Dan He
ABSTRACT
Current HIV vaccines are designed to elicit both T-cell and B-cell responses. A common endpoint in any T-cell based vaccine trial are measurements of vaccine-induced T-cell responses such as breadth and magnitude [7]. In order to measure such endpoints blood samples are collected at multiple timepoints. Current immunological assays for measuring T-cell responses are functional assays in which peripheral blood mononuclear cells (PBMC) is incubated with target peptide(s) and then the release of various cytokines such as IFN-γ are measured. The major limiting factor in these mappping studies is sample availability, as each of these tests requires an order of 100K live cells. Therefore current mapping strategies use a group-testing approach in which responses to the immunogen are first measured using peptide pools that span a full protein, and are then further refined using sets of mini-pool and finally a peptide matrix [23].
In this paper we explore the idea of using HLA binding predictors to improve the efficiency of epitope mapping protocols in vaccine trials. Given information about participant's HLA alleles, we attempt to predict vaccine induced T-cell responses at various levels of refinement, based on the current group-testing hierarchical mapping approach. Using extensive epitope mapping data from a cohort of 12 acutely infected HIV infected individuals, we show that using state-of-the-art HLA binding predictors, significant improvements in mapping efficiency can be obtained.
- Y. Altuvia and H. Margalit. A structure-based approach for prediction of MHC-binding peptides. Methods, 34:454--459, Dec 2004.Google Scholar
Cross Ref
- E. Assarsson, H.-H. Bui, J. Sidney, Q. Zhang, J. Glenn, C. Oseroff, I. N. Mbawuike, J. Alexander, M. J. Newman, H. Grey, and A. Sette. Immunomic analysis of the repertoire of T-cell specificities for influenza A virus in humans. Journal of Virology, 82(24):12241--51, Dec 2008.Google ScholarCross Ref
- V. Brusic, N. Petrovsky, G. Zhang, and V. B. Bajic. Prediction of promiscuous peptides that bind HLA class I molecules. Immunol. Cell Biol., 80:280--285, Jun 2002.Google ScholarCross Ref
- V. Brusic, G. Rudy, and L. C. Harrison. Prediction of MHC binding peptides using artificial neural networks. Complexity International, 2, april 1995.Google Scholar
- S. Buus, S. Lauemoller, P. Worning, C. Kesmir, T. Frimurer, S. Corbet, A. Fomsgaard, J. Hilden, A. Holm, and S. Brunak. Sensitive quantitative predictions of peptide-MHC binding by a 'query by committee' artificial neural network approach. Tissue Antigens, 62(5):378--384, 2003.Google ScholarCross Ref
- P. Dãűnnes and A. Elofsson. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics, 3:25, Sep 2002.Google ScholarCross Ref
- P. B. Gilbert, I. W. McKeague, and Y. Sun. The 2-sample problem for failure rates depending on a continuous mark: an application to vaccine efficacy. Biostatistics (Oxford, England), 9(2):263--76, Apr 2008.Google Scholar
- P. B. Gilbert, A. Sato, X. Sun, and D. V. Mehrotra. Efficient and robust method for comparing the immunogenicity of candidate vaccines in randomized clinical trials. Vaccine, 27(3):396--401, Jan 2009.Google ScholarCross Ref
- P. Guan, I. A. Doytchinova, C. Zygouri, and D. R. Flower. MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. Appl. Bioinformatics, 2:63--66, 2003.Google Scholar
- K. Gulukota, J. Sidney, A. Sette, and C. DeLisi. Two complementary methods for predicting peptides binding major histocompatibility complex molecules. Journal of Molecular Biology, 267:1258--1267, 1996.Google ScholarCross Ref
- D. Heckerman, C. Kadie, and J. Listgarten. Leveraging information across HLA alleles/supertypes improves epitope prediction. J Comput Biol, 14(6):736--46, Jan 2007.Google ScholarCross Ref
- T. Hertz, D. Nolan, I. James, M. John, S. Gaudieri, E. Phillips, J. C. Huang, G. Riadi, S. Mallal, and N. Jojic. Mapping the landscape of host-pathogen coevolution: Hla class i binding and its relationship with evolutionary conservation in human and viral proteins. Journal of Virology, 85(3):1310--21, Feb 2011.Google ScholarCross Ref
- T. Hertz and C. Yanover. Pepdist: A new framework for protein-peptide binding prediction based on learning peptide distance functions. BMC Bioinformatics, 7(Suppl 1):S3, Jan 2006.Google ScholarCross Ref
- I. Hoof, B. Peters, J. Sidney, L. E. Pedersen, A. Sette, O. Lund, S. Buus, and M. Nielsen. Netmhcbpan, a method for MHC class I binding prediction beyond humans. Immunogenