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image of Immunoproteomics: Approach to Diagnostic and Vaccine Development

Abstract

The study of large protein sets (proteomics) involved in the immunological reaction is known as immunoproteomics. The methodology of immunoproteomics plays a major role in identifying possible vaccine candidates that could protect against pathogenic infection. The study of immunogenic proteins that are expressed during the outset of infection is the focus of the cross-talk between proteomics and immune protection antigens utilizing serum. Peptide presentation by MHC provides the new ‘window’ into changes that occur in the cell. Thus, there is strong, intense pressure on the pathogen that has been mutated in such an unusual manner that it can bypass the MHC peptide presentation by the MHC molecule. The pathogen's ability to evade the immune system is strongly restricted by the two unique distinct properties of MHC molecules, , polygenic and polymorphic properties. MHC-I restriction epitope identification has traditionally been accomplished using genetic motif prediction. The study of immune system proteins and their interactions is the main emphasis of the specialist field of immunoproteomics within proteomics. Methodologies include mass spectrometry (MS), SRM assay, MALDI-TOF, Chromatography, ELISA, 2DG PAGE, and bioinformatics tools. Challenges are the complexity of the immune system, protein abundance and dynamics, sample variability, post-translational modifications (PTMs), and data integration. Current advancements are enhanced mass spectrometry techniques, single-cell proteomics, artificial intelligence and machine learning, advanced protein labeling techniques, integration with other omics technologies, and functional proteomics. However, the recently emerging field of immunoproteomics has more promising possibilities in the field of peptide-based vaccines and virus-like particle vaccines. The importance of immunoproteomics technologies and methodologies, as well as their use in the field of vaccinomics, are the main topics of this review. Here, we have discussed immunoproteomics in relation to a step towards the future of vaccination.

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2024-10-25
2024-11-22

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References

  1. Gomase V. Kale K. Tagore S. Hatture S. Proteomics: Technologies for protein analysis. Curr. Drug Metab. 2008 9 3 213 220 10.2174/138920008783884740 18336224
    [Google Scholar]
  2. Jungblut PR Proteome analysis of bacterial pathogens. Microbes Infect. 2004 3 10 831 840 10.1016/S1286‑4579(01)01441‑1
    [Google Scholar]
  3. Trommer J. Lesniowski F. Buchner J. Svilenov H.L. Specific features of a scaffolding antibody light chain. Protein Sci. 2024 33 5 e4990 10.1002/pro.4990 38607241
    [Google Scholar]
  4. Duan Z. Baughn L.B. Wang X. Zhang Y. Gupta V. MacCarthy T. Scharff M.D. Yu G. Role of Dot1L and H3K79 methylation in regulating somatic hypermutation of immunoglobulin genes. Proc. Natl. Acad. Sci. USA 2021 118 29 e2104013118 10.1073/pnas.2104013118 34253616
    [Google Scholar]
  5. Jenkins G.W. Safonova Y. Smider V.V. Germline-encoded positional cysteine polymorphisms enhance diversity in antibody ultralong CDR H3 regions. J. Immunol. 2022 209 11 2141 2148 10.4049/jimmunol.2200455 36426974
    [Google Scholar]
  6. Jenner E. An inquiry into causes and effects of variolae vaccinae, a disease, discovered in some of the Western countries of England, particularly Gloucestershire, and known by the name of cow pox. 1798 84
    [Google Scholar]
  7. Cohen S. McGREGOR I.A. Carrington S. Gamma-globulin and acquired immunity to human malaria. Nature 1961 192 4804 733 737 10.1038/192733a0 13880318
    [Google Scholar]
  8. Baxby D. Edward Jenner’s Inquiry; A bicentenary analysis. Vaccine 1999 17 4 301 307 10.1016/S0264‑410X(98)00207‑2 9987167
    [Google Scholar]
  9. Tuells J. Vaccinology: The name, the concept, the adjectives. Vaccine 2012 30 37 5491 5495 10.1016/j.vaccine.2012.06.059 22766245
    [Google Scholar]
  10. Rosenkranz M. Nkumama I.N. Ogwang R. Kraker S. Blickling M. Mwai K. Odera D. Tuju J. Fürle K. Frank R. Chepsat E. Kapulu M.C. Osier F.H.A. Full-length MSP1 is a major target of protective immunity after controlled human malaria infection. Life Sci. Alliance 2024 7 8 e202301910 10.26508/lsa.202301910 38803222
    [Google Scholar]
  11. Gbaguidi M.L.E. Adamou R. Edslev S. Hansen A. Domingo N.D. Dechavanne C. Massougbodji A. Garcia A. Theisen M. Milet J. Donadi E.A. Courtin D. IgG and IgM responses to the Plasmodium falciparum asexual stage antigens reflect respectively protection against malaria during pregnancy and infanthood. Malar. J. 2024 23 1 154 10.1186/s12936‑024‑04970‑7 38764069
    [Google Scholar]
  12. Rappuoli R. Reverse vaccinology. Curr. Opin. Microbiol. 2000 3 5 445 450 10.1016/S1369‑5274(00)00119‑3 11050440
    [Google Scholar]
  13. Cuypers B. Rappuoli R. Brozzi A. A lean reverse vaccinology pipeline with publicly available bioinformatic tools. Methods Mol. Biol. 2023 2673 341 356 10.1007/978‑1‑0716‑3239‑0_24 37258926
    [Google Scholar]
  14. Delany I. Rappuoli R. De Gregorio E. Vaccines for the 21st century. EMBO Mol. Med. 2014 6 6 708 720 10.1002/emmm.201403876 24803000
    [Google Scholar]
  15. Finco O. Rappuoli R. Designing vaccines for the twenty-first century society. Front. Immunol. 2014 5 12 10.3389/fimmu.2014.00012 24478777
    [Google Scholar]
  16. Singh G. Rana A. Smriti Decoding antimicrobial resistance: Unraveling molecular mechanisms and targeted strategies. Arch. Microbiol. 2024 206 6 280 10.1007/s00203‑024‑03998‑2 38805035
    [Google Scholar]
  17. Rappuoli R. Black S. Lambert P.H. Vaccine discovery and translation of new vaccine technology. Lancet 2011 378 9788 360 368 10.1016/S0140‑6736(11)60440‑6 21664687
    [Google Scholar]
  18. Guo X. Pan X. Sun Q. Hu Y. Shi J. Design of a novel multiepitope vaccine against Chlamydia pneumoniae using the extracellular protein as a target. Sci. Rep. 2023 13 1 15070 10.1038/s41598‑023‑42222‑x 37700027
    [Google Scholar]
