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Volume 31, Issue 11
  • ISSN: 0929-8665
  • E-ISSN: 1875-5305

Abstract

Immunoproteomics is the branch of proteomics with an emphasis on the study of functional peptides and proteins related to the immune system. Combining proteomics techniques with immunological research aims to uncover the complex interactions of proteins involved in immune responses. This review discusses the methods, applications, and recent advancements in immunoproteomics, highlighting its critical role in understanding immune responses, discovering biomarkers, and developing vaccines and therapeutics. This study offers a comprehensive exploration of the methodologies, applications, and advancements within immunoproteomics. Techniques such as mass spectrometry, antibody-based assays, and computational analysis are pivotal in unraveling the complexities of the immune system at the protein level. Immunoproteomics finds diverse applications in biomarker discovery, vaccine development, autoimmune disease research, infectious disease diagnostics, and cancer immunotherapy. Challenges, including data integration, sample heterogeneity, and biomarker validation, persist, necessitating innovative approaches and interdisciplinary collaborations. In the future, immunoproteomics will likely play a major role in expanding our knowledge of immune-related diseases and accelerating the creation of targeted and precise immunotherapies.

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2025-01-31

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References

  1. GomaseV. ChitlangeN. ChangbhaleS. KaleK. Prediction of Brugia malayi antigenic peptides: Candidates for synthetic vaccine design against lymphatic filariasis.Protein Pept. Lett.201320886488710.2174/092986651132008000423537185
     [Google Scholar]
  2. GomaseV. ChangbhaleS. Antigenicity prediction in melittin: Possibilities of in drug development from Apis dorsata.Curr. Proteomics20074210711410.2174/157016407782194639
     [Google Scholar]
  3. GomaseV. KaleK. TagoreS. HattureS. Proteomics: technologies for protein analysis.Curr. Drug Metab.20089321322010.2174/13892000878388474018336224
     [Google Scholar]
  4. GomaseV. KemkarK. PotnisV. Intellectual property rights: Protection of biotechnological inventions in India.Recent Pat. Biotechnol.202418212814310.2174/187220831766623061214560038282443
     [Google Scholar]
  5. GomaseV. TagoreS. Cytomics.Curr. Drug Metab.20089326326610.2174/13892000878388473118336233
     [Google Scholar]
  6. GomaseV. TagoreS. Kinomics.Curr. Drug Metab.20089325525810.2174/13892000878388480318336231
     [Google Scholar]
  7. FredoliniC. ByströmS. PinE. EdforsF. TamburroD. IglesiasM.J. HäggmarkA. HongM.G. UhlenM. NilssonP. SchwenkJ.M. Immunocapture strategies in translational proteomics.Expert Rev. Proteomics2016131839810.1586/14789450.2016.111114126558424
     [Google Scholar]
  8. BeckerJ.O. HoofnagleA.N. Replacing immunoassays with tryptic digestion-peptide immunoaffinity enrichment and LC-MS/MS.Bioanalysis20124328129010.4155/bio.11.31922303832
     [Google Scholar]
  9. KumarV. BarnidgeD.R. ChenL.S. TwentymanJ.M. CradicK.W. GrebeS.K. SinghR.J. Quantification of serum 1-84 parathyroid hormone in patients with hyperparathyroidism by immunocapture in situ digestion liquid chromatography-tandem mass spectrometry.Clin. Chem.201056230631310.1373/clinchem.2009.13464320007860
     [Google Scholar]
  10. MelbyJ.A. RobertsD.S. LarsonE.J. BrownK.A. BayneE.F. JinS. GeY. Novel strategies to address the challenges in top-down proteomics.J. Am. Soc. Mass Spectrom.20213261278129410.1021/jasms.1c0009933983025
     [Google Scholar]
  11. ArtiguesA. NadeauO.W. RimmerM.A. VillarM.T. DuX. FentonA.W. CarlsonG.M. Protein structural analysis via mass spectrometry-based proteomics.Adv. Exp. Med. Biol.201691939743110.1007/978‑3‑319‑41448‑5_1927975228
     [Google Scholar]
  12. Darie-IonL. WhithamD. JayathirthaM. RaiY. NeaguA.N. DarieC.C. PetreB.A. Applications of MALDI-MS/MS-based proteomics in biomedical research.Molecules20222719619610.3390/molecules2719619636234736
     [Google Scholar]
  13. IgnjatovicV. GeyerP.E. PalaniappanK.K. ChaabanJ.E. OmennG.S. BakerM.S. DeutschE.W. SchwenkJ.M. Mass spectrometry-based plasma proteomics: Considerations from sample collection to achieving translational data.J. Proteome Res.201918124085409710.1021/acs.jproteome.9b0050331573204
     [Google Scholar]
  14. ZhangY. BottinelliD. LisacekF. LubanJ. Strambio-De-CastilliaC. VaresioE. HopfgartnerG. Optimization of human dendritic cell sample preparation for mass spectrometry-based proteomic studies.Anal. Biochem.2015484405010.1016/j.ab.2015.05.00725983236
     [Google Scholar]
  15. DonnellA.M. LewisS. AbrahamS. SubramanianK. FigueroaJ.L. DeepeG.S.Jr VonderheideA.P. Investigation of an optimal cell lysis method for the study of the zinc metalloproteome of Histoplasma capsulatum.Anal. Bioanal. Chem.2017409266163617210.1007/s00216‑017‑0556‑728801743
     [Google Scholar]
  16. WadaO.Z. RashidN. WijtenP. ThornalleyP. MckayG. MackeyH.R. Evaluation of cell disruption methods for protein and coenzyme Q10 quantification in purple non-sulfur bacteria.Front. Microbiol.202415132409910.3389/fmicb.2024.132409938550862
     [Google Scholar]
  17. JaneckiD.J. ReillyJ.P. Denaturation of metalloproteins with EDTA to facilitate enzymatic digestion and mass fingerprinting.Rapid Commun. Mass Spectrom.200519101268127210.1002/rcm.192415834845
     [Google Scholar]
  18. SierraL.S. DixonC.K. WilkenL.R. Enzymatic cell disruption of the microalgae Chlamydomonas reinhardtii for lipid and protein extraction.Algal Res.20172514915910.1016/j.algal.2017.04.004
     [Google Scholar]
  19. YanY. ZhangY. GaoJ. QinL. LiuF. ZengW. WanJ. Intracellular and extracellular sources, transformation process and resource recovery value of proteins extracted from wastewater treatment sludge via alkaline thermal hydrolysis and enzymatic hydrolysis.Sci. Total Environ.202285215851210.1016/j.scitotenv.2022.15851236063951
     [Google Scholar]
  20. TakemoriA. ButcherD.S. HarmanV.M. BrownridgeP. ShimaK. HigoD. IshizakiJ. HasegawaH. SuzukiJ. YamashitaM. LooJ.A. LooR.R.O. BeynonR.J. AndersonL.C. TakemoriN. PEPPI-MS: Polyacrylamide-gel-based prefractionation for analysis of intact proteoforms and protein complexes by mass spectrometry.J. Proteome Res.20201993779379110.1021/acs.jproteome.0c0030332538093
     [Google Scholar]
  21. DemmersL.C. HeckA.J.R. WuW. Pre-fractionation extends but also creates a bias in the detectable HLA class Ι ligandome.J. Proteome Res.20191841634164310.1021/acs.jproteome.8b0082130784271
     [Google Scholar]
  22. ChristopherJ.A. StadlerC. MartinC.E. MorgensternM. PanY. BetsingerC.N. RattrayD.G. MahdessianD. GingrasA.C. WarscheidB. LehtiöJ. CristeaI.M. FosterL.J. EmiliA. LilleyK.S. Subcellular proteomics.Nat. Rev. Methods Primers2021113210.1038/s43586‑021‑00029‑y34549195
     [Google Scholar]
  23. WiederholdE. VeenhoffL.M. PoolmanB. SlotboomD.J. Proteomics of Saccharomyces cerevisiae organelles.Mol. Cell. Proteomics20109343144510.1074/mcp.R900002‑MCP20019955081
     [Google Scholar]
  24. QingR. HaoS. SmorodinaE. JinD. ZalevskyA. ZhangS. Protein design: From the aspect of water solubility and stability.Chem. Rev.202212218140851417910.1021/acs.chemrev.1c0075735921495
     [Google Scholar]
  25. RabaniV. DavaniS. Gambert-NicotS. MeneveauN. MontangeD. Comparative lipidomics and proteomics analysis of platelet lipid rafts using different detergents.Platelets201627763464110.3109/09537104.2016.117420327184886
     [Google Scholar]
  26. ShogomoriH. BrownD.A. Use of detergents to study membrane rafts: the good, the bad, and the ugly.Biol. Chem.200338491259126310.1515/BC.2003.13914515986
     [Google Scholar]
  27. GutsteinH.B. MorrisJ.S. Laser capture sampling and analytical issues in proteomics.Expert Rev. Proteomics20074562763710.1586/14789450.4.5.62717941818
     [Google Scholar]
  28. TerkelsenT. PernemalmM. GromovP. Børresen-DaleA.L. KroghA. HaakensenV.D. LethiöJ. PapaleoE. GromovaI. High-throughput proteomics of breast cancer interstitial fluid: Identification of tumor subtype-specific serologically relevant biomarkers.Mol. Oncol.202115242946110.1002/1878‑0261.1285033176066
     [Google Scholar]
  29. LinT.T. ZhangT. KitataR.B. LiuT. SmithR.D. QianW.J. ShiT. Mass spectrometry-based targeted proteomics for analysis of protein mutations.Mass Spectrom. Rev.202342279682110.1002/mas.2174134719806
     [Google Scholar]
  30. ItkonenH.M. UrbanucciA. MartinS.E.S. KhanA. MathelierA. ThiedeB. WalkerS. MillsI.G. High OGT activity is essential for MYC-driven proliferation of prostate cancer cells.Theranostics2019982183219710.7150/thno.3083431149037
     [Google Scholar]
  31. MacKinnonA.L. GarrisonJ.L. HegdeR.S. TauntonJ. Photo-leucine incorporation reveals the target of a cyclodepsipeptide inhibitor of cotranslational translocation.J. Am. Chem. Soc.200712947145601456110.1021/ja076250y17983236
     [Google Scholar]
  32. AgrawalP. YuK. SalomonA.R. SedivyJ.M. Proteomic profiling of Myc-associated proteins.Cell Cycle20109244908492110.4161/cc.9.24.1419921150319
     [Google Scholar]
  33. ThomasS. HaoL. RickeW.A. LiL. Biomarker discovery in mass spectrometry-based urinary proteomics.Proteomics Clin. Appl.201610435837010.1002/prca.20150010226703953
     [Google Scholar]
  34. LanucaraF. HolmanS.W. GrayC.J. EyersC.E. The power of ion mobility-mass spectrometry for structural characterization and the study of conformational dynamics.Nat. Chem.20146428129410.1038/nchem.188924651194
     [Google Scholar]
  35. AchourB. Al FeteisiH. LanucaraF. Rostami-HodjeganA. BarberJ. Global proteomic analysis of human liver microsomes: Rapid characterization and quantification of hepatic drug-metabolizing enzymes.Drug Metab. Dispos.201745666667510.1124/dmd.116.07473228373266
     [Google Scholar]
  36. PrasadB. AchourB. ArturssonP. HopC.E.C.A. LaiY. SmithP.C. BarberJ. WisniewskiJ.R. SpellmanD. UchidaY. ZientekM.A. UnadkatJ.D. Rostami-HodjeganA. Toward a consensus on applying quantitative liquid chromatography-tandem mass spectrometry proteomics in translational pharmacology research: A white paper.Clin. Pharmacol. Ther.2019106352554310.1002/cpt.153731175671
     [Google Scholar]
  37. DowlingP. ZweyerM. SwandullaD. OhlendieckK. Characterization of contractile proteins from skeletal muscle using gel-based top-down proteomics.Proteomes2019722510.3390/proteomes702002531226838
     [Google Scholar]
  38. GuoT. WangX. LiM. YangH. LiL. PengF. ZhanX. Identification of glioblastoma phosphotyrosine-containing proteins with two-dimensional western blotting and tandem mass spectrometry.BioMed Res. Int.2015201512110.1155/2015/13405026090378
     [Google Scholar]
  39. WeissN.G. JarvisJ.W. NelsonR.W. HayesM.A. Examining serum amyloid P component microheterogeneity using capillary isoelectric focusing and MALDI-MS.Proteomics201111110611310.1002/pmic.20100031021182198
     [Google Scholar]
  40. BabačićH. LehtiöJ. Pico de CoañaY. PernemalmM. ErikssonH. In-depth plasma proteomics reveals increase in circulating PD-1 during anti-PD-1 immunotherapy in patients with metastatic cutaneous melanoma.J. Immunother. Cancer202081e00020410.1136/jitc‑2019‑00020432457125
     [Google Scholar]
  41. HamzaG.M. BergoV.B. MamaevS. WojchowskiD.M. ToranP. WorsfoldC.R. CastaldiM.P. SilvaJ.C. Affinity-bead assisted mass spectrometry (Affi-BAMS): A multiplexed microarray platform for targeted proteomics.Int. J. Mol. Sci.2020216201610.3390/ijms2106201632188029
     [Google Scholar]
  42. OngS.E. KratchmarovaI. MannM. Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC).J. Proteome Res.20032217318110.1021/pr025570812716131
     [Google Scholar]
  43. MannM. Functional and quantitative proteomics using SILAC.Nat. Rev. Mol. Cell Biol.200671295295810.1038/nrm206717139335
     [Google Scholar]
  44. OngS.E. MannM. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC).Nat. Protoc.2006162650266010.1038/nprot.2006.42717406521
     [Google Scholar]
  45. ChenX. WeiS. JiY. GuoX. YangF. Quantitative proteomics using SILAC: Principles, applications, and developments.Proteomics201515183175319210.1002/pmic.20150010826097186
     [Google Scholar]
  46. LauH.T. SuhH.W. GolkowskiM. OngS.E. Comparing SILAC- and stable isotope dimethyl-labeling approaches for quantitative proteomics.J. Proteome Res.20141394164417410.1021/pr500630a25077673
     [Google Scholar]
  47. GouwJ.W. KrijgsveldJ. HeckA.J.R. Quantitative proteomics by metabolic labeling of model organisms.Mol. Cell. Proteomics201091112410.1074/mcp.R900001‑MCP20019955089
     [Google Scholar]
  48. RossP.L. HuangY.N. MarcheseJ.N. WilliamsonB. ParkerK. HattanS. KhainovskiN. PillaiS. DeyS. DanielsS. PurkayasthaS. JuhaszP. MartinS. Bartlet-JonesM. HeF. JacobsonA. PappinD.J. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.Mol. Cell. Proteomics20043121154116910.1074/mcp.M400129‑MCP20015385600
     [Google Scholar]
  49. TonackS. Aspinall-O`DeaM. JenkinsR.E. ElliotV. MurrayS. LaneC.S. KitteringhamN.R. NeoptolemosJ.P. CostelloE. A technically detailed and pragmatic protocol for quantitative serum proteomics using iTRAQ.J. Proteomics200973235235610.1016/j.jprot.2009.07.00919651253
     [Google Scholar]
  50. ThompsonA. SchäferJ. KuhnK. KienleS. SchwarzJ. SchmidtG. NeumannT. HamonC. MohammedA.K. HamonC. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS.Anal. Chem.20037581895190410.1021/ac026256012713048
     [Google Scholar]
  51. SinclairJ. TimmsJ.F. Quantitative profiling of serum samples using TMT protein labelling, fractionation and LC–MS/MS.Methods201154436136910.1016/j.ymeth.2011.03.00421397697
     [Google Scholar]
  52. HwangW. LeiW. KatritsisN.M. MacMahonM. ChapmanK. HanN. Current and prospective computational approaches and challenges for developing COVID-19 vaccines.Adv. Drug Deliv. Rev.202117224927410.1016/j.addr.2021.02.00433561453
     [Google Scholar]
  53. ShiT. SuD. LiuT. TangK. CampD.G.II QianW.J. SmithR.D. Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics.Proteomics20121281074109210.1002/pmic.20110043622577010
     [Google Scholar]
  54. HuangB.X. KimH.Y. Effective identification of Akt interacting proteins by two-step chemical crosslinking, co-immuno- precipitation and mass spectrometry.PLoS One201384e6143010.1371/journal.pone.006143023613850
     [Google Scholar]
  55. HaymondA. DavisJ.B. EspinaV. Proteomics for cancer drug design.Expert Rev. Proteomics201916864766410.1080/14789450.2019.165002531353977
     [Google Scholar]
  56. van MierloG. VermeulenM. Chromatin proteomics to study epigenetics-challenges and opportunities.Mol. Cell. Proteomics20212010005610.1074/mcp.R120.00220833556626
     [Google Scholar]
  57. GeyerP.E. HoldtL.M. TeupserD. MannM. Revisiting biomarker discovery by plasma proteomics.Mol. Syst. Biol.201713994210.15252/msb.2015629728951502
     [Google Scholar]
  58. DenisG.V. McCombM.E. FallerD.V. SinhaA. RomesserP.B. CostelloC.E. Identification of transcription complexes that contain the double bromodomain protein Brd2 and chromatin remodeling machines.J. Proteome Res.20065350251110.1021/pr050430u16512664
     [Google Scholar]
  59. GomaseV. TagoreS. Transcriptomics.Curr. Drug Metab.20089324524910.2174/13892000878388475918336229
     [Google Scholar]
  60. GomaseV. TagoreS. Epigenomics.Curr. Drug Metab.20089323223710.2174/13892000878388482118336226
     [Google Scholar]
  61. WuC. DuanJ. LiuT. SmithR.D. QianW.J. Contributions of immunoaffinity chromatography to deep proteome profiling of human biofluids.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.20161021576810.1016/j.jchromb.2016.01.01526868616
     [Google Scholar]
  62. MoserA.C. HageD.S. Immunoaffinity chromatography: an introduction to applications and recent developments.Bioanalysis20102476979010.4155/bio.10.3120640220
     [Google Scholar]
  63. FangX. ZhangW.W. Affinity separation and enrichment methods in proteomic analysis.J. Proteomics200871328430310.1016/j.jprot.2008.06.01118619565
     [Google Scholar]
  64. Abi-GhanemD.A. BerghmanL.R. Immunoaffinity Chromatography: A Review.Affinity Chromatography. MagdeldinD.S. InTech201295103
     [Google Scholar]
  65. GomaseV. Prediction of antigenic epitopes of neurotoxin Bmbktx1 from Mesobuthus martensii.Curr. Drug Discov. Technol.20063322522910.2174/15701630678013681717311567
     [Google Scholar]
  66. CossarizzaA. ChangH.D. RadbruchA. AcsA. AdamD. Adam-KlagesS. AgaceW.W. AghaeepourN. AkdisM. AllezM. Guidelines for the use of flow cytometry and cell sorting in immunological studies.Eur. J. Immunol.201949101457197310.1002/eji.201970107.
     [Google Scholar]
  67. ShengW. ZhangC. MohiuddinT.M. Al-RaweM. ZeppernickF. FalconeF.H. Meinhold-HeerleinI. HussainA.F. Multiplex immunofluorescence: A powerful tool in cancer immunotherapy.Int. J. Mol. Sci.2023244308610.3390/ijms2404308636834500
     [Google Scholar]
  68. RivestF. ErogluD. PelzB. KowalJ. KehrenA. NavikasV. ProcopioM.G. BordignonP. PérèsE. AmmannM. DorelE. ScalmazziS. BrunoL. RueggM. CampargueG. CasqueiroG. ArnL. FischerJ. BrajkovicS. JorisP. CassanoM. DupouyD. Fully automated sequential immunofluorescence (seqIF) for hyperplex spatial proteomics.Sci. Rep.20231311699410.1038/s41598‑023‑43435‑w37813886
     [Google Scholar]
  69. TanW.C.C. NerurkarS.N. CaiH.Y. NgH.H.M. WuD. WeeY.T.F. LimJ.C.T. YeongJ. LimT.K.H. Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy.Cancer Commun. (Lond.)202040413515310.1002/cac2.1202332301585
     [Google Scholar]
  70. BosisioF.M. VanH.Y. MessiaenJ. BolognesiM.M. MarcelisL. Van HaeleM. CattorettiG. AntoranzA. De SmetF. Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level.Front. Oncol.20221291890010.3389/fonc.2022.91890035992810
     [Google Scholar]
  71. ShakyaR. NguyenT.H. WaterhouseN. KhannaR. Immune contexture analysis in immuno-oncology: Applications and challenges of multiplex fluorescent immunohistochemistry.Clin. Transl. Immunology2020910e118310.1002/cti2.118333072322
     [Google Scholar]
  72. TaylorC.R. LevensonR.M. Quantification of immunohistochemistry-issues concerning methods, utility and semiquantitative assessment II.Histopathology200649441142410.1111/j.1365‑2559.2006.02513.x16978205
     [Google Scholar]
  73. BlindC. KoepenikA. Pacyna-GengelbachM. FernahlG. DeutschmannN. DietelM. KrennV. PetersenI. Antigenicity testing by immunohistochemistry after tissue oxidation.J. Clin. Pathol.2008611798310.1136/jcp.2007.04734017412873
     [Google Scholar]
  74. van den BroekL.J.C.M. van de VijverM.J. Assessment of problems in diagnostic and research immunohistochemistry associated with epitope instability in stored paraffin sections.Appl. Immunohistochem. Mol. Morphol.20008431632110.1097/00129039‑200012000‑0000911127924
     [Google Scholar]
  75. HewittS.M. BaskinD.G. FrevertC.W. StahlW.L. Rosa-MolinarE. Controls for immunohistochemistry.J. Histochem. Cytochem.2014621069369710.1369/002215541454522425023613
     [Google Scholar]
  76. González-MartínezM.Á. PuchadesR. MaquieiraÁ. Immunoanalytical technique: Enzyme-linked immunosorbent assay (ELISA).Modern Techniques for Food Authentication.Elsevier201861765710.1016/B978‑0‑12‑814264‑6.00015‑3
     [Google Scholar]
  77. SlageK.M. GhosnS.J. Immunoassays: Tools for sensitive, specific, and accurate test results.Lab. Med.1996273117183
     [Google Scholar]
  78. WuA.H.B. A selected history and future of immunoassay development and applications in clinical chemistry.Clin. Chim. Acta2006369211912410.1016/j.cca.2006.02.04516701599
     [Google Scholar]
  79. DavidW. The Immunoassay Handbook.AmsterdamElsevier2005
     [Google Scholar]
  80. CoxK.L. DevanarayanV. KriauciunasA. ManettaJ. MontroseC. SittampalamS. Immunoassay Methods.Eli Lilly & Company and the National Center for Advancing Translational Sciences200422553884
     [Google Scholar]
  81. EllingtonA.A. KulloI.J. BaileyK.R. KleeG.G. Antibody-based protein multiplex platforms: Technical and operational challenges.Clin. Chem.201056218619310.1373/clinchem.2009.12751419959625
     [Google Scholar]
  82. EngvallE. PerlmannP. Enzyme-linked immunosorbent assay (ELISA) quantitative assay of immunoglobulin G.Immunochemistry19718987187410.1016/0019‑2791(71)90454‑X5135623
     [Google Scholar]
  83. McCarthyJ. Immunological techniques: ELISA.Detecting Pathogens in Food200324125810.1533/9781855737044.2.241.
     [Google Scholar]
  84. AhsanH. Monoplex and multiplex immunoassays: Approval, advancements, and alternatives.Comp. Clin. Pathol.202131233334510.1007/s00580‑021‑03302‑434840549
     [Google Scholar]
  85. SongZ. MaoJ. BarreroR. WangP. ZhangF. WangT. Development of a CD63 aptamer for efficient cancer immunochemistry and immunoaffinity-based exosome isolation.Molecules20202523558510.3390/molecules2523558533261145
     [Google Scholar]
  86. GreeningD.W. XuR. JiH. TauroB.J. SimpsonR.J. A protocol for exosome isolation and characterization: Evaluation of ultracentrifugation, density-gradient separation, and immunoaffinity capture methods.Methods Mol. Biol.2015129517920910.1007/978‑1‑4939‑2550‑6_1525820723
     [Google Scholar]
  87. TangQ. XiaoX. LiR. HeH. LiS. MaC. Recent advances in detection for breast-cancer-derived exosomes.Molecules20222719667310.3390/molecules2719667336235208
     [Google Scholar]
  88. KangY.T. HadlockT. LoT.W. PurcellE. MutukuriA. FouladdelS. RagueraM.D.S. FairbairnH. MurlidharV. DurhamA. McLeanS.A. NagrathS. Dual-isolation and profiling of circulating tumor cells and cancer exosomes from blood samples with melanoma using immunoaffinity-based microfluidic interfaces.Adv. Sci. (Weinh.)2020719200158110.1002/advs.20200158133042766
     [Google Scholar]
  89. LiP. YuX. HanW. KongY. BaoW. ZhangJ. ZhangW. GuY. Ultrasensitive and reversible nanoplatform of urinary exosomes for prostate cancer diagnosis.ACS Sens.2019451433144110.1021/acssensors.9b0062131017389
     [Google Scholar]
  90. GuH. RenJ.M. JiaX. LevyT. RikovaK. YangV. LeeK.A. StokesM.P. SilvaJ.C. Quantitative profiling of post-translational modifications by immunoaffinity enrichment and LC-MS/MS in cancer serum without immunodepletion.Mol. Cell. Proteomics201615269270210.1074/mcp.O115.05226626635363
     [Google Scholar]
  91. CominettiO. Núñez GalindoA. CorthésyJ. Oller MorenoS. IrincheevaI. ValsesiaA. AstrupA. SarisW.H.M. HagerJ. KussmannM. DayonL. Proteomic biomarker discovery in 1000 human plasma samples with mass spectrometry.J. Proteome Res.201615238939910.1021/acs.jproteome.5b0090126620284
     [Google Scholar]
  92. ParkerC.E. BorchersC.H. Mass spectrometry based biomarker discovery, verification, and validation-Quality assurance and control of protein biomarker assays.Mol. Oncol.20148484085810.1016/j.molonc.2014.03.00624713096
     [Google Scholar]
  93. RifaiN. GilletteM.A. CarrS.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility.Nat. Biotechnol.200624897198310.1038/nbt123516900146
     [Google Scholar]
  94. SkatesS.J. GilletteM.A. LaBaerJ. CarrS.A. AndersonL. LieblerD.C. RansohoffD. RifaiN. KondratovichM. TežakŽ. MansfieldE. ObergA.L. WrightI. BarnesG. GailM. MesriM. KinsingerC.R. RodriguezH. BojaE.S. Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies.J. Proteome Res.201312125383539410.1021/pr400132j24063748
     [Google Scholar]
  95. ZhouC. SimpsonK.L. LancashireL.J. WalkerM.J. DawsonM.J. UnwinR.D. RembielakA. PriceP. WestC. DiveC. WhettonA.D. Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery.J. Proteome Res.20121142103211310.1021/pr200636x22338609
     [Google Scholar]
  96. MannM. KumarC. ZengW.F. StraussM.T. Artificial intelligence for proteomics and biomarker discovery.Cell Syst.202112875977010.1016/j.cels.2021.06.00634411543
     [Google Scholar]
  97. HochrainerK. YangW. Stroke proteomics: From discovery to diagnostic and therapeutic applications.Circ. Res.202213081145116610.1161/CIRCRESAHA.122.32011035420912
     [Google Scholar]
  98. SandinM. ChawadeA. LevanderF. Is label-free LC-MS/MS ready for biomarker discovery?Proteomics Clin. Appl.201593-428929410.1002/prca.20140020225656266
     [Google Scholar]
  99. KeshishianH. BurgessM.W. SpechtH. WallaceL. ClauserK.R. GilletteM.A. CarrS.A. Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry.Nat. Protoc.20171281683170110.1038/nprot.2017.05428749931
     [Google Scholar]
  100. VellanC.J. JayapalanJ.J. YoongB.K. Abdul-AzizA. Mat-JunitS. SubramanianP. Application of proteomics in pancreatic ductal adenocarcinoma biomarker investigations: A review.Int. J. Mol. Sci.2022234209310.3390/ijms2304209335216204
     [Google Scholar]
  101. SchiessR. WollscheidB. AebersoldR. Targeted proteomic strategy for clinical biomarker discovery.Mol. Oncol.200931334410.1016/j.molonc.2008.12.00119383365
     [Google Scholar]
  102. RadulovicD. JelvehS. RyuS. HamiltonT.G. FossE. MaoY. EmiliA. Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry.Mol. Cell. Proteomics200431098499710.1074/mcp.M400061‑MCP20015269249
     [Google Scholar]
  103. SrinivasP.R. VermaM. ZhaoY. SrivastavaS. Proteomics for cancer biomarker discovery.Clin. Chem.20024881160116912142368
     [Google Scholar]
  104. GomaseV. TagoreS. KaleK. BhiwgadeD. Oncogenomics.Curr. Drug Metab.20089319920610.2174/13892000878388471318336222
     [Google Scholar]
  105. GomaseV. TagoreS. ChangbhaleS. KaleK. Pharmacogenomics.Curr. Drug Metab.20089320721210.2174/13892000878388483018336223
     [Google Scholar]
  106. GomaseV.S. TagoreS. Vaccinomics.Gene Ther. Mol. Biol.200812141146
     [Google Scholar]
  107. GomaseV. TagoreS. Blood stage parasites: sufficient to induce protective immunity.Curr. Drug Metab.20089323824010.2174/13892000878388470418336227
     [Google Scholar]
  108. GomaseV.S. TagoreS. ShyamkumarK. Prediction of antigenic binders from C-terminal domain human papillomavirus oncoprotein E7.Gene Ther. Mol. Biol.200812147166
     [Google Scholar]
  109. GomaseV.S. KaleK.V. Development of MHC class nonamers from cowpea mosaic viral protein.Gene Ther. Mol. Biol.2008128794
     [Google Scholar]
  110. GomaseV.S. KaleK.V. Prediction of MHC binder for fragment based viral peptide vaccines from cabbage leaf curl virus.Gene Ther. Mol. Biol.2008128386
     [Google Scholar]
  111. GomaseV.S. TagoreS. Transgenomics.Gene Ther. Mol. Biol.2008127782
     [Google Scholar]
  112. GomaseV.S. KaleK.V. Antigenic epitopes of viral polyprotein: An approach for fragment based peptide vaccines from papaya ringspot virus.Gene Ther. Mol. Biol.2008123138
     [Google Scholar]
  113. OjhaR. PandeyR.K. PrajapatiV.K. Vaccinomics strategy to concoct a promising subunit vaccine for visceral leishmaniasis targeting sandfly and leishmania antigens.Int. J. Biol. Macromol.202015654855710.1016/j.ijbiomac.2020.04.09732311400
     [Google Scholar]
  114. NeldeA. RammenseeH.G. WalzJ.S. The peptide vaccine of the future.Mol. Cell. Proteomics20212010002210.1074/mcp.R120.00230933583769
     [Google Scholar]
  115. DuY. HuX. MiaoL. ChenJ. Current status and development prospects of aquatic vaccines.Front. Immunol.202213104033610.3389/fimmu.2022.104033636439092
     [Google Scholar]
  116. GuptaM. WahiA. SharmaP. NagpalR. RainaN. KauravM. BhattacharyaJ. Rodrigues OliveiraS.M. DolmaK.G. PaulA.K. de Lourdes PereiraM. WilairatanaP. RahmatullahM. NissapatornV. Recent advances in cancer vaccines: Challenges, achievements, and futuristic prospects.Vaccines (Basel)20221012201110.3390/vaccines1012201136560420
     [Google Scholar]
  117. ZhuangL. YeZ. LiL. YangL. GongW. Next-GenerationT.B. Next-generation TB vaccines: Progress, challenges, and prospects.Vaccines (Basel)2023118130410.3390/vaccines1108130437631874
     [Google Scholar]
  118. StengerS. GrasshoffH. HundtJ.E. LangeT. Potential effects of shift work on skin autoimmune diseases.Front. Immunol.202313100095110.3389/fimmu.2022.100095136865523
     [Google Scholar]
  119. MaW.T. GaoF. GuK. ChenD.K. The role of monocytes and macrophages in autoimmune diseases: A comprehensive review.Front. Immunol.201910114010.3389/fimmu.2019.0114031178867
     [Google Scholar]
  120. GanesanV. SchmidtB. AvulaR. CookeD. MaggiacomoT. TellinL. AschermanD.P. BruchezM.P. MindenJ. Immuno-proteomics: Development of a novel reagent for separating antibodies from their target proteins.Biochim. Biophys. Acta. Proteins Proteomics20151854659260010.1016/j.bbapap.2014.10.01125466873
     [Google Scholar]
  121. ZubairM. WangJ. YuY. FaisalM. QiM. ShahA.U. FengZ. ShaoG. WangY. XiongQ. Proteomics approaches: A review regarding an importance of proteome analyses in understanding the pathogens and diseases.Front. Vet. Sci.20229107935910.3389/fvets.2022.107935936601329
     [Google Scholar]
  122. WhiteakerJ.R. SharmaK. HoffmanM.A. KuhnE. ZhaoL. CoccoA.R. SchoenherrR.M. KennedyJ.J. VoytovichU. LinC. FangB. BowersK. WhiteleyG. ColantonioS. BocikW. RobertsR. HiltkeT. BojaE. RodriguezH. McCormickF. HolderfieldM. CarrS.A. KoomenJ.M. PaulovichA.G. Targeted mass-spectrometry-based assays enable multiplex quantification of receptor tyrosine kinase, MAP kinase, and AKT signaling.Cell Rep. Methods20211310001510.1016/j.crmeth.2021.10001534671754
     [Google Scholar]
  123. RebeccaV.W. WoodE. FedorenkoI.V. ParaisoK.H.T. HaarbergH.E. ChenY. XiangY. SarnaikA. GibneyG.T. SondakV.K. KoomenJ.M. SmalleyK.S.M. Evaluating melanoma drug response and therapeutic escape with quantitative proteomics.Mol. Cell. Proteomics20141371844185410.1074/mcp.M113.03742424760959
     [Google Scholar]
  124. SchoenherrR.M. SaulR.G. WhiteakerJ.R. YanP. WhiteleyG.R. PaulovichA.G. Anti-peptide monoclonal antibodies generated for immuno-multiple reaction monitoring-mass spectrometry assays have a high probability of supporting Western blot and ELISA.Mol. Cell. Proteomics201514238239810.1074/mcp.O114.04313325512614
     [Google Scholar]
  125. PernaF. BermanS.H. SoniR.K. Mansilla-SotoJ. EyquemJ. HamiehM. HendricksonR.C. BrennanC.W. SadelainM. Integrating proteomics and transcriptomics for systematic combinatorial chimeric antigen receptor therapy ofAML.Cancer Cell2017324506519.e510.1016/j.ccell.2017.09.00429017060
     [Google Scholar]
  126. RaghunathanR. TurajaneK. WongL.C. Biomarkers in neurodegenerative diseases: Proteomics spotlight on ALS and Parkinson’s disease.Int. J. Mol. Sci.20222316929910.3390/ijms2316929936012563
     [Google Scholar]
  127. ZhuJ. NieS. WuJ. LubmanD.M. Target proteomic profiling of frozen pancreatic CD24+ adenocarcinoma tissues by immuno-laser capture microdissection and nano-LC-MS/MS.J. Proteome Res.20131262791280410.1021/pr400139c23679566
     [Google Scholar]
  128. MacMullanM.A. DunnZ.S. GrahamN. YangL. WangP. Quantitative proteomics and metabolomics reveal biomarkers of disease as potential immunotherapy targets and indicators of therapeutic efficacy.Theranostics20199257872788810.7150/thno.3737331695805
     [Google Scholar]
  129. ShiT. SongE. NieS. RodlandK.D. LiuT. QianW.J. SmithR.D. Advances in targeted proteomics and applications to biomedical research.Proteomics20161615-162160218210.1002/pmic.20150044927302376
     [Google Scholar]
  130. KobeissyF. GoliM. YadikarH. ShakkourZ. KurupM. HaidarM.A. AlroumiS. MondelloS. WangK.K. MechrefY. Advances in neuroproteomics for neurotrauma: Unraveling insights for personalized medicine and future prospects.Front. Neurol.202314128874010.3389/fneur.2023.128874038073638
     [Google Scholar]
  131. VizcaínoJ.A. KubiniokP. KovalchikK.A. MaQ. DuquetteJ.D. MongrainI. DeutschE.W. PetersB. SetteA. SiroisI. CaronE. The human immunopeptidome project: A roadmap to predict and treat immune diseases.Mol. Cell. Proteomics2020191314910.1074/mcp.R119.00174331744855
     [Google Scholar]
  132. SpiraA. DisisM.L. SchillerJ.T. VilarE. RebbeckT.R. BejarR. IdekerT. ArtsJ. YurgelunM.B. MesirovJ.P. RaoA. GarberJ. JaffeeE.M. LippmanS.M. Leveraging premalignant biology for immune-based cancer prevention.Proc. Natl. Acad. Sci. USA201611339107501075810.1073/pnas.160807711327638202
     [Google Scholar]
  133. JacobM. MasoodA. ShinwariZ. Abdel JabbarM. Al-MousaH. ArnaoutR. AlSaudB. DasoukiM. AlaiyaA.A. Abdel RahmanA.M. Proteomics profiling to distinguish DOCK8 deficiency from atopic dermatitis.Front. Allergy2021277490210.3389/falgy.2021.77490235386989
     [Google Scholar]
  134. PulendranB. DavisM.M. The science and medicine of human immunology.Science20203696511eaay401410.1126/science.aay401432973003
     [Google Scholar]
  135. JayawardanaK. SchrammS.J. HayduL. ThompsonJ.F. ScolyerR.A. MannG.J. MüllerS. YangJ.Y.H. Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information.Int. J. Cancer2015136486387410.1002/ijc.2904724975271
     [Google Scholar]
  136. MoQ. WangS. SeshanV.E. OlshenA.B. SchultzN. SanderC. PowersR.S. LadanyiM. ShenR. Pattern discovery and cancer gene identification in integrated cancer genomic data.Proc. Natl. Acad. Sci. USA2013110114245425010.1073/pnas.120894911023431203
     [Google Scholar]
  137. WuD. WangD. ZhangM.Q. GuJ. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: Application to cancer molecular classification.BMC Genomics2015161102210.1186/s12864‑015‑2223‑826626453
     [Google Scholar]
  138. DevonshireA. GautamY. JohanssonE. MershaT.B. Multi-omics profiling approach in food allergy.World Allergy Organ. J.202316510077710.1016/j.waojou.2023.10077737214173
     [Google Scholar]
  139. JainK.K. Innovations, challenges and future prospects of oncoproteomics.Mol. Oncol.20082215316010.1016/j.molonc.2008.05.00319383334
     [Google Scholar]
  140. PunethaA. KotiyaD. Advancements in oncoproteomics technologies: Treading toward translation into clinical practice.Proteomes2023111210.3390/proteomes1101000236648960
     [Google Scholar]
  141. López VillarE. WangX. MaderoL. ChoW.C. Application of oncoproteomics to aberrant signalling networks in changing the treatment paradigm in acute lymphoblastic leukaemia.J. Cell. Mol. Med.2015191465210.1111/jcmm.1250725537633
     [Google Scholar]
  142. KarhemoP.R. HyvönenM. LaakkonenP. Metastasis-associated cell surface oncoproteomics.Front. Pharmacol.2012319210.3389/fphar.2012.0019223162466
     [Google Scholar]
  143. ChoW.C.S. Contribution of oncoproteomics to cancer biomarker discovery.Mol. Cancer2007612510.1186/1476‑4598‑6‑2517407558
     [Google Scholar]
  144. AlhamdaniM.S.S. SchröderC. HoheiselJ.D. Oncoproteomic profiling with antibody microarrays.Genome Med.2009176810.1186/gm6819591665
     [Google Scholar]
  145. JainK.K. Recent advances in clinical oncoproteomics.J. Balkan Union Oncol.200712S31S3817935275
     [Google Scholar]
  146. ZhouL. LiQ. WangJ. HuangC. NiceE.C. Oncoproteomics: Trials and tribulations.Proteomics Clin. Appl.201610451653110.1002/prca.20150008126518147
     [Google Scholar]
  147. ChoW.C.S. ChengC.H.K. Oncoproteomics: Current trends and future perspectives.Expert Rev. Proteomics20074340141010.1586/14789450.4.3.40117552924
     [Google Scholar]
  148. RobozJ. Mass spectrometry in diagnostic oncoproteomics.Cancer Invest.200523546547810.1081/CNV‑6718216193645
     [Google Scholar]
  149. JainK.K. Role of oncoproteomics in the personalized management of cancer.Expert Rev. Proteomics200411495510.1586/14789450.1.1.4915966798
     [Google Scholar]
  150. ShuklaH.D. MahmoodJ. VujaskovicZ. Integrated proteo-genomic approach for early diagnosis and prognosis of cancer.Cancer Lett.20153691283610.1016/j.canlet.2015.08.00326276717
     [Google Scholar]
  151. JoshiS. TiwariA.K. MondalB. SharmaA. Oncoproteomics.Clin. Chim. Acta20114123-421722610.1016/j.cca.2010.10.00220955692
     [Google Scholar]
  152. VsG. KrishnanS. Phospho-onco-proteomics.Int. J. Genet.20091161510.9735/0975‑2862.1.1.6‑15
     [Google Scholar]
  153. FourmanL.T. StanleyT.L. OckeneM.W. McClureC.M. ToribioM. CoreyK.E. ChungR.T. TorrianiM. KleinerD.E. HadiganC.M. GrinspoonS.K. Proteomic analysis of hepatic fibrosis in human immunodeficiency virus–associated nonalcoholic fatty liver disease demonstrates up-regulation of immune response and tissue repair pathways.J. Infect. Dis.2023227456557610.1093/infdis/jiac47536461941
     [Google Scholar]
  154. LoreyM. AdlerB. YanH. SoliymaniR. EkströmS. Yli-KauhaluomaJ. LaurellT. BaumannM. Mass-tag enhanced immuno-laser desorption/ionization mass spectrometry for sensitive detection of intact protein antigens.Anal. Chem.201587105255526210.1021/acs.analchem.5b0030425867450
     [Google Scholar]
  155. YehC.H. HuangH.H. ChangT.C. LinH.P. LinY.C. Using an electro-microchip, a nanogold probe, and silver enhancement in an immunoassay.Biosens. Bioelectron.20092461661166610.1016/j.bios.2008.08.03918838263
     [Google Scholar]
  156. YousafM. IsmailS. UllahA. BibiS. Immuno-informatics profiling of monkeypox virus cell surface binding protein for designing a next generation multi-valent peptide-based vaccine.Front. Immunol.202213103592410.3389/fimmu.2022.103592436405737
     [Google Scholar]
  157. DutschA. UhligC. BockM. GraesserC. SchuchardtS. UhligS. SchunkertH. JonerM. HoldenriederS. LechnerK. Multi-omic candidate screening for markers of severe clinical courses of COVID-19.J. Clin. Med.20231219622510.3390/jcm1219622537834869
     [Google Scholar]
  158. OualiR. VieiraL.R. SalmonD. BousbataS. Rhodnius prolixus hemolymph immuno-physiology: deciphering the systemic immune response triggered by Trypanosoma cruzi establishment in the vector using quantitative proteomics.Cells2022119144910.3390/cells1109144935563760
     [Google Scholar]
  159. MoensC. FiléeP. BoesA. AlieC. DufrasneF. AndréE. MarchéS. FretinD. Identification of new Mycobacterium bovis antigens and development of a multiplexed serological bead-immunoassay for the diagnosis of bovine tuberculosis in cattle.PLoS One20231810e029259010.1371/journal.pone.029259037812634
     [Google Scholar]
  160. FrigerioR. MusicòA. StradaA. MussidaA. GagniP. BergamaschiG. ChiariM. BarzonL. GoriA. CretichM. Epitope mapping on microarrays highlights a sequence on the N protein with strong immune response in SARS-CoV-2 patients.Methods Mol. Biol.2023257820921710.1007/978‑1‑0716‑2732‑7_1536152290
     [Google Scholar]
  161. Aparici-HerraizI. Gualdrón-LópezM. Castro-CavadíaC.J. Carmona-FonsecaJ. YasnotM.F. Fernandez-BecerraC. del PortilloH.A. Antigen discovery in circulating extracellular vesicles From Plasmodium vivax patients.Front. Cell. Infect. Microbiol.20221181139010.3389/fcimb.2021.81139035141172
     [Google Scholar]
  162. HuB. SajidM. LvR. LiuL. SunC. A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology.Front. Immunol.20221399672110.3389/fimmu.2022.99672136389765
     [Google Scholar]
  163. MausA. FigdoreD. MilosevicD. Algeciras-SchimnichA. BornhorstJ. Comparison of intact protein and digested peptide techniques for high throughput proteotyping of ApoE.Clin. Proteomics20221914210.1186/s12014‑022‑09379‑536380282
     [Google Scholar]
  164. ZhangY.V. WeiB. ZhuY. ZhangY. BluthM.H. Liquid Chromatography–Tandem Mass Spectrometry.Clin. Lab. Med.201636463566110.1016/j.cll.2016.07.00127842783
     [Google Scholar]
  165. HirtzC. VialaretJ. NouadjeG. SchraenS. BenlianP. MaryS. PhilibertP. TiersL. BrosP. DelabyC. GabelleA. LehmannS. Development of new quantitative mass spectrometry and semi-automatic isofocusing methods for the determination of Apolipoprotein E typing.Clin. Chim. Acta2016454333810.1016/j.cca.2015.12.02026707914
     [Google Scholar]
  166. SignoreM. ManganelliV. Reverse phase protein arrays in cancer stem cells.Methods Cell Biol.2022171336110.1016/bs.mcb.2022.04.00435953205
     [Google Scholar]
  167. WhiteakerJ.R. LundeenR.A. ZhaoL. SchoenherrR.M. BurianA. HuangD. VoytovichU. WangT. KennedyJ.J. IveyR.G. LinC. MurilloO.D. LorentzenT.D. ThiagarajanM. ColantonioS. CaceresT.W. RobertsR.R. KnottsJ.G. ReadingJ.J. KaczmarczykJ.A. RichardsonC.W. Garcia-BuntleyS.S. BocikW. HewittS.M. MurrayK.E. DoN. BrophyM. WilzS.W. YuH. AjjarapuS. BojaE. HiltkeT. RodriguezH. PaulovichA.G. Targeted mass spectrometry enables multiplexed quantification of immunomodulatory proteins in clinical biospecimens.Front. Immunol.20211276589810.3389/fimmu.2021.76589834858420
     [Google Scholar]
  168. ScaranoC. VenerusoI. De SimoneR.R. Di BonitoG. SecondinoA. D’ArgenioV. The third-generation sequencing challenge: Novel insights for the omic sciences.Biomolecules202414556810.3390/biom1405056838785975
     [Google Scholar]
  169. DezemF.S. ArjumandW. DuBoseH. MorosiniN.S. PlummerJ. Spatially resolved single-cell omics: Methods, challenges, and future perspectives.Annu. Rev. Biomed. Data Sci.20247113115310.1146/annurev‑biodatasci‑102523‑10364038768396
     [Google Scholar]
  170. TanY.C. LowT.Y. LeeP.Y. LimL.C. Single-cell proteomics by mass spectrometry: Advances and implications in cancer research.Proteomics20242412-13230021010.1002/pmic.20230021038727198
     [Google Scholar]
  171. GomaseV. TagoreS. RNAi-a tool for target finding in new drug development.Curr. Drug Metab.20089324124410.2174/13892000878388477718336228
     [Google Scholar]
  172. GomaseV.S. ParundekarA.N. microRNA: human disease and development.Int. J. Bioinform. Res. Appl.20095547950010.1504/IJBRA.2009.02867819778865
     [Google Scholar]
  173. AgiotiS. ZaravinosA. Immune cytolytic activity and strategies for therapeutic treatment.Int. J. Mol. Sci.2024257362410.3390/ijms2507362438612436
     [Google Scholar]
  174. MishraS. GomaseV.S. In silico insights to predict the major histocompatibility complex peptide binders from protein.Eur. J. Mol. Clin. Med.202182219226
     [Google Scholar]
  175. GomaseV.S. KaleK.V. ChikhaleN.J. ChangbhaleS.S. Prediction of MHC binding peptides and epitopes from alfalfa mosaic virus.Curr. Drug Discov. Technol.20074211712510.2174/15701630778148344117691913
     [Google Scholar]
  176. MishraS. GomaseV.S. Application of in silico approach in prediction of epitopes.Res. J. Biotechnol.2021162206214
     [Google Scholar]
  177. PorfetyeA.T. StegeP. Rebollido-RiosR. HoffmannD. SchraderT. VetterI.R. How do molecular tweezers bind to proteins? Lessons from x-ray crystallography.Molecules2024298176410.3390/molecules2908176438675584
     [Google Scholar]
  178. BockL.V. IgaevM. GrubmüllerH. Single-particle Cryo-EM and molecular dynamics simulations: A perfect match.Curr. Opin. Struct. Biol.20248610282510.1016/j.sbi.2024.10282538723560
     [Google Scholar]
  179. den BoonJ. A. NishikioriM. ZhanH. AhlquistP. Positive-strand RNA virus genome replication organelles: Structure, assembly, control.Trends Genet.202440868169310.1016/j.tig.2024.04.003.
     [Google Scholar]
  180. GomaseV.S. KemkarK.R. BaviskarB.A. MundheV.S. SakhareA.D. KolsureA.K. BhimanwarA.A. DhamaneS.P. PotnisV.V. Physicochemical and immunoproteomic analysis to design synthetic peptide vaccine from naja kaouthia neurotoxin.World J. Pharm. Res.202312412861291
     [Google Scholar]
  181. GomaseV.S. ChitlangeN.R. Sensitive quantitative predictions of MHC binding peptides and fragment based peptide vaccines from taenia crassiceps.J. Vaccines Vaccin.201231131
     [Google Scholar]
  182. GomaseV.S. KemkarK.R. BaviskarB.A. MundheV.S. SakhareA.D. KolsureA.K. BhimanwarA.A. DhamaneS.P. PotnisV.V. Immunoinformatics study of physical properties of scorpion neurotoxin Bmk-M8 from Mesobuthus martensii .Int. J. Med. Pharm. Res.2023731315
     [Google Scholar]
  183. GomaseV.S. PangarkarP.R. KemkarK.R. Immunoproteomics physicochemical analysis of heterodimeric neurotoxic phospholipases A2 from apis cerana.Int. J. Pharm. Res.2023151118123
     [Google Scholar]
  184. LarocheC. EngenR.M. Immune monitoring in pediatric kidney transplant.Pediatr. Transplant.2024284e1478510.1111/petr.1478538766986
     [Google Scholar]
  185. IyerM. RavichandranN. KaruppusamyP.A. GnanarajanR. YadavM.K. NarayanasamyA. VellingiriB. Molecular insights and promise of oncolytic virus based immunotherapy.Adv. Protein Chem. Struct. Biol.202414041949210.1016/bs.apcsb.2023.12.00738762277
     [Google Scholar]
  186. GomaseV. TagoreS. KaleK. Microarray: An approach for current drug targets.Curr. Drug Metab.20089322123110.2174/13892000878388479518336225
     [Google Scholar]
  187. GomaseV.S. TripathiA.K. TagoreS. Cellunomics: The interaction analysis of cells.Int. J. Bioinform. Res. Appl.20095667469010.1504/IJBRA.2009.02904619887340
     [Google Scholar]
  188. GomaseV.S. TagoreS. Phylogenomics: Evolution and genomics intersection.Int. J. Bioinform. Res. Appl.20095554856310.1504/IJBRA.2009.02868219778869
     [Google Scholar]
  189. GomaseV. TagoreS. Omics: An approach for drug targets.Curr. Drug Metab.20089318910.2174/13892000878388472218336219
     [Google Scholar]
  190. GomaseV. TagoreS. Toxicogenomics.Curr. Drug Metab.20089325025410.2174/13892000878388469618336230
     [Google Scholar]
  191. GomaseV. ChangbhaleS. PatilS. KaleK. Metabolomics.Curr. Drug Metab.200891899810.2174/13892000878333114918220576
     [Google Scholar]
/content/journals/ppl/10.2174/0109298665333029240926092919
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Immunoproteomics: A Review of Techniques, Applications, and Advancements
PPL 31, 827 (2024); https://doi.org/10.2174/0109298665333029240926092919
/content/journals/ppl/10.2174/0109298665333029240926092919
/content/journals/ppl/10.2174/0109298665333029240926092919
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  • Article Type:     Review Article
Keyword(s): antibody-based assays; cancer immunotherapy; computational analysis; Immunoproteomics; immunotherapies; mass spectrometry

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