A high throughput MHC II binding assay for quantitative analysis of peptide epitopes

Journal of Visualized Experiments

Volumn , Issue 85, 2014, Pages

  Salvat, Regina ^a   Moise, Leonard ^b   Bailey Kellogg, Chris ^c   Griswold, Karl E ^a  

^a DARTMOUTH COLLEGE   (United States)

^b UNIVERSITY OF RHODE ISLAND   (United States)

^c Dartmouth Department of Computer Science_Dartmouth College   (United States)

Author keywords

Biochemistry; Deimmunization; High throughput screen; Immunoassay; Issue 85; MHC II; Protein immunogenicity; T cell epitope; Vaccine design

Indexed keywords

EID: 84897423891     PISSN: 1940087X     EISSN: None     Source Type: Journal    
DOI: 10.3791/51308     Document Type: Article

References (35)

1. Steere, A. C. et al. Antibiotic-refractory Lyme arthritis is associated with HLA-DR molecules that bind a Borrelia burgdorferi peptide. J. Exp. Med. 203, 961-971, (2006). doi:10.1084/Jem.20052471
2. Raddrizzani, L. et al. Different modes of peptide interaction enable HLA-DQ and HLA-DR molecules to bind diverse peptide repertoires. J. Immunol. 159, 703-711 (1997).
3. Osipovitch, D. C. et al. Design and analysis of immune-evading enzymes for ADEPT therapy. Protein Eng. Design Select. 25, 613-623, (2012). doi:10.1093/Protein/Gzs044
4. Moise, L. et al. In silico-accelerated identification of conserved and immunogenic variola/vaccinia T-cell epitopes. Vaccine. 27, 6471-6479, (2009). doi:10.1016/j.vaccine.2009.06.018
5. Moise, L. et al. Effect of HLA DR epitope de-immunization of Factor VIII in vitro and in vivo. Clin. Immunol. 142, 320-331, (2012). doi:10.1016/j.clim.2011.11.010
6. Aggarwal, S. R. What's fueling the biotech engine-2011 to 2012. Nat. Biotechnol. 30, 1191-1197, (2012). doi:10.1038/nbt.2437
7. Trombetta, E. S. & Mellman, I. Cell biology of antigen processing in vitro and in vivo. Ann. Rev. Immunol. 23, 975-1028, (2005). doi:10.1146/annurev.immunol.22.012703.104538
8. Warmerdam, P. A. M. et al. Elimination of a human T-cell region in staphylokinase by T-cell screening and computer modeling. Thrombosis Haemostasis. 87, 666-673 (2002).
9. Jones, T. D. et al. Identification and removal of a promiscuous CD4+T cell epitope from the C1 domain of factor VIII. J. Thrombosis Haemostasis. 3, 991-1000, (2005). doi:10.1111/J.1538-7836.2005.01309.X
10. Harding, F. A. et al. A beta-lactamase with reduced immunogenicity for the targeted delivery of chemotherapeutics using antibody-directed enzyme prodrug therapy. Mol. Cancer Ther. 4, 1791-1800, (2005). doi:10.1158/1535-7163.MCT-05-0189
11. Mazor, R. et al. Identification and elimination of an immunodominant T-cell epitope in recombinant immunotoxins based on Pseudomonas exotoxin A. Proc. Natl. Acad. Sci. U.S.A. 109, E3597-E3603, (2012). doi:10.1073/pnas.1218138109
12. Cantor, J. R. et al. Therapeutic enzyme deimmunization by combinatorial T-cell epitope removal using neutral drift. Proc. Natl. Acad. Sci. U.S.A. 108, 1272-1277, (2011). doi:10.1073/pnas.1014739108
13. Kern, F., LiPira, G., Gratama, J. W., Manca, F. & Roederer, M. Measuring Ag-specific immune responses: understanding immunopathogenesis and improving diagnostics in infectious disease, autoimmunity and cancer. Trends Immunol. 26, 477-484, (2005). doi:10.1016/j.it.2005.07.005
14. Li Pira, G., Ivaldi, F., Moretti, P. & Manca, F. High throughput T epitope mapping and vaccine development. J. Biomed. Biotechnol. 2010, 325720, (2010). doi:10.1155/2010/325720