  19. Keith J.A. Agostini Bigger L. Arthur P.A. Maes E. Daems R. Delivering the promise of the Decade of Vaccines: Opportunities and challenges in the development of high quality new vaccines. Vaccine 2013 31 2 Suppl. 2 B184 B193 10.1016/j.vaccine.2012.12.032 23598480
    [Google Scholar]
  20. Decouttere C. De Boeck K. Vandaele N. Advancing sustainable development goals through immunization: A literature review. Global. Health 2021 17 1 95 10.1186/s12992‑021‑00745‑w 34446050
    [Google Scholar]
  21. Grimm S.K. Ackerman M.E. Vaccine design: Emerging concepts and renewed optimism. Curr. Opin. Biotechnol. 2013 24 6 1078 1088 10.1016/j.copbio.2013.02.015 23474232
    [Google Scholar]
  22. Yurina V. Adianingsih O.R. Predicting epitopes for vaccine development using bioinformatics tools. Ther. Adv. Vaccines Immunother. 2022 10 10.1177/25151355221100218 35647486
    [Google Scholar]
  23. Rosendahl Huber S. van Beek J. de Jonge J.ø. Luytjes W. van Baarle D. T cell responses to viral infections - Opportunities for Peptide vaccination. Front. Immunol. 2014 5 171 10.3389/fimmu.2014.00171 24795718
    [Google Scholar]
  24. Bukhari S.N.H. Elshiekh E. Abbas M. Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets. PeerJ Comput. Sci. 2024 10 e1980 10.7717/peerj‑cs.1980 38686005
    [Google Scholar]
  25. Dennehy R. McClean S. Immunoproteomics: The key to discovery of new vaccine antigens against bacterial respiratory infections. Curr. Protein Pept. Sci. 2012 13 8 807 815 10.2174/138920312804871184 23305366
    [Google Scholar]
  26. Croucher N.J. Immune interface interference vaccines: An evolution‐informed approach to anti‐bacterial vaccine design. Microb. Biotechnol. 2024 17 3 e14446 10.1111/1751‑7915.14446 38536702
    [Google Scholar]
  27. Kidd P. Th1/Th2 balance: The hypothesis, its limitations, and implications for health and disease. Altern. Med. Rev. 2003 8 3 223 246 12946237
    [Google Scholar]
  28. Wardhani K. Yazzie S. McVeigh C. Edeh O. Grimes M. Jacquez Q. Dixson C. Barr E. Liu R. Bolt A.M. Feng C. Zychowski K.E. Systemic immunological responses are dependent on sex and ovarian hormone presence following acute inhaled woodsmoke exposure. Part. Fibre Toxicol. 2024 21 1 27 10.1186/s12989‑024‑00587‑5 38797836
    [Google Scholar]
  29. Kilmury S.L.N. Twine S.M. The francisella tularensis proteome and its recognition by antibodies. Front. Microbiol. 2011 1 143 10.3389/fmicb.2010.00143 21687770
    [Google Scholar]
  30. D’haeseleer P. Collette N.M. Lao V. Segelke B.W. Branda S.S. Franco M. Shotgun immunoproteomic approach for the discovery of linear B-cell epitopes in biothreat agents Francisella tularensis and Burkholderia pseudomallei. Front. Immunol. 2021 12 716676 10.3389/fimmu.2021.716676 34659206
    [Google Scholar]
  31. Uthailak N. Adisakwattana P. Thiangtrongjit T. Limpanont Y. Chusongsang P. Chusongsang Y. Tanasarnprasert K. Reamtong O. Discovery of Schistosoma mekongi circulating proteins and antigens in infected mouse sera. PLoS One 2022 17 10 e0275992 10.1371/journal.pone.0275992 36227939
    [Google Scholar]
  32. Krautz-Peterson G. Debatis M. Tremblay J.M. Oliveira S.C. Da’dara A.A. Skelly P.J. Shoemaker C.B. Schistosoma mansoni infection of mice, rats and humans elicits a strong antibody response to a limited number of reduction-sensitive epitopes on five major tegumental membrane proteins. PLoS Negl. Trop. Dis. 2017 11 1 e0005306 10.1371/journal.pntd.0005306 28095417
    [Google Scholar]
  33. Chen J.H. Zhang T. Ju C. Xu B. Lu Y. Mo X.J. Chen S.B. Fan Y.T. Hu W. Zhou X.N. An integrated immunoproteomics and bioinformatics approach for the analysis of Schistosoma japonicum tegument proteins. J. Proteomics 2014 98 289 299 10.1016/j.jprot.2014.01.010 24448400
    [Google Scholar]
  34. Wangsanut T. Pongpom M. Human-fungal pathogen interactions from the perspective of immunoproteomics analyses. Int. J. Mol. Sci. 2024 25 6 3531 10.3390/ijms25063531 38542504
    [Google Scholar]
  35. Farnell E.J. Tyagi N. Ryan S. Chalmers I.W. Pinot de Moira A. Jones F.M. Wawrzyniak J. Fitzsimmons C.M. Tukahebwa E.M. Furnham N. Maizels R.M. Dunne D.W. Known allergen structures predict Schistosoma mansoni IgE-binding antigens in human infection. Front. Immunol. 2015 6 26 10.3389/fimmu.2015.00026 25691884
    [Google Scholar]
  36. Udoye C.C. Ehlers M. Manz R.A. The B cell response and formation of allergenic and anti-allergenic antibodies in food allergy. Biology (Basel) 2023 12 12 1501 10.3390/biology12121501 38132327
    [Google Scholar]
  37. Riveau G. Deplanque D. Remoué F. Schacht A.M. Vodougnon H. Capron M. Thiry M. Martial J. Libersa C. Capron A. Safety and immunogenicity of rSh28GST antigen in humans: Phase 1 randomized clinical study of a vaccine candidate against urinary schistosomiasis. PLoS Negl. Trop. Dis. 2012 6 7 e1704 10.1371/journal.pntd.0001704 22802974
    [Google Scholar]
  38. Zumuk C.P. Jones M.K. Navarro S. Gray D.J. You H. Transmission-blocking vaccines against Schistosomiasis japonica. Int. J. Mol. Sci. 2024 25 3 1707 10.3390/ijms25031707 38338980
    [Google Scholar]
  39. Diemert D.J. Pinto A.G. Freire J. Jariwala A. Santiago H. Hamilton R.G. Periago M.V. Loukas A. Tribolet L. Mulvenna J. Correa-Oliveira R. Hotez P.J. Bethony J.M. Generalized urticaria induced by the Na-ASP-2 hookworm vaccine: Implications for the development of vaccines against helminths. J. Allergy Clin. Immunol. 2012 130 1 169 176.e6 10.1016/j.jaci.2012.04.027 22633322
    [Google Scholar]
  40. Gomase V. Tagore S. Transcriptomics. Curr. Drug Metab. 2008 9 3 245 249 10.2174/138920008783884759 18336229
    [Google Scholar]
  41. Kaur R. Arora N. Nair M.G. Prasad A. The interplay of helminthic neuropeptides and proteases in parasite survival and host immunomodulation. Biochem. Soc. Trans. 2022 50 1 107 118 10.1042/BST20210405 35076687
    [Google Scholar]
  42. Gomase V.S. Chitlange N.R. Immunoproteomics approach for development of synthetic peptide vaccine from Mycobacterium tuberculosis. J. Immunol. Res. 2009 1 1 7 12
    [Google Scholar]
  43. Piao X. Duan J. Jiang N. Liu S. Hou N. Chen Q. Schistosoma japonicum tyrosine hydroxylase is promising targets for immunodiagnosis and immunoprotection of Schistosomiasis japonica. PLoS Negl. Trop. Dis. 2023 17 6 e0011389 10.1371/journal.pntd.0011389 37276235
    [Google Scholar]
  44. de la Maza L.M. Zhong G. Brunham R.C. Update on Chlamydia trachomatis vaccinology. Clin. Vaccine Immunol. 2017 24 4 e00543 e16 10.1128/CVI.00543‑16 28228394
    [Google Scholar]
  45. Lim H.X. Lim J. Poh C.L. Identification and selection of immunodominant B and T cell epitopes for dengue multi-epitope-based vaccine. Med. Microbiol. Immunol. (Berl.) 2021 210 1 1 11 10.1007/s00430‑021‑00700‑x 33515283
    [Google Scholar]
  46. Comber J.D. Philip R. MHC class I antigen presentation and implications for developing a new generation of therapeutic vaccines. Ther. Adv. Vaccines 2014 2 3 77 89 10.1177/2051013614525375 24790732
    [Google Scholar]
  47. Gibadullin R. Morris R.K. Niu J. Sidney J. Sette A. Gellman S.H. Thioamide analogues of MHC I antigen peptides. J. Am. Chem. Soc. 2023 145 47 25559 25569 10.1021/jacs.3c05300 37968794
    [Google Scholar]
  48. Hawlina S. Zorec R. Chowdhury H.H. Potential of personalized dendritic cell-based immunohybridoma vaccines to treat prostate cancer. Life (Basel) 2023 13 7 1498 10.3390/life13071498 37511873
    [Google Scholar]
  49. Stenger R.M. Meiring H.D. Kuipers B. Poelen M. van Gaans-van den Brink J.A.M. Boog C.J.P. de Jong A.P.J.M. van Els C.A.C.M. Bordetella pertussis proteins dominating the major histocompatibility complex class II-presented epitope repertoire in human monocyte-derived dendritic cells. Clin. Vaccine Immunol. 2014 21 5 641 650 10.1128/CVI.00665‑13 24599530
    [Google Scholar]
  50. Zepp F. Knuf M. Habermehl P. Schmitt H.J. Meyer C. Clemens R. Slaoui M. Cell-mediated immunity after pertussis vaccination and after natural infection. Dev. Biol. Stand. 1997 89 307 314 9272364
    [Google Scholar]
  51. Brummelman J. Wilk M.M. Han W.G.H. van Els C.A.C.M. Mills K.H.G. Roads to the development of improved pertussis vaccines paved by immunology. Pathog. Dis. 2015 73 8 ftv067 10.1093/femspd/ftv067 26347400
    [Google Scholar]
  52. Heinekamp T. Schmidt H. Lapp K. Pähtz V. Shopova I. Köster-Eiserfunke N. Krüger T. Kniemeyer O. Brakhage A.A. Interference of Aspergillus fumigatus with the immune response. Semin. Immunopathol. 2015 37 2 141 152 10.1007/s00281‑014‑0465‑1 25404120
    [Google Scholar]
  53. Earle K. Valero C. Conn D.P. Vere G. Cook P.C. Bromley M.J. Bowyer P. Gago S. Pathogenicity and virulence of Aspergillus fumigatus. Virulence 2023 14 1 2172264 10.1080/21505594.2023.2172264 36752587
    [Google Scholar]
  54. Boamah D. Kikuchi M. Huy N.T. Okamoto K. Chen H. Ayi I. Boakye D.A. Bosompem K.M. Hirayama K. Immunoproteomics identification of major IgE and IgG4 reactive Schistosoma japonicum adult worm antigens using chronically infected human plasma. Trop. Med. Health 2012 40 3 89 102 10.2149/tmh.2012‑16 23264728
    [Google Scholar]
  55. Gomase V.S. Chitlange N.R. Antigen protein from Schistosoma mansoni: New paradigm of synthetic vaccine development. Int. J. Pharma Bio Sci. 2010 1 3 BS43
    [Google Scholar]
  56. Zhu Q. Liu M. Dai L. Ying X. Ye H. Zhou Y. Han S. Zhang J.Y. Using immunoproteomics to identify tumor-associated antigens (TAAs) as biomarkers in cancer immunodiagnosis. Autoimmun. Rev. 2013 12 12 1123 1128 10.1016/j.autrev.2013.06.015 23806562
    [Google Scholar]
  57. Li T. Xia J. Yun H. Sun G. Shen Y. Wang P. Shi J. Wang K. Yang H. Ye H. A novel autoantibody signatures for enhanced clinical diagnosis of pancreatic ductal adenocarcinoma. Cancer Cell Int. 2023 23 1 273 10.1186/s12935‑023‑03107‑1 37974212
    [Google Scholar]
  58. Álvarez-Fernández S.M. De Monte L. Alessio M. Natural antibodies to tumor-associated antigens. Methods Mol. Biol. 2016 1393 11 25 10.1007/978‑1‑4939‑3338‑9_2 27033212
    [Google Scholar]
  59. Gillette M.A. Carr S.A. Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nat. Methods 2013 10 1 28 34 10.1038/nmeth.2309 23269374
    [Google Scholar]
  60. Harlan R. Zhang H. Targeted proteomics: A bridge between discovery and validation. Expert Rev. Proteomics 2014 11 6 657 661 10.1586/14789450.2014.976558 25348939
    [Google Scholar]
  61. Carbonara K. Andonovski M. Coorssen J.R. Proteomes are of proteoforms: Embracing the complexity. Proteomes 2021 9 3 38 10.3390/proteomes9030038 34564541
    [Google Scholar]
  62. Liu J. Li W. Wang L. Li J. Li E. Luo Y. Multi-omics technology and its applications to life sciences: A review. Sheng Wu Gong Cheng Xue Bao 2022 38 10 3581 3593 10.13345/j.cjb.220724
    [Google Scholar]
  63. Croxatto A. Prod’hom G. Greub G. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol. Rev. 2012 36 2 380 407 10.1111/j.1574‑6976.2011.00298.x 22092265
    [Google Scholar]
  64. Moore J.L. Patterson N.H. Norris J.L. Caprioli R.M. Prospective on imaging mass spectrometry in clinical diagnostics. Mol. Cell. Proteomics 2023 22 9 100576 10.1016/j.mcpro.2023.100576 37209813
    [Google Scholar]
  65. Cox C.R. Harris R.M. Mass spectrometry and microbial diagnostics in the clinical laboratory. Clin. Lab. Med. 2021 41 2 285 295 10.1016/j.cll.2021.03.007 34020764
    [Google Scholar]
  66. Saint-Marcoux F. Sauvage F.L. Marquet P. Current role of LC-MS in therapeutic drug monitoring. Anal. Bioanal. Chem. 2007 388 7 1327 1349 10.1007/s00216‑007‑1320‑1 17520242
    [Google Scholar]
  67. Holbrook J.H. Kemper G.E. Hummon A.B. Quantitative mass spectrometry imaging: Therapeutics & biomolecules. Chem. Commun. (Camb.) 2024 60 16 2137 2151 10.1039/D3CC05988J 38284765
    [Google Scholar]
  68. Liotta E. Gottardo R. Bertaso A. Polettini A. Screening for pharmaco‐toxicologically relevant compounds in biosamples using high‐resolution mass spectrometry: A ‘metabolomic’ approach to the discrimination between isomers. J. Mass Spectrom. 2010 45 3 261 271 10.1002/jms.1710 20014151
    [Google Scholar]
  69. Bui-Thi D. Liu Y. Lippens J.L. Laukens K. De Vijlder T. TransExION: A transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry. J. Cheminform. 2024 16 1 61 10.1186/s13321‑024‑00858‑5 38807166
    [Google Scholar]
  70. Xu A.M. Tang L.C. Jovanovic M. Regev O. Uncovering distinct peptide charging behaviors in electrospray ionization mass spectrometry using a large-scale dataset. J. Am. Soc. Mass Spectrom. 2024 35 1 90 99 10.1021/jasms.3c00325 38095561
    [Google Scholar]
  71. Millington D.S. How mass spectrometry revolutionized newborn screening. J. Mass Spectrom. Adv. Clin. Lab. 2024 32 1 10 10.1016/j.jmsacl.2024.01.006 38333514
    [Google Scholar]
  72. Psychogios N. Hau D.D. Peng J. Guo A.C. Mandal R. Bouatra S. Sinelnikov I. Krishnamurthy R. Eisner R. Gautam B. Young N. Xia J. Knox C. Dong E. Huang P. Hollander Z. Pedersen T.L. Smith S.R. Bamforth F. Greiner R. McManus B. Newman J.W. Goodfriend T. Wishart D.S. The human serum metabolome. PLoS One 2011 6 2 e16957 10.1371/journal.pone.0016957 21359215
    [Google Scholar]
  73. Neagu A.N. Whitham D. Bruno P. Morrissiey H. Darie C.A. Darie C.C. Omics-based investigations of breast cancer. Molecules 2023 28 12 4768 10.3390/molecules28124768 37375323
    [Google Scholar]
  74. Sauer S. Kliem M. Mass spectrometry tools for the classification and identification of bacteria. Nat. Rev. Microbiol. 2010 8 1 74 82 10.1038/nrmicro2243 20010952
    [Google Scholar]
  75. Birhanu A.G. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clin. Proteomics 2023 20 1 32 10.1186/s12014‑023‑09424‑x 37633929
    [Google Scholar]
  76. Picotti P. Aebersold R. Selected reaction monitoring–based proteomics: Workflows, potential, pitfalls and future directions. Nat. Methods 2012 9 6 555 566 10.1038/nmeth.2015 22669653
    [Google Scholar]
  77. Manes N.P. Nita-Lazar A. Application of targeted mass spectrometry in bottom-up proteomics for systems biology research. J. Proteomics 2018 189 75 90 10.1016/j.jprot.2018.02.008 29452276
    [Google Scholar]
  78. Hüttenhain R. Soste M. Selevsek N. Röst H. Sethi A. Carapito C. Farrah T. Deutsch E.W. Kusebauch U. Moritz R.L. Niméus-Malmström E. Rinner O. Aebersold R. Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics. Sci. Transl. Med. 2012 4 142 142ra94 10.1126/scitranslmed.3003989 22786679
    [Google Scholar]
  79. Hüttenhain R. Choi M. Martin de la Fuente L. Oehl K. Chang C.Y. Zimmermann A.K. Malander S. Olsson H. Surinova S. Clough T. Heinzelmann-Schwarz V. Wild P.J. Dinulescu D.M. Niméus E. Vitek O. Aebersold R. A targeted mass spectrometry strategy for developing proteomic biomarkers: A case study of epithelial ovarian cancer. Mol. Cell. Proteomics 2019 18 9 1836 1850 10.1074/mcp.RA118.001221 31289117
    [Google Scholar]
  80. Chang C.Y. Picotti P. Hüttenhain R. Heinzelmann-Schwarz V. Jovanovic M. Aebersold R. Vitek O. Protein significance analysis in selected reaction monitoring (SRM) measurements. Mol. Cell Proteomics 2012 11 4 M111.014662 10.1074/mcp.M111.014662
    [Google Scholar]
  81. Sucha R. Kubickova M. Cervenka J. Hruska-Plochan M. Bohaciakova D. Vodickova Kepkova K. Novakova T. Budkova K. Susor A. Marsala M. Motlik J. Kovarova H. Vodicka P. Targeted mass spectrometry for monitoring of neural differentiation. Biol. Open 2021 10 8 bio058727 10.1242/bio.058727 34357391
    [Google Scholar]
  82. Escher C. Reiter L. MacLean B. Ossola R. Herzog F. Chilton J. MacCoss M.J. Rinner O. Using i RT, a normalized retention time for more targeted measurement of peptides. Proteomics 2012 12 8 1111 1121 10.1002/pmic.201100463 22577012
    [Google Scholar]
  83. Chen Y. Vu J. Thompson M.G. Sharpless W.A. Chan L.J.G. Gin J.W. Keasling J.D. Adams P.D. Petzold C.J. A rapid methods development workflow for high-throughput quantitative proteomic applications. PLoS One 2019 14 2 e0211582 10.1371/journal.pone.0211582 30763335
    [Google Scholar]
  84. Shi T. Fillmore T.L. Sun X. Zhao R. Schepmoes A.A. Hossain M. Xie F. Wu S. Kim J.S. Jones N. Moore R.J. Paša-Tolić L. Kagan J. Rodland K.D. Liu T. Tang K. Camp D.G. II Smith R.D. Qian W.J. Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum. Proc. Natl. Acad. Sci. USA 2012 109 38 15395 15400 10.1073/pnas.1204366109 22949669
    [Google Scholar]
  85. Schutzer S.E. Liu T. Tsai C.F. Petyuk V.A. Schepmoes A.A. Wang Y.T. Weitz K.K. Bergquist J. Smith R.D. Natelson B.H. Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes. Ann. Med. 2023 55 1 2208372 10.1080/07853890.2023.2208372 37722890
    [Google Scholar]
  86. Whiteaker J.R. Zhao L. Lin C. Yan P. Wang P. Paulovich A.G. 2012 Sequential multiplexed analyte quantification using peptide immunoaffinity enrichment coupled to mass spectrometry. Mol. Cell Proteomics 2012 11 6 M111.015347 10.1074/mcp.M111.015347
    [Google Scholar]
  87. Collins C.J. Chang I.J. Jung S. Dayuha R. Whiteaker J.R. Segundo G.R.S. Torgerson T.R. Ochs H.D. Paulovich A.G. Hahn S.H. Rapid multiplexed proteomic screening for primary immunodeficiency disorders from dried blood spots. Front. Immunol. 2018 9 2756 10.3389/fimmu.2018.02756 30564228
    [Google Scholar]
  88. Kuhn E. Whiteaker J.R. Mani D.R. Jackson A.M. Zhao L. Pope M.E. Smith D. Rivera K.D. Anderson N.L. Skates S.J. 2012 Interlaboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in. Mol. Cell Proteomics 2012 11 6 M111.013854 10.1074/mcp.M111.013854
    [Google Scholar]
  89. Whiteaker J.R. Zhao L. Schoenherr R.M. Huang D. Lundeen R.A. Voytovich U. Kennedy J.J. Ivey R.G. Lin C. Murillo O.D. Lorentzen T.D. Colantonio S. Caceres T.W. Roberts R.R. Knotts J.G. Reading J.J. Perry C.D. Richardson C.W. Garcia-Buntley S.S. Bocik W. Hewitt S.M. Chowdhury S. Vandermeer J. Smith S.D. Gopal A.K. Ramchurren N. Fling S.P. Wang P. Paulovich A.G. A multiplexed assay for quantifying immunomodulatory proteins supports correlative studies in immunotherapy clinical trials. Front. Oncol. 2023 13 1168710 10.3389/fonc.2023.1168710 37205196
    [Google Scholar]
  90. Percy A.J. Chambers A.G. Yang J. Domanski D. Borchers C.H. Comparison of standard- and nano-flow liquid chromatography platforms for MRM-based quantitation of putative plasma biomarker proteins. Anal. Bioanal. Chem. 2012 404 4 1089 1101 10.1007/s00216‑012‑6010‑y 22547352
    [Google Scholar]
  91. Zhu S. Wuolikainen A. Wu J. Öhman A. Wingsle G. Moritz T. Andersen P.M. Forsgren L. Trupp M. Targeted multiple reaction monitoring analysis of CSF identifies UCHL1 and GPNMB as candidate biomarkers for ALS. J. Mol. Neurosci. 2019 69 4 643 657 10.1007/s12031‑019‑01411‑y 31721001
    [Google Scholar]
  92. Gillet L.C. Navarro P. Tate S. Röst H. Selevsek N. Reiter L. Bonner R. Aebersold R. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: A new concept for consistent and accurate proteome analysis. Mol. Cell Proteomics 2012 11 6 O111.016717
    [Google Scholar]
  93. Ctortecka C. Mechtler K. The rise of single‐cell proteomics. Anal. Sci. Adv. 2021 2 3-4 84 94 10.1002/ansa.202000152 38716457
    [Google Scholar]
  94. Comber J.D. Karabudak A. Huang X. Piazza P.A. Marques E.T.A. Philip R. Dengue virus specific dual HLA binding T cell epitopes induce CD8 + T cell responses in seropositive individuals. Hum. Vaccin. Immunother. 2014 10 12 3531 3543 10.4161/21645515.2014.980210 25668665
    [Google Scholar]
  95. Samri A. Bandeira A.C. Gois L.L. Silva C.G.R. Rousseau A. Corneau A. Tarantino N. Maucourant C. Queiroz G.A.N. Vieillard V. Yssel H. Campos G.S. Sardi S. Autran B. Rios Grassi M.F. Comprehensive analysis of early T cell responses to acute Zika Virus infection during the first epidemic in Bahia, Brazil. PLoS One 2024 19 5 e0302684 10.1371/journal.pone.0302684 38722858
    [Google Scholar]
  96. Gallien S. Duriez E. Crone C. Kellmann M. Moehring T. Domon B. Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol. Cell. Proteomics 2012 11 12 1709 1723 10.1074/mcp.O112.019802 22962056
    [Google Scholar]
  97. Joshi S.K. Piehowski P. Liu T. Gosline S.J.C. McDermott J.E. Druker B.J. Traer E. Tyner J.W. Agarwal A. Tognon C.E. Rodland K.D. Mass spectrometry-based proteogenomics: New therapeutic opportunities for precision medicine. Annu. Rev. Pharmacol. Toxicol. 2024 64 1 455 479 10.1146/annurev‑pharmtox‑022723‑113921 37738504
    [Google Scholar]
  98. Ohyama K. Kuroda N. Proteomic approaches to profiling the humoral immune response and identifying disease-Associated antigens. Biol. Pharm. Bull. 2012 35 9 1409 1412 10.1248/bpb.b212010 22975488
    [Google Scholar]
  99. Li W. Zhang Q. Li Q. Liu S. Yuan G. Pan Y. Innate immune response restarts adaptive immune response in tumors. Front. Immunol. 2023 14 1260705 10.3389/fimmu.2023.1260705 37781382
    [Google Scholar]
  100. Biswas S. Sharma S. Saroha A. Bhakuni D.S. Malhotra R. Zahur M. Oellerich M. Das H.R. Asif A.R. Identification of novel autoantigen in the synovial fluid of rheumatoid arthritis patients using an immunoproteomics approach. PLoS One 2013 8 2 e56246 10.1371/journal.pone.0056246 23418544
    [Google Scholar]
  101. Ossipova E. Cerqueira C. Reed E. Kharlamova N. Israelsson L. Holmdahl R. Nandakumar K. Engström M. Harre U. Schett G. Catrina A.I. Malmström V. Sommarin Y. Klareskog L. Jakobsson P.J. Lundberg K. Affinity purified anti-citrullinated protein/peptide antibodies target antigens expressed in the rheumatoid joint. Arthritis Res. Ther. 2014 16 4 R167 10.1186/ar4683 25112157
    [Google Scholar]
  102. Webster J. Oxley D. Protein identification by MALDI-TOF mass spectrometry. Methods Mol. Biol. 2012 800 227 240 10.1007/978‑1‑61779‑349‑3_15 21964792
    [Google Scholar]
  103. Sivanesan I. Gopal J. Hasan N. Muthu M. A systematic assessment of matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) application for rapid identification of pathogenic microbes that affect food crops: Delivered and future deliverables. RSC Advances 2023 13 25 17297 17314 10.1039/D3RA01633A 37304772
    [Google Scholar]
  104. Chen Y. Azman S.N. Kerishnan J.P. Zain R.B. Chen Y.N. Wong Y.L. Gopinath S.C.B. Identification of host-immune response protein candidates in the sera of human oral squamous cell carcinoma patients. PLoS One 2014 9 10 e109012 10.1371/journal.pone.0109012 25272005
    [Google Scholar]
  105. Zhang Q. Teow J.Y. Kerishnan J.P. Abd Halim A.A. Chen Y. Clusterin and its isoforms in oral squamous cell carcinoma and their potential as biomarkers: A comprehensive review. Biomedicines 2023 11 5 1458 10.3390/biomedicines11051458 37239129
    [Google Scholar]
  106. Yu Y. Wu G. Zhai Z. Yao H. Lu C. Zhang W. Fifteen novel immunoreactive proteins of Chinese virulent Haemophilus parasuis serotype 5 verified by an immunoproteomic assay. Folia Microbiol. (Praha) 2015 60 1 81 87 10.1007/s12223‑014‑0343‑1 25200063
    [Google Scholar]
  107. Wang X. Xu F. Ning K. Shen L. Qi X. Wang J. Construction and application of MALDI-TOF mass spectrometry for the detection of Haemophilus parasuis. BioMed Res. Int. 2021 2021 1 8 10.1155/2021/5588855 33937398
    [Google Scholar]
  108. Hu D.D. Cui J. Xiao D. Wang L. Liu L.N. Liu R.D. Zhang J.Z. Wang Z.Q. Identification of early diagnostic antigens from Spirometra erinaceieuropaei sparganum soluble proteins using immunoproteomics. Southeast Asian J. Trop. Med. Public Health 2014 45 3 576 583 24974641
    [Google Scholar]
  109. Lu Y. Sun J.H. Lu L.L. Chen J.X. Song P. Ai L. Cai Y.C. Li L.H. Chen S.H. Proteomic and immunological identification of diagnostic antigens from Spirometra erinaceieuropaei plerocercoid. Korean J. Parasitol. 2021 59 6 615 623 10.3347/kjp.2021.59.6.615 34974668
    [Google Scholar]
  110. Hu Q. Ding C. Tu J. Wang X. Han X. Duan Y. Yu S. Immunoproteomics analysis of whole cell bacterial proteins of Riemerella anatipestifer. Vet. Microbiol. 2012 157 3-4 428 438 10.1016/j.vetmic.2012.01.009 22317978
    [Google Scholar]
  111. Yang Z. Wang M. Jia R. Chen S. Liu M. Zhao X. Yang Q. Wu Y. Zhang S. Huang J. Ou X. Mao S. Gao Q. Sun D. Tian B. He Y. Wu Z. Zhu D. Cheng A. Genome-based assessment of antimicrobial resistance reveals the lineage specificity of resistance and resistance gene profiles in Riemerella anatipestifer from China. Microbiol. Spectr. 2024 12 2 e03132-23 10.1128/spectrum.03132‑23 38169285
    [Google Scholar]
  112. Morgenthaler N.G. Kostrzewa M. Rapid identification of pathogens in positive blood culture of patients with sepsis: Review and meta-analysis of the performance of the sepsityper kit. Int. J. Microbiol. 2015 2015 1 10 10.1155/2015/827416 26000017
    [Google Scholar]
  113. Perše G. Samošćanec I. Bošnjak Z. Budimir A. Kuliš T. Mareković I. Sepsityper® kit versus in-house method in rapid identification of bacteria from positive blood cultures by MALDI-TOF mass spectrometry. Life (Basel) 2022 12 11 1744 10.3390/life12111744 36362899
    [Google Scholar]
  114. Wang L. Cui J. Hu D. Liu R. Wang Z. Identification of early diagnostic antigens from major excretory-secretory proteins of Trichinella spiralis muscle larvae using immunoproteomics. Parasit. Vectors 2014 7 1 40 10.1186/1756‑3305‑7‑40 24450759
    [Google Scholar]
  115. Cybulska A. Immunoproteomic analysis of Trichinella britovi proteins recognized by IgG antibodies from meat juice of carnivores naturally infected with T. britovi. Pathogens 2022 11 10 1155 10.3390/pathogens11101155 36297212
    [Google Scholar]
  116. Downard K.M. An immunoproteomics approach to screen the antigenicity of the influenza virus. Methods Mol. Biol. 2013 1061 141 153 10.1007/978‑1‑62703‑589‑7_8 23963935
    [Google Scholar]
  117. Downard K.M. 25 years responding to respiratory and other viruses with mass spectrometry. Mass Spectrom. (Tokyo) 2023 12 1 A0136 10.5702/massspectrometry.A0136 38053835
    [Google Scholar]
  118. Goldszmid R.S. Dzutsev A. Trinchieri G. Host immune response to infection and cancer: Unexpected commonalities. Cell Host Microbe 2014 15 3 295 305 10.1016/j.chom.2014.02.003 24629336
    [Google Scholar]
  119. Yang T. Chen F. Xu F. Wang F. Xu Q. Chen Y. A liquid chromatography–tandem mass spectrometry-based targeted proteomics assay for monitoring P-glycoprotein levels in human breast tissue. Clin. Chim. Acta 2014 436 283 289 10.1016/j.cca.2014.06.013 24972002
    [Google Scholar]
  120. Agostini M. Traldi P. Hamdan M. Mass spectrometry-based proteomics: Analyses related to drug-resistance and disease biomarkers. Medicina (Kaunas) 2023 59 10 1722 10.3390/medicina59101722 37893440
    [Google Scholar]
  121. Bergamini S. Bellei E. Reggiani Bonetti L. Monari E. Cuoghi A. Borelli F. Sighinolfi M. Bianchi G. Ozben T. Tomasi A. Inflammation: An important parameter in the search of prostate cancer biomarkers. Proteome Sci. 2014 12 1 32 10.1186/1477‑5956‑12‑32 24944525
    [Google Scholar]
  122. Bellei E. Caramaschi S. Giannico G.A. Monari E. Martorana E. Reggiani Bonetti L. Bergamini S. Research of prostate cancer urinary diagnostic biomarkers by proteomics: The noteworthy influence of inflammation. Diagnostics (Basel) 2023 13 7 1318 10.3390/diagnostics13071318 37046536
    [Google Scholar]
  123. Gomase V. Tagore S. Kale K. Bhiwgade D. Oncogenomics. Curr. Drug Metab. 2008 9 3 199 206 10.2174/138920008783884713 18336222
    [Google Scholar]
  124. Ledford H. Trove of tumour genomes offers clues to cancer origins. Nature 2022 604 7907 609 10.1038/d41586‑022‑01095‑2 35449305
    [Google Scholar]
  125. Millon L. Reboux G. Barrera C. Rognon B. Roussel S. Monod M. Immunoproteomics for serological diagnosis of hypersensitivity pneumonitis caused by environmental microorganisms. Curr. Protein Pept. Sci. 2014 15 5 430 436 10.2174/1389203715666140512121733 24818758
    [Google Scholar]
  126. Edfors F. Boström T. Forsström B. Zeiler M. Johansson H. Lundberg E. Hober S. Lehtiö J. Mann M. Uhlen M. Immunoproteomics using polyclonal antibodies and stable isotope-labeled affinity-purified recombinant proteins. Mol. Cell. Proteomics 2014 13 6 1611 1624 10.1074/mcp.M113.034140 24722731
    [Google Scholar]
  127. Geyer P.E. Holdt L.M. Teupser D. Mann M. Revisiting biomarker discovery by plasma proteomics. Mol. Syst. Biol. 2017 13 9 942 10.15252/msb.20156297 28951502
    [Google Scholar]
  128. Florentinus-Mefailoski A. Safi F. Marshall J.G. Enzyme linked immuno mass spectrometric assay (ELIMSA). J. Proteomics 2014 96 343 352 10.1016/j.jprot.2013.11.022 24316356
    [Google Scholar]
  129. Schmidt S. Hoffmann H. Garbe L.A. Harrer A. Steiner M. Himly M. Schneider R.J. Re-assessment of monoclonal antibodies against diclofenac for their application in the analysis of environmental waters. Anal. Methods 2024 16 21 3349 3363 10.1039/D3AY01333B 38742423
    [Google Scholar]
  130. Gomase V. Changbhale S. Patil S. Kale K. Metabolomics. Curr. Drug Metab. 2008 9 1 89 98 10.2174/138920008783331149 18220576
    [Google Scholar]
  131. Martins C. Magalhães S. Almeida I. Neto V. Rebelo S. Nunes A. Metabolomics to study human aging: A review. Curr. Mol. Med. 2024 24 4 457 477 10.2174/1566524023666230407123727 37026499
    [Google Scholar]
  132. Zhang Y. Wu Y. Liu H. Gong W. Hu Y. Shen Y. Cao J. Granulocytic myeloid-derived suppressor cells inhibit T follicular helper cells during experimental Schistosoma japonicum infection. Parasit. Vectors 2021 14 1 497 10.1186/s13071‑021‑05006‑8 34565440
    [Google Scholar]
  133. Doubleday P. Ballif B. Developmentally-dynamic murine brain proteomes and phosphoproteomes revealed by quantitative proteomics. Proteomes 2014 2 2 191 207 10.3390/proteomes2020191 25177544
    [Google Scholar]
  134. Liu X. Fields R. Schweppe D.K. Paulo J.A. Strategies for mass spectrometry-based phosphoproteomics using isobaric tagging. Expert Rev. Proteomics 2021 18 9 795 807 10.1080/14789450.2021.1994390 34652972
    [Google Scholar]
  135. Milioli H.H. Santos Sousa K. Kaviski R. Dos Santos Oliveira N.C. De Andrade Urban C. De Lima R.S. Cavalli I.J. De Souza Fonseca Ribeiro E.M. Comparative proteomics of primary breast carcinomas and lymph node metastases outlining markers of tumor invasion. Cancer Genomics Proteomics 2015 12 2 89 101 25770193
    [Google Scholar]
  136. Neagu A.N. Whitham D. Seymour L. Haaker N. Pelkey I. Darie C.C. Proteomics-based identification of dysregulated proteins and biomarker discovery in invasive ductal carcinoma, the most common breast cancer subtype. Proteomes 2023 11 2 13 10.3390/proteomes11020013 37092454
    [Google Scholar]
  137. Cristofaro M.G. Scumaci D. Fiumara C.V. Di Sanzo M. Zuccalà V. Donato G. Caruso D. Riccelli U. Faniello M.C. Cuda G. Costanzo F. Giudice M. Identification of prognosis-related proteins in gingival squamous cell carcinoma by twodimensional gel electrophoresis and mass spectrometry-based proteomics. Ann. Ital. Chir. 2014 85 6 518 524 25712919
    [Google Scholar]
  138. Tsolakos N. Brookes C. Taylor S. Gorringe A. Tang C.M. Feavers I.M. Wheeler J.X. Identification of vaccine antigens using integrated proteomic analyses of surface immunogens from serogroup B Neisseria meningitidis. J. Proteomics 2014 101 63 76 10.1016/j.jprot.2014.02.013 24561796
    [Google Scholar]
  139. Andreae C.A. Sessions R.B. Virji M. Hill D.J. Bioinformatic analysis of meningococcal Msf and Opc to inform vaccine antigen design. PLoS One 2018 13 3 e0193940 10.1371/journal.pone.0193940 29547646
    [Google Scholar]
  140. Moreau Y. Tranchevent L.C. Computational tools for prioritizing candidate genes: Boosting disease gene discovery. Nat. Rev. Genet. 2012 13 8 523 536 10.1038/nrg3253 22751426
    [Google Scholar]
  141. Choi Y. Cha J. Choi S. Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES). BMC Bioinformatics 2024 25 1 56 10.1186/s12859‑024‑05677‑x 38308205
    [Google Scholar]
  142. Patronov A. Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol. 2013 3 1 120139 10.1098/rsob.120139 23303307
    [Google Scholar]
  143. Anwar T. Ismail S. Parvaiz F. Abbasi S.W. A Al-Abbasi F. M Alghamdi A. Al-Regaiey K. Ul-Haq A. Kaleem I. Bashir S. Waheed Y. Computational design of experimentally validated multi-epitopes vaccine against hepatitis E virus: An immunological approach. PLoS One 2023 18 12 e0294663 10.1371/journal.pone.0294663 38096182
    [Google Scholar]
  144. Ulmer J.B. Mansoura M.K. Geall A.J. Vaccines ‘on demand’: Science fiction or a future reality. Expert Opin. Drug Discov. 2015 10 2 101 106 10.1517/17460441.2015.996128 25582273
    [Google Scholar]
  145. Ankrah P.K. Ilesanmi A. Akinyemi A.O. Lasehinde V. Adurosakin O.E. Ajayi O.H. Clinical analysis and applications of mRNA vaccines in infectious diseases and cancer treatment. Cureus 2023 15 10 e46354 10.7759/cureus.46354 37920621
    [Google Scholar]
  146. Gomase V.S. Kemkar K.R. Baviskar B.A. Mundhe V.S. Sakhare A.D. Kolsure A.K. Bhimanwar A.A. Dhamane S.P. Potnis V.V. Physicochemical and immunoproteomic analysis to design synthetic peptide vaccine from Naja kaouthia neurotoxin. World J. Pharm. Res. 2023 12 4 1286 1291 10.20959/wjpr20234‑46863
    [Google Scholar]
  147. Guarra F. Colombo G. Computational methods in immunology and vaccinology: Design and development of antibodies and immunogens. J. Chem. Theory Comput. 2023 19 16 5315 5333 10.1021/acs.jctc.3c00513 37527403
    [Google Scholar]
  148. Wang J. Yu Y. Zhao Y. Zhang D. Li J. Evaluation and integration of existing methods for computational prediction of allergens. BMC Bioinformatics 2013 14 S4 Suppl. 4 S1 10.1186/1471‑2105‑14‑S4‑S1 23514097
    [Google Scholar]
  149. Li J. Wang J. Li J. Improving allergen prediction in main crops using a weighted integrative method. Interdiscip. Sci. 2017 9 4 545 549 10.1007/s12539‑016‑0192‑5 27734271
    [Google Scholar]
  150. He Y. Xiang Z. Mobley H.L.T. Vaxign: The first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J. Biomed. Biotechnol. 2010 2010 1 15 10.1155/2010/297505 20671958
    [Google Scholar]
  151. Yu Y. Zu L. Jiang J. Wu Y. Wang Y. Xu M. Liu Q. Structure-aware deep model for MHC-II peptide binding affinity prediction. BMC Genomics 2024 25 1 127 10.1186/s12864‑023‑09900‑6 38291350
    [Google Scholar]
  152. Xiang Z. He Y. Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology. BMC Bioinformatics 2013 14 S4 Suppl. 4 S2 10.1186/1471‑2105‑14‑S4‑S2 23514126
    [Google Scholar]
  153. Mayer R.L. Mechtler K. Immunopeptidomics in the era of single-cell proteomics. Biology (Basel) 2023 12 12 1514 10.3390/biology12121514 38132340
    [Google Scholar]
  154. ElAbd H. Franke A. Mass spectrometry-based immunopeptidomics of peptides presented on human leukocyte antigen proteins. Peptidomics Humana New York Schrader M. Fricker L.D. 2024 2758 425 443 10.1007/978‑1‑0716‑3646‑6_23
    [Google Scholar]
  155. Mayer R.L. Verbeke R. Asselman C. Aernout I. Gul A. Eggermont D. Boucher K. Thery F. Maia T.M. Demol H. Gabriels R. Martens L. Bécavin C. De Smedt S.C. Vandekerckhove B. Lentacker I. Impens F. Immunopeptidomics-based design of mRNA vaccine formulations against Listeria monocytogenes. Nat. Commun. 2022 13 1 6075 10.1038/s41467‑022‑33721‑y 36241641
    [Google Scholar]
  156. Sun J. Wu D. Xu T. Wang X. Xu X. Tao L. Li Y.X. Cao Z.W. 2009 SEPPA: A computational server for spatial epitope prediction of protein antigens. Nucleic Acids Res. 2009 37 W612 W616 10.1093/nar/gkp417
    [Google Scholar]
  157. Zeng X. Bai G. Sun C. Ma B. Recent progress in antibody epitope prediction. Antibodies (Basel) 2023 12 3 52 10.3390/antib12030052 37606436
    [Google Scholar]
  158. Lo Y.T. Pai T.W. Wu W.K. Chang H.T. Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics. BMC Bioinformatics 2013 14 S4 Suppl. 4 S3 10.1186/1471‑2105‑14‑S4‑S3 23514199
    [Google Scholar]
  159. Lu S. Li Y. Ma Q. Nan X. Zhang S. A structure-based B-cell epitope prediction model through combining local and global features. Front. Immunol. 2022 13 890943 10.3389/fimmu.2022.890943 35844532
    [Google Scholar]
  160. Xu Y. Luo C. Mamitsuka H. Zhu S. MetaMHCpan, a meta approach for pan-specific MHC peptide binding prediction. Vaccine Design Humana New York Thomas S. 2016 1404 753 760 10.1007/978‑1‑4939‑3389‑1_49
    [Google Scholar]
  161. Terry F. Ardito M. Spero D. Martin W. De Groot D.S. iVAX web-based vaccine design. The 2nd ISV pre-conference computational vaccinology workshop (ICoVax 2012) Shanghai, China 2012
    [Google Scholar]
  162. Huang J. Ru B. SAROTUP 2.0: A suite of web tools for finding potential target-unrelated peptides from phage display data. The 2nd ISV Pre-Conference Computational Vaccinology Workshop (ICoVax 2012) Shanghai, China October 13, 2012 2012
    [Google Scholar]
  163. He Y. Cao Z. De Groot A.S. Brusic V. Schönbach C. Petrovsky N. Computational vaccinology and the ICoVax 2012 workshop. BMC Bioinformatics 2013 14 S4 I1 10.1186/1471‑2105‑14‑S4‑I1 23514034
    [Google Scholar]
  164. Chen L. Wu D. Ji L. Wu X. Xu D. Cao Z. Han J. Bioinformatics analysis of the epitope regions for norovirus capsid protein. BMC Bioinformatics 2013 14 S4 S5 10.1186/1471‑2105‑14‑S4‑S5 23514273
    [Google Scholar]
  165. Gupta A. Singh A.P. Singh V.K. Sinha R.P. Recent developments and future perspectives of vaccines and therapeutic agents against SARS-CoV-2 using the BCOV_S1_CTD of the S protein. Viruses 2023 15 6 1234 10.3390/v15061234 37376534
    [Google Scholar]
  166. Mattei A.E. Gutierrez A.H. Seshadri S. Tivin J. Ardito M. Rosenberg A.S. Martin W.D. De Groot A.S. In silico methods for immunogenicity risk assessment and human homology screening for therapeutic antibodies. MAbs 2024 16 1 2333729 10.1080/19420862.2024.2333729 38536724
    [Google Scholar]
  167. De Groot A.S. Desai A.K. Lelias S. Miah S.M.S. Terry F.E. Khan S. Li C. Yi J.S. Ardito M. Martin W.D. Kishnani P.S. Immune tolerance-adjusted personalized immunogenicity prediction for Pompe disease. Front. Immunol. 2021 12 636731 10.3389/fimmu.2021.636731 34220802
    [Google Scholar]
  168. Mishra S. Gomase V.S. Application of in silico approach in prediction of epitopes. Res. J. Biotechnol. 2021 16 2 206 214
    [Google Scholar]
  169. Gustiananda M. Sasmono T. Nunez A.G. Yohan B. Moise L. Wardhani P. Terry F. Martin W. De Groot A.S. Analysis of ChimeriVax dengue virus envelope for T-cell epitopes and comparison to circulating viral strains in Indonesia. The 2nd ISV Pre-Conference Computational Vaccinology Workshop (ICoVax 2012) Shanghai, China October 13, 2012 2012
    [Google Scholar]
  170. Imani Fooladi A.A. Mahmoodzadeh Hosseini H. Amani J. An in silico chimeric vaccine targeting breast cancer containing inherent adjuvant. Iran. J. Cancer Prev. 2015 8 3 e2326 10.17795/ijcp2326 26413246
    [Google Scholar]
  171. Sliwkowski M.X. Mellman I. Antibody therapeutics in cancer. Science 2013 341 6151 1192 1198 10.1126/science.1241145 24031011
    [Google Scholar]
  172. Wei B. Lantz C. Ogorzalek Loo R.R. Campuzano I.D.G. Loo J.A. Internal fragments enhance middle-down mass spectrometry structural characterization of monoclonal antibodies and antibody-drug conjugates. Anal. Chem. 2024 96 6 2491 2499 10.1021/acs.analchem.3c04526 38294207
    [Google Scholar]
  173. Nakaya H.I. Li S. Pulendran B. Systems vaccinology: Learning to compute the behavior of vaccine induced immunity. Wiley Interdiscip. Rev. Syst. Biol. Med. 2012 4 2 193 205 10.1002/wsbm.163 22012654
    [Google Scholar]
  174. Gomase V.S. Chitlange N.R. Sensitive quantitative predictions of MHC binding peptides and fragment-based peptide vaccines from Taenia crassiceps. J. Vaccines Vaccin. 2012 3 1 131 10.4172/2157‑7560.1000131
    [Google Scholar]
  175. Gomase V. Chitlange N. Changbhale S. Kale K. Prediction of Brugia malayi antigenic peptides: Candidates for synthetic vaccine design against lymphatic filariasis. Protein Pept. Lett. 2013 20 8 864 887 10.2174/0929866511320080004 23537185
    [Google Scholar]
  176. Dormitzer P.R. Grandi G. Rappuoli R. Structural vaccinology starts to deliver. Nat. Rev. Microbiol. 2012 10 12 807 813 10.1038/nrmicro2893 23154260
    [Google Scholar]
  177. Gomase V.S. Kemkar K.R. Baviskar B.A. Mundhe V.S. Sakhare A.D. Kolsure A.K. Bhimanwar A.A. Dhamane S.P. Potnis V.V. Immunoinformatics study of physical properties of scorpion neurotoxin Bmk-M8 from Mesobuthus martensii. Int. J. Modern Pharm. Res. 2023 7 3 13 15 10.46376/ijmpr/7.3.4
    [Google Scholar]
  178. Gomase V.S. Pangarkar P.R. Kemkar K.R. Albhar K.G. Kolsure A.K. Dhamane S.P. Potnis V.V. Immunoproteomics physicochemical analysis of heterodimeric neurotoxic phospholipases A2 from Apis cerana. Int. J. Pharm. Res. 2023 15 1 118 123 10.31838/ijpr/15.1.019
    [Google Scholar]
  179. Mishra S. Gomase V.S. In silico insights to predict the major histocompatibility complex peptide binders from protein. Eur. J. Mol. Clin. Med. 2021 8 2 219 226
    [Google Scholar]
  180. Gomase V. Tagore S. RNAi - A tool for target finding in new drug development. Curr. Drug Metab. 2008 9 3 241 244 10.2174/138920008783884777 18336228
    [Google Scholar]
  181. Gomase V. Changbhale S. Antigenicity prediction in melittin: Possibilities of in drug development from Apis dorsata. Curr. Proteomics 2007 4 2 107 114 10.2174/157016407782194639
    [Google Scholar]
  182. Gomase V. Prediction of antigenic epitopes of neurotoxin Bmbktx1 from Mesobuthus martensii. Curr. Drug Discov. Technol. 2006 3 3 225 229 10.2174/157016306780136817 17311567
    [Google Scholar]
  183. Zhou Y. Murre C. Bursty gene expression and mRNA decay pathways orchestrate B cell activation. Sci. Adv. 2021 7 49 eabm0819 10.1126/sciadv.abm0819 34860551
    [Google Scholar]
  184. Bahrami A.A. Payandeh Z. Khalili S. Zakeri A. Bandehpour M. Immunoinformatics: In silico approaches and computational design of a multi-epitope, immunogenic protein. Int. Rev. Immunol. 2019 38 6 307 322 10.1080/08830185.2019.1657426 31478759
    [Google Scholar]
  185. Taghizadeh M.S. Niazi A. Afsharifar A. Virus-like particles (VLPs): A promising platform for combating against Newcastle disease virus. Vaccine X 2024 16 100440 10.1016/j.jvacx.2024.100440 38283623
    [Google Scholar]
  186. Wu X. Zhai X. Lai Y. Zuo L. Zhang Y. Mei X. Xiang R. Kang Z. Zhou L. Wang H. Construction and immunogenicity of novel chimeric virus-like particles bearing antigens of infectious bronchitis virus and Newcastle disease virus. Viruses 2019 11 3 254 10.3390/v11030254 30871190
    [Google Scholar]
  187. Sepotokele K.M. O’Kennedy M.M. Wandrag D.B.R. Abolnik C. Optimization of infectious bronchitis virus-like particle expression in Nicotiana benthamiana as potential poultry vaccines. PLoS One 2023 18 7 e0288970 10.1371/journal.pone.0288970 37471377
    [Google Scholar]
  188. Hadj Hassine I. Ben M’hadheb M. Almalki M.A. Gharbi J. Virus‐like particles as powerful vaccination strategy against human viruses. Rev. Med. Virol. 2024 34 1 e2498 10.1002/rmv.2498 38116958
    [Google Scholar]
  189. Kheirvari M. Liu H. Tumban E. Virus-like particle vaccines and platforms for vaccine development. Viruses 2023 15 5 1109 10.3390/v15051109 37243195
    [Google Scholar]
/content/journals/ppl/10.2174/0109298665342261240912105111
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Immunoproteomics: Approach to Diagnostic and Vaccine Development
Bentham Science Publishers ; https://doi.org/10.2174/0109298665342261240912105111
/content/journals/ppl/10.2174/0109298665342261240912105111
/content/journals/ppl/10.2174/0109298665342261240912105111
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  • Article Type:     Review Article
Keywords: MHC ; proteome ; bioinformatics ; Immunoproteomics ; pathogen ; vaccine ; epitopes

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