3c5j Citations

The structure of HLA-DR52c: comparison to other HLA-DRB3 alleles.

Proc Natl Acad Sci U S A 105 11893-7 (2008)
Cited: 60 times
EuropePMC logo PMID: 18697946

Abstract

Class II major histocompatibility complex (MHCII) molecules present antigens to CD4(+) T cells. In addition to the most commonly studied human MHCII isotype, HLA-DR, whose beta chain is encoded by the HLA-DRB1 locus, several other isotypes that use the same alpha chain but have beta chains encoded by other genes. These other DR molecules also are expressed in antigen-presenting cells and are known to participate in peptide presentation to T cells and to be recognized as alloantigens by other T cells. Like some of the HLA-DRB1 alleles, several of these alternate DR molecules have been associated with specific autoimmune diseases and T cell hypersensitivity. Here we present the structure of an HLA-DR molecule (DR52c) containing one of these alternate beta chains (HLA-DRB3*0301) bound to a self-peptide derived from the Tu elongation factor. The molecule shares structurally conserved elements with other MHC class II molecules but has some unique features in the peptide-binding groove. Comparison of the three major HLA-DBR3 alleles (DR52a, b, and c) suggests that they were derived from one another by recombination events that scrambled the four major peptide-binding pockets at peptide positions 1, 4, 6, and 9 but left virtually no polymorphisms elsewhere in the molecules.

Reviews - 3c5j mentioned but not cited (1)

Articles - 3c5j mentioned but not cited (43)

  1. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, Nielsen M. Immunogenetics 67 641-650 (2015)
  2. TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. Zhang L, Chen Y, Wong HS, Zhou S, Mamitsuka H, Zhu S. PLoS One 7 e30483 (2012)
  3. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS Coronavirus-2 (SARS-CoV-2). Sarkar B, Ullah MA, Johora FT, Taniya MA, Araf Y. Immunobiology 225 151955 (2020)
  4. PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity. Oyarzún P, Ellis JJ, Bodén M, Kobe B. BMC Bioinformatics 14 52 (2013)
  5. The structure of HLA-DR52c: comparison to other HLA-DRB3 alleles. Dai S, Crawford F, Marrack P, Kappler JW. Proc. Natl. Acad. Sci. U.S.A. 105 11893-11897 (2008)
  6. Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes. Bordner AJ. PLoS One 5 e14383 (2010)
  7. Combination of In Silico Methods in the Search for Potential CD4(+) and CD8(+) T Cell Epitopes in the Proteome of Leishmania braziliensis. E Silva Rde F, Ferreira LF, Hernandes MZ, de Brito ME, de Oliveira BC, da Silva AA, de-Melo-Neto OP, Rezende AM, Pereira VR. Front Immunol 7 327 (2016)
  8. In-Silico Proteomic Exploratory Quest: Crafting T-Cell Epitope Vaccine Against Whipple's Disease. Joshi A, Kaushik V. Int J Pept Res Ther 27 169-179 (2021)
  9. Identification of vaccine and drug targets in Shigella dysenteriae sd197 using reverse vaccinology approach. Jalal K, Abu-Izneid T, Khan K, Abbas M, Hayat A, Bawazeer S, Uddin R. Sci Rep 12 251 (2022)
  10. A Newly Recognized Pairing Mechanism of the α- and β-Chains of the Chicken Peptide-MHC Class II Complex. Zhang L, Li X, Ma L, Zhang B, Meng G, Xia C. J Immunol 204 1630-1640 (2020)
  11. Genome based evolutionary lineage of SARS-CoV-2 towards the development of novel chimeric vaccine. Akhand MRN, Azim KF, Hoque SF, Moli MA, Joy BD, Akter H, Afif IK, Ahmed N, Hasan M. Infect Genet Evol 85 104517 (2020)
  12. An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles. Shen WJ, Zhang S, Wong HS. Proteome Sci 11 S15 (2013)
  13. Eleven Amino Acids of HLA-DRB1 and Fifteen Amino Acids of HLA-DRB3, 4, and 5 Include Potentially Causal Residues Responsible for the Risk of Childhood Type 1 Diabetes. Zhao LP, Papadopoulos GK, Kwok WW, Xu B, Kong M, Moustakas AK, Bondinas GP, Carlsson A, Elding-Larsson H, Ludvigsson J, Marcus C, Persson M, Samuelsson U, Wang R, Pyo CW, Nelson WC, Geraghty DE, Lernmark Å. Diabetes 68 1692-1704 (2019)
  14. Proteomic Exploration of Listeria monocytogenes for the Purpose of Vaccine Designing Using a Reverse Vaccinology Approach. Srivastava S, Sharma SK, Srivastava V, Kumar A. Int J Pept Res Ther 27 779-799 (2021)
  15. T-cell epitope-based vaccine designing against Orthohantavirus: a causative agent of deadly cardio-pulmonary disease. Joshi A, Ray NM, Singh J, Upadhyay AK, Kaushik V. Netw Model Anal Health Inform Bioinform 11 2 (2022)
  16. Can molecular mimicry explain the cytokine storm of SARS-CoV-2?: An in silico approach. Obando-Pereda G. J Med Virol 93 5350-5357 (2021)
  17. Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. Solanki V, Tiwari V. Sci Rep 8 9044 (2018)
  18. Combination of highly antigenic nucleoproteins to inaugurate a cross-reactive next generation vaccine candidate against Arenaviridae family. Azim KF, Lasker T, Akter R, Hia MM, Bhuiyan OF, Hasan M, Hossain MN. Heliyon 7 e07022 (2021)
  19. Immunoinformatic approach to design a multiepitope vaccine targeting non-mutational hotspot regions of structural and non-structural proteins of the SARS CoV2. Solanki V, Tiwari M, Tiwari V. PeerJ 9 e11126 (2021)
  20. Molecular docking and dynamic simulation of conserved B cell epitope of SARS-CoV-2 glycoprotein Indonesian isolates: an immunoinformatic approach. Rantam FA, Kharisma VD, Sumartono C, Nugraha J, Wijaya AY, Susilowati H, Kuncorojakti S, Nugraha AP. F1000Res 10 Chem Inf Sci-813 (2021)
  21. Reverse vaccinology approach for multi-epitope centered vaccine design against delta variant of the SARS-CoV-2. Jalal K, Khan K, Basharat Z, Abbas MN, Uddin R, Ali F, Khan SA, Hassan SSU. Environ Sci Pollut Res Int 29 60035-60053 (2022)
  22. A comprehensive analysis of amino-peptidase N1 protein (APN) from Anopheles culicifacies for epitope design using Immuno-informatics models. Jakhar R, Kumar P, Sehrawat N, Gakhar SK. Bioinformation 15 600-612 (2019)
  23. HLA-Modeler: Automated Homology Modeling of Human Leukocyte Antigens. Amari S, Kataoka R, Ikegami T, Hirayama N. Int J Med Chem 2013 690513 (2013)
  24. Immunoinformatic approach for the construction of multi-epitopes vaccine against omicron COVID-19 variant. Khan K, Khan SA, Jalal K, Ul-Haq Z, Uddin R. Virology 572 28-43 (2022)
  25. Impact of HLA-DR Antigen Binding Cleft Rigidity on T Cell Recognition. Szeto C, Bloom JI, Sloane H, Lobos CA, Fodor J, Jayasinghe D, Chatzileontiadou DSM, Grant EJ, Buckle AM, Gras S. Int J Mol Sci 21 E7081 (2020)
  26. A Systematic Immuno-Informatic Approach to Design a Multiepitope-Based Vaccine Against Emerging Multiple Drug Resistant Serratia marcescens. Damas MSF, Mazur FG, Freire CCM, da Cunha AF, Pranchevicius MDS. Front Immunol 13 768569 (2022)
  27. An automated framework for understanding structural variations in the binding grooves of MHC class II molecules. Yeturu K, Utriainen T, Kemp GJ, Chandra N. BMC Bioinformatics 11 Suppl 1 S55 (2010)
  28. Anticipation of Antigenic Sites for the Goal of Vaccine Designing Against Nipah Virus: An Immunoinformatics Inquisitive Quest. Sharma SK, Srivastava S, Kumar A, Srivastava V. Int J Pept Res Ther 27 1899-1911 (2021)
  29. Computer-aided genomic data analysis of drug-resistant Neisseria gonorrhoeae for the Identification of alternative therapeutic targets. Qasim A, Jaan S, Wara TU, Shehroz M, Nishan U, Shams S, Shah M, Ojha SC. Front Cell Infect Microbiol 13 1017315 (2023)
  30. Core Proteomics and Immunoinformatic Approaches to Design a Multiepitope Reverse Vaccine Candidate against Chagas Disease. Islam SI, Sanjida S, Ahmed SS, Almehmadi M, Allahyani M, Aljuaid A, Alsaiari AA, Halawi M. Vaccines (Basel) 10 1669 (2022)
  31. Design of a Multi-epitope Vaccine Against Acinetobacter baumannii Using Immunoinformatics Approach. Touhidinia M, Sefid F, Bidakhavidi M. Int J Pept Res Ther 27 2417-2437 (2021)
  32. Designing a multi-epitope vaccine against coxsackievirus B based on immunoinformatics approaches. Huang S, Zhang C, Li J, Dai Z, Huang J, Deng F, Wang X, Yue X, Hu X, Li Y, Deng Y, Wang Y, Zhao W, Zhong Z, Wang Y. Front Immunol 13 933594 (2022)
  33. Highly conserved hemagglutinin peptides of H1N1 influenza virus elicit immune response. Lohia N, Baranwal M. 3 Biotech 8 492 (2018)
  34. Immunoinformatic paradigm predicts macrophage and T-cells epitope responses against globally conserved spike fragments of SARS CoV-2 for universal vaccination. Maiti S, Banerjee A, Santra D, Kanwar M. Int Immunopharmacol 108 108847 (2022)
  35. Immunoinformatic-guided designing of multi-epitope vaccine construct against Brucella Suis 1300. Jalal K, Khan K, Uddin R. Immunol Res (2022)
  36. Immunoinformatics Approach to Design a Multi-Epitope Nanovaccine against Leishmania Parasite: Elicitation of Cellular Immune Responses. Margaroni M, Agallou M, Tsanaktsidou E, Kammona O, Kiparissides C, Karagouni E. Vaccines (Basel) 11 304 (2023)
  37. Insight into the first multi-epitope-based peptide subunit vaccine against avian influenza A virus (H5N6): An immunoinformatics approach. Mia MM, Hasan M, Ahmed S, Rahman MN. Infect Genet Evol 104 105355 (2022)
  38. Multi-Epitope Vaccine for Monkeypox Using Pan-Genome and Reverse Vaccinology Approaches. Swetha RG, Basu S, Ramaiah S, Anbarasu A. Viruses 14 2504 (2022)
  39. Multi-epitope chimeric vaccine designing and novel drug targets prioritization against multi-drug resistant Staphylococcus pseudintermedius. Jaan S, Shah M, Ullah N, Amjad A, Javed MS, Nishan U, Mustafa G, Nawaz H, Ahmed S, Ojha SC. Front Microbiol 13 971263 (2022)
  40. Pan-Genome Reverse Vaccinology Approach for the Design of Multi-Epitope Vaccine Construct against Escherichia albertii. Jalal K, Khan K, Ahmad D, Hayat A, Basharat Z, Abbas MN, Alghamdi S, Almehmadi M, Sahibzada MUK. Int J Mol Sci 22 12814 (2021)
  41. Predicting Humoral Alloimmunity from Differences in Donor and Recipient HLA Surface Electrostatic Potential. Mallon DH, Kling C, Robb M, Ellinghaus E, Bradley JA, Taylor CJ, Kabelitz D, Kosmoliaptsis V. J. Immunol. 201 3780-3792 (2018)
  42. Prioritization of potential vaccine targets using comparative proteomics and designing of the chimeric multi-epitope vaccine against Pseudomonas aeruginosa. Solanki V, Tiwari M, Tiwari V. Sci Rep 9 5240 (2019)
  43. Proteome Exploration of Legionella pneumophila To Identify Novel Therapeutics: a Hierarchical Subtractive Genomics and Reverse Vaccinology Approach. Khan MT, Mahmud A, Hasan M, Azim KF, Begum MK, Rolin MH, Akter A, Mondal SI. Microbiol Spectr 10 e0037322 (2022)


Reviews citing this publication (4)

  1. T cell antigen receptor recognition of antigen-presenting molecules. Rossjohn J, Gras S, Miles JJ, Turner SJ, Godfrey DI, McCluskey J. Annu. Rev. Immunol. 33 169-200 (2015)
  2. Linkers in the structural biology of protein-protein interactions. Reddy Chichili VP, Kumar V, Sivaraman J. Protein Sci. 22 153-167 (2013)
  3. Structural basis of metal hypersensitivity. Wang Y, Dai S. Immunol. Res. 55 83-90 (2013)
  4. Major histocompatibility complex variation and the evolution of resistance to amphibian chytridiomycosis. Fu M, Waldman B. Immunogenetics 69 529-536 (2017)

Articles citing this publication (12)

  1. Monitoring of NY-ESO-1 specific CD4+ T cells using molecularly defined MHC class II/His-tag-peptide tetramers. Ayyoub M, Dojcinovic D, Pignon P, Raimbaud I, Schmidt J, Luescher I, Valmori D. Proc. Natl. Acad. Sci. U.S.A. 107 7437-7442 (2010)
  2. Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. Erlich HA, Valdes AM, McDevitt SL, Simen BB, Blake LA, McGowan KR, Todd JA, Rich SS, Noble JA, Type 1 Diabetes Genetics Consortium (T1DGC). Diabetes 62 2618-2622 (2013)
  3. The conundrum of HLA-DRB1*14:01/*14:54 and HLA-DRB3*02:01/*02:02 mismatches in unrelated hematopoietic SCT. Pasi A, Crocchiolo R, Bontempelli M, Carcassi C, Carella G, Crespiatico L, Garbarino L, Mascaretti L, Mazzi B, Mazzola G, Miotti V, Porfirio B, Tagliaferri C, Valentini T, Vecchiato C, Fleischhauer K, Sacchi N, Bosi A, Martinetti M. Bone Marrow Transplant. 46 916-922 (2011)
  4. T-cell receptor (TCR) interaction with peptides that mimic nickel offers insight into nickel contact allergy. Yin L, Crawford F, Marrack P, Kappler JW, Dai S. Proc. Natl. Acad. Sci. U.S.A. 109 18517-18522 (2012)
  5. Reassessing the role of HLA-DRB3 T-cell responses: evidence for significant expression and complementary antigen presentation. Faner R, James E, Huston L, Pujol-Borrel R, Kwok WW, Juan M. Eur. J. Immunol. 40 91-102 (2010)
  6. TCR-contacting residues orientation and HLA-DRβ* binding preference determine long-lasting protective immunity against malaria. Alba MP, Suarez CF, Varela Y, Patarroyo MA, Bermudez A, Patarroyo ME. Biochem. Biophys. Res. Commun. 477 654-660 (2016)
  7. GDP-l-fucose synthase is a CD4+ T cell-specific autoantigen in DRB3*02:02 patients with multiple sclerosis. Planas R, Santos R, Tomas-Ojer P, Cruciani C, Lutterotti A, Faigle W, Schaeren-Wiemers N, Espejo C, Eixarch H, Pinilla C, Martin R, Sospedra M. Sci Transl Med 10 (2018)
  8. Using DR52c/Ni2+ mimotope tetramers to detect Ni2+ reactive CD4+ T cells in patients with joint replacement failure. Zhang Y, Wang Y, Anderson K, Novikov A, Liu Z, Pacheco K, Dai S. Toxicol. Appl. Pharmacol. 331 69-75 (2017)
  9. Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome. Kaabinejadian S, Barra C, Alvarez B, Yari H, Hildebrand WH, Nielsen M. Front Immunol 13 835454 (2022)
  10. Antibodies against HLA-DP recognize broadly expressed epitopes. Simmons DP, Kafetzi ML, Wood I, Macaskill PC, Milford EL, Guleria I. Hum. Immunol. 77 1128-1139 (2016)
  11. Antibody-Dependent Complement Responses toward SARS-CoV-2 Receptor-Binding Domain Immobilized on "Pseudovirus-like" Nanoparticles. Gaikwad H, Li Y, Wang G, Li R, Dai S, Rester C, Kedl R, Saba L, Banda NK, Scheinman RI, Patrick C, Mallela KMG, Moghimi SM, Simberg D. ACS Nano (2022)
  12. The prevalence of antibodies against the HLA-DRB3 protein in kidney transplantation and the correlation with HLA expression. Habets THPM, Hepkema BG, Kouprie N, Schnijderberg MCA, van Smaalen TC, Bungener LB, Christiaans MHL, Bos GMJ, Vanderlocht J. PLoS ONE 13 e0203381 (2018)