15. Hoffmeister, B. et al. Mapping T cell epitopes by flow cytometry. Methods. 29, 270-281 (2003).
16. Singh, H. & Raghava, G. P. ProPred: prediction of HLA-DR binding sites. Bioinformatics. 17, 1236-1237 (2001).
17. Guan, P., Doytchinova, I. A., Zygouri, C. & Flower, D. R. MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. Appl. Bioinformatics. 2, 63-66 (2003).
18. Wan, J. et al. SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics. 7, 463, (2006). doi:10.1186/1471-2105-7-463
19. Bui, H. H. et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics. 57, 304-314, (2005). doi:10.1007/s00251-005-0798-y
20. Nielsen, M., Lundegaard, C. & Lund, O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics. 8, 238, (2007). doi:10.1186/1471-2105-8-238
21. Nielsen, M., Justesen, S., Lund, O., Lundegaard, C. & Buus, S. NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure. Immun. Res. 6, 9, (2010). doi:10.1186/1745-7580-6-9
22. De Groot, A. S., Knopp, P. M. & Martin, W. De-immunization of therapeutic proteins by T-cell epitope modification. Dev. Biol. 122, 171-194 (2005).
23. Choi, Y., Griswold, K. E. & Bailey-Kellogg, C. Structure-based redesign of proteins for minimal T-cell epitope content. J. Comput. Chem. 34, 879-891, (2013). doi:10.1002/Jcc.23213
24. Parker, A. S., Choi, Y., Griswold, K. E. & Bailey-Kellogg, C. Structure-guided deimmunization of therapeutic proteins. J. Comput. Biol. 20, 152-165, (2013). doi:10.1089/cmb.2012.0251
25. Parker, A. S., Griswold, K. E. & Bailey-Kellogg, C. Optimization of therapeutic proteins to delete T-cell epitopes while maintaining beneficial residue interactions. J. Bioinform. Comput. Biol. 9, 207-229 (2011).
26. Parker, A. S., Zheng, W., Griswold, K. E. & Bailey-Kellogg, C. Optimization algorithms for functional deimmunization of therapeutic proteins. BMC Bioinformatics. 11, 180, (2010). doi:10.1186/1471-2105-11-180
27. De Groot, A. S. & Martin, W. Reducing risk, improving outcomes: Bioengineering less immunogenic protein therapeutics. Clin. Immunol. 131, 189-201, (2009). doi:10.1016/J.Clim.2009.01.009
28. Wang, P. et al. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput. Biol. 4, e1000048, (2008). doi:10.1371/journal.pcbi.1000048
29. Sette, A. & Rappuoli, R. Reverse vaccinology: developing vaccines in the era of genomics. Immunity. 33, 530-541, (2010). doi:10.1016/j.immuni.2010.09.017
30. Moise, L., Cousens, L., Fueyo, J. & De Groot, A. S. Harnessing the power of genomics and immunoinformatics to produce improved vaccines. Expert Opin. Drug Discov. 6, 9-15, (2011). doi:10.1517/17460441.2011.534454
31. Sturniolo, T. et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat. Biotechnol. 17, 555-561 (1999).
32. Biologic drugs set to top 2012 sales. Nat. Med. 18, 636, doi:10.1038/nm0512-636a (2012).
33. Seib, K. L., Zhao, X. & Rappuoli, R. Developing vaccines in the era of genomics: a decade of reverse vaccinology. Clin. Microbiol. Infect. 18, 109-116, (2012). doi: 10.1111/J.1469-0691.2012.03939.X
34. Jiang, W. & Boder, E. T. High-throughput engineering and analysis of peptide binding to class II MHC. Proc. Natl. Acad. Sci. U.S.A. 107, 13258-13263, (2010). doi:10.1073/pnas.1006344107
35. Justesen, S., Harndahl, M., Lamberth, K., Nielsen, L.-L. B. & Buus, S. Functional recombinant MHC class II molecules and high-throughput peptide-binding assays. Immun. Res. 5, 1-20, (2009) doi:10.1186/1745-7580-5-2