods and software. Rev Med Virol. 2009;19:77–96. [DOI] [PubMed]

18.

Peters B, Sidney J, Bourne P, Bui H, Buus S, Doh G, et al. The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol. 2005;3:e91. [DOI] [PubMed] [PMC]

19.

Zhang L, Udaka K, Mamitsuka H, Zhu S. Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools. Brief Bioinform. 2012;13:350–64. [DOI] [PubMed]

20.

Bhasin M, Raghava GPS. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine. 2004;22:3195–204. [DOI] [PubMed]

21.

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. [DOI] [PubMed] [PMC]

22.

Lin HH, Ray S, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol. 2008;9:8. [DOI] [PubMed] [PMC]

23.

Paul S, Sidney J, Sette A, Peters B. TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates. Curr Protoc Immunol. 2016;114:18.19.1–24. [DOI] [PubMed] [PMC]

24.

Dimitrov I, Garnev P, Flower DR, Doytchinova I. MHC Class II Binding Prediction-A Little Help from a Friend. J Biomed Biotechnol. 2010;2010:705821. [DOI] [PubMed] [PMC]

25.

Hobohm U, Scharf M, Schneider R, Sander C. Selection of representative protein data sets. Protein Sci. 1992;1:409–17. [DOI] [PubMed] [PMC]

26.

Henikoff S, Henikoff JG. Position-based sequence weights. J Mol Biol. 1994;243:574–8. [DOI] [PubMed]

27.

Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003;12:1007–17. [DOI] [PubMed] [PMC]

28.

Doytchinova IA, Guan P, Flower DR. EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics. 2006;7:131. [DOI] [PubMed] [PMC]

29.

Singh H, Raghava GPS. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics. 2003;19:1009–14. [DOI] [PubMed]

30.

Jaishwal P, Jha K, Singh SP. Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing. Int J Biol Macromol. 2024;277:134012. [DOI] [PubMed]

31.

Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One. 2007;2:e796. [DOI] [PubMed] [PMC]

32.

Gribskov M, McLachlan AD, Eisenberg D. Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci U S A. 1987;84:4355–8. [DOI] [PubMed] [PMC]

33.

Lüthy R, Xenarios I, Bucher P. Improving the sensitivity of the sequence profile method. Protein Sci. 1994;3:139–46. [DOI] [PubMed] [PMC]

34.

Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402. [DOI] [PubMed] [PMC]

35.

Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics. 2004;20:1388–97. [DOI] [PubMed]

36.

Reche PA, Reinherz EL. Definition of MHC supertypes through clustering of MHC peptide-binding repertoires. Methods Mol Biol. 2007;409:163–73. [DOI] [PubMed]

37.

Brusic V, van Endert P, Zeleznikow J, Daniel S, Hammer J, Petrovsky N. A neural network model approach to the study of human TAP transporter. In Silico Biol. 1999;1:109–21. [PubMed]

38.

Brusic V, Zeleznikow J, Sturniolo T, Bono E, Hammer J. Data cleaning for computer models: a case study from immunology. Proceedings of the 6th International Conference on Neural Information Processing (ICONIP’99/ANZIIS'99/ANNES’99/ACNN’99); 1999 Nov 22–24; Perth, Australia. Piscataway (NJ): IEEE; 1999. pp. 603–9. [DOI]

39.

Lund O, Nielsen M, Lundegaard C, Kesmir C, Brunak S. Immunological Bioinformatics. Cambridge: The MIT Press; 2005.

40.

Zhao W, Sher X. Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes. PLoS Comput Biol. 2018;14:e1006457. [DOI] [PubMed] [PMC]

41.

Rumelhart DE, Hinton GE, Williams RJ. Parallel distributed processing: explorations in the microstructure of cognition. Cambridge: MIT Press; 1986. pp. 318–62.

42.

Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285–93. [DOI] [PubMed]

43.

Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform. 2005;38:404–15. [DOI] [PubMed]

44.

Wohlwend J, Nathan A, Shalon N, Crain CR, Tano-Menka R, Goldberg B, et al. Deep learning enhances the prediction of HLA class I-presented CD8⁺ T cell epitopes in foreign pathogens. Nat Mach Intell. 2025;7:232–43. [DOI] [PubMed] [PMC]

45.

Larsen MV, Lundegaard C, Lamberth K, Buus S, Brunak S, Lund O, et al. An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol. 2005;35:2295–303. [DOI] [PubMed]

46.

Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics. 2007;8:424. [DOI] [PubMed] [PMC]

47.

Stranzl T, Larsen MV, Lundegaard C, Nielsen M. NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics. 2010;62:357–68. [DOI] [PubMed] [PMC]

48.

Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanović S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 1999;50:213–9. [DOI] [PubMed]

49.

Grifoni A, Sidney J, Zhang Y, Scheuermann RH, Peters B, Sette A. A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. Cell Host Microbe. 2020;27:671–80.e2. [DOI] [PubMed] [PMC]

50.

Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, et al. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8⁺ T-cell epitopes. Cell Syst. 2023;14:72–83.e5. [DOI] [PubMed] [PMC]

51.

Fleri W, Paul S, Dhanda SK, Mahajan S, Xu X, Peters B, et al. The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design. Front Immunol. 2017;8:278. [DOI] [PubMed] [PMC]

52.

Peters B, Bulik S, Tampe R, Endert PMV, Holzhütter H. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J Immunol. 2003;171:1741–9. [DOI] [PubMed]

53.

Doytchinova I, Hemsley S, Flower DR. Transporter associated with antigen processing preselection of peptides binding to the MHC: a bioinformatic evaluation. J Immunol. 2004;173:6813–9. [DOI] [PubMed]

54.

Toes RE, Nussbaum AK, Degermann S, Schirle M, Emmerich NP, Kraft M, et al. Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products. J Exp Med. 2001;194:1–12. [DOI] [PubMed] [PMC]

55.

Kuttler C, Nussbaum AK, Dick TP, Rammensee HG, Schild H, Hadeler KP. An algorithm for the prediction of proteasomal cleavages. J Mol Biol. 2000;298:417–29. [DOI] [PubMed]

56.

Schneider TD, Stephens RM. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 1990;18:6097–100. [DOI] [PubMed] [PMC]

57.

Rapin N, Hoof I, Lund O, Nielsen M. MHC motif viewer. Immunogenetics. 2008;60:759–65. [DOI] [PubMed] [PMC]

58.

Sidney J, Peters B, Frahm N, Brander C, Sette A. HLA class I supertypes: a revised and updated classification. BMC Immunol. 2008;9:1. [DOI] [PubMed] [PMC]

59.

Dzuris JL, Sidney J, Appella E, Chesnut RW, Watkins DI, Sette A. Conserved MHC class I peptide binding motif between humans and rhesus macaques. J Immunol. 2000;164:283–91. [DOI] [PubMed]

60.

Frahm N, Yusim K, Suscovich TJ, Adams S, Sidney J, Hraber P, et al. Extensive HLA class I allele promiscuity among viral CTL epitopes. Eur J Immunol. 2007;37:2419–33. [DOI] [PubMed] [PMC]

61.

Lundegaard C, Lund O, Buus S, Nielsen M. Major histocompatibility complex class I binding predictions as a tool in epitope discovery. Immunology. 2010;130:309–18. [DOI] [PubMed] [PMC]

62.

Sette A, Vitiello A, Reherman B, Fowler P, Nayersina R, Kast WM, et al. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol. 1994;153:5586–92. [PubMed]

63.

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. [DOI] [PubMed] [PMC]

64.

Paul S, Weiskopf D, Angelo MA, Sidney J, Peters B, Sette A. HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J Immunol. 2013;191:5831–9. [DOI] [PubMed] [PMC]

65.

van der Burg SH, Visseren MJ, Brandt RM, Kast WM, Melief CJ. Immunogenicity of peptides bound to MHC class I molecules depends on the MHC-peptide complex stability. J Immunol. 1996;156:3308–14. [PubMed]

66.

Harndahl M, Rasmussen M, Roder G, Pedersen ID, Sørensen M, Nielsen M, et al. Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. Eur J Immunol. 2012;42:1405–16. [DOI] [PubMed]

67.

Bonsack M, Hoppe S, Winter J, Tichy D, Zeller C, Küpper MD, et al. Performance Evaluation of MHC Class-I Binding Prediction Tools Based on an Experimentally Validated MHC-Peptide Binding Data Set. Cancer Immunol Res. 2019;7:719–36. [DOI] [PubMed]

68.

Roomp K, Antes I, Lengauer T. Predicting MHC class I epitopes in large datasets. BMC Bioinformatics. 2010;11:90. [DOI] [PubMed] [PMC]

69.

Zhang H, Lundegaard C, Nielsen M. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics. 2009;25:83–9. [DOI] [PubMed] [PMC]

70.

Singh SP, Mishra BN. Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network. Bioinformation. 2008;3:150–5. [DOI] [PubMed] [PMC]

71.

Wang M, Claesson MH. Classification of human leukocyte antigen (HLA) supertypes. Methods Mol Biol. 2014;1184:309–17. [DOI] [PubMed] [PMC]

72.

Shen Y, Parks JM, Smith JC. HLA Class I Supertype Classification Based on Structural Similarity. J Immunol. 2023;210:103–14. [DOI] [PubMed]

73.

Singh SP, Mishra BN. Major histocompatibility complex linked databases and prediction tools for designing vaccines. Hum Immunol. 2016;77:295–306. [DOI] [PubMed]

74.

Tscharke DC, Croft NP, Doherty PC, Gruta NLL. Sizing up the key determinants of the CD8(+) T cell response. Nat Rev Immunol. 2015;15:705–16. [DOI] [PubMed]

75.

Nosrati M, Mohabatkar H, Behbahani M. Introducing of an integrated artificial neural network and Chou’s pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens. Int Immunopharmacol. 2020;78:106020. [DOI] [PubMed]

76.

Grifoni A, Weiskopf D, Ramirez SI, Mateus J, Dan JM, Moderbacher CR, et al. Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals. Cell. 2020;181:1489–501.e15. [DOI] [PubMed] [PMC]

77.

Nielsen M, Lundegaard C, Lund O, Keşmir C. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics. 2005;57:33–41. [DOI] [PubMed]

78.

Brouwenstijn N, Serwold T, Shastri N. MHC class I molecules can direct proteolytic cleavage of antigenic precursors in the endoplasmic reticulum. Immunity. 2001;15:95–104. [DOI] [PubMed]

79.

van Hall T, van Bergen J, van Veelen PA, Kraakman M, Heukamp LC, Koning F, et al. Identification of a novel tumor-specific CTL epitope presented by RMA, EL-4, and MBL-2 lymphomas reveals their common origin. J Immunol. 2000;165:869–77. [DOI] [PubMed]

80.

Chen W, Norbury CC, Cho Y, Yewdell JW, Bennink JR. Immunoproteasomes shape immunodominance hierarchies of antiviral CD8(+) T cells at the levels of T cell repertoire and presentation of viral antigens. J Exp Med. 2001;193:1319–26. [DOI] [PubMed] [PMC]

81.

Assarsson E, Sidney J, Oseroff C, Pasquetto V, Bui H, Frahm N, et al. A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection. J Immunol. 2007;178:7890–901. [DOI] [PubMed]

82.

Holzhütter HG, Frömmel C, Kloetzel PM. A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome. J Mol Biol. 1999;286:1251–65. [DOI] [PubMed]

83.

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. [DOI] [PubMed]

84.

Hakenberg J, Nussbaum AK, Schild H, Rammensee H, Kuttler C, Holzhütter H, et al. MAPPP: MHC class I antigenic peptide processing prediction. Appl Bioinformatics. 2003;2:155–8. [PubMed]

85.

Reche PA, Reinherz EL. PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands. Nucleic Acids Res. 2005;33:W138–42. [DOI] [PubMed] [PMC]

86.

Brusic V, Bajic VB, Petrovsky N. Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications. Methods. 2004;34:436–43. [DOI] [PubMed]

87.

Heemels MT, Ploegh H. Generation, translocation, and presentation of MHC class I-restricted peptides. Annu Rev Biochem. 1995;64:463–91. [DOI] [PubMed]

88.

Dönnes P, Kohlbacher O. Integrated modeling of the major events in the MHC class I antigen processing pathway. Protein Sci. 2005;14:2132–40. [DOI] [PubMed] [PMC]

89.

Tenzer S, Peters B, Bulik S, Schoor O, Lemmel C, Schatz MM, et al. Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell Mol Life Sci. 2005;62:1025–37. [DOI] [PubMed] [PMC]

90.

Adotévi O, Mollier K, Neuveut C, Cardinaud S, Boulanger E, Mignen B, et al. Immunogenic HLA-B*0702-restricted epitopes derived from human telomerase reverse transcriptase that elicit antitumor cytotoxic T-cell responses. Clin Cancer Res. 2006;12:3158–67. [DOI] [PubMed]

91.

Hundemer M, Schmidt S, Condomines M, Lupu A, Hose D, Moos M, et al. Identification of a new HLA-A2-restricted T-cell epitope within HM1.24 as immunotherapy target for multiple myeloma. Exp Hematol. 2006;34:486–96. [DOI] [PubMed] [PMC]

92.

Mustafa AS, Shaban FA. ProPred analysis and experimental evaluation of promiscuous T-cell epitopes of three major secreted antigens of Mycobacterium tuberculosis. Tuberculosis (Edinb). 2006;86:115–24. [DOI] [PubMed]

93.

Sundar K, Boesen A, Coico R. Computational prediction and identification of HLA-A2.1-specific Ebola virus CTL epitopes. Virology. 2007;360:257–63. [DOI] [PubMed]

94.

Wen J, Jiang L, Zhou J, Yan H, Fang D. Computational prediction and identification of dengue virus-specific CD4(+) T-cell epitopes. Virus Res. 2008;132:42–8. [DOI] [PubMed] [PMC]

95.

Baruah V, Bose S. Immunoinformatics-aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019-nCoV. J Med Virol. 2020;92:495–500. [DOI] [PubMed] [PMC]

96.

Wang M, Lamberth K, Harndahl M, Røder G, Stryhn A, Larsen MV, et al. CTL epitopes for influenza A including the H5N1 bird flu; genome-, pathogen-, and HLA-wide screening. Vaccine. 2007;25:2823–31. [DOI] [PubMed]

97.

Perez EE, Wang J, Miller JC, Jouvenot Y, Kim KA, Liu O, et al. Establishment of HIV-1 resistance in CD4+ T cells by genome editing using zinc-finger nucleases. Nat Biotechnol. 2008;26:808–16. [DOI] [PubMed] [PMC]

98.

Tang ST, Wang M, Lamberth K, Harndahl M, Dziegiel MH, Claesson MH, et al. MHC-I-restricted epitopes conserved among variola and other related orthopoxviruses are recognized by T cells 30 years after vaccination. Arch Virol. 2008;153:1833–44. [DOI] [PubMed] [PMC]

99.

Lehnert E, Tampé R. Structure and Dynamics of Antigenic Peptides in Complex with TAP. Front Immunol. 2017;8:10. [DOI] [PubMed] [PMC]

100.

Calis JJA, Maybeno M, Greenbaum JA, Weiskopf D, Silva ADD, Sette A, et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol. 2013;9:e1003266. [DOI] [PubMed] [PMC]

101.

Lasso P, Cárdenas C, Guzmán F, Rosas F, Thomas MC, López MC, et al. Effect of secondary anchor amino acid substitutions on the immunogenic properties of an HLA-A*0201-restricted T cell epitope derived from the Trypanosoma cruzi KMP-11 protein. Peptides. 2016;78:68–76. [DOI] [PubMed]

102.

Rasmussen M, Fenoy E, Harndahl M, Kristensen AB, Nielsen IK, Nielsen M, et al. Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity. J Immunol. 2016;197:1517–24. [DOI] [PubMed] [PMC]

103.

Antunes DA, Devaurs D, Moll M, Lizée G, Kavraki LE. General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept. Sci Rep. 2018;8:4327. [DOI] [PubMed] [PMC]

104.

Kuai R, Ochyl LJ, Bahjat KS, Schwendeman A, Moon JJ. Designer vaccine nanodiscs for personalized cancer immunotherapy. Nat Mater. 2017;16:489–96. [DOI] [PubMed] [PMC]

105.

Kapadia CH, Perry JL, Tian S, Luft JC, DeSimone JM. Nanoparticulate immunotherapy for cancer. J Control Release. 2015;219:167–80. [DOI] [PubMed]

106.

Bissati KE, Zhou Y, Paulillo SM, Raman SK, Karch CP, Roberts CW, et al. Protein nanovaccine confers robust immunity against Toxoplasma. NPJ Vaccines. 2017;2:24. [DOI] [PubMed] [PMC]

107.

Zhu X, Vo C, Taylor M, Smith BR. Non-spherical micro-and nanoparticles in nanomedicine. Mater Horiz. 2019;6:1094–121. [DOI]

108.

Liu J, Miao L, Sui J, Hao Y, Huang G. Nanoparticle cancer vaccines: Design considerations and recent advances. Asian J Pharm Sci. 2020;15:576–90. [DOI] [PubMed] [PMC]

109.

Jha K, Jaishwal P, Yadav TP, Singh SP. Self-assembling of coiled-coil peptides into virus-like particles: Basic principles, properties, design, and applications with special focus on vaccine design and delivery. Biophys Chem. 2025;318:107375. [DOI] [PubMed]

110.

Saleh T, Shojaosadati SA. Multifunctional nanoparticles for cancer immunotherapy. Hum Vaccin Immunother. 2016;12:1863–75. [DOI] [PubMed] [PMC]

111.

Schudel A, Francis DM, Thomas SN. Material design for lymph node drug delivery. Nat Rev Mater. 2019;4:415–28. [DOI] [PubMed] [PMC]

112.

Mu R, Zhu D, Abdulmalik S, Wijekoon S, Wei G, Kumbar SG. Stimuli-responsive peptide assemblies: Design, self-assembly, modulation, and biomedical applications. Bioact Mater. 2024;35:181–207. [DOI] [PubMed] [PMC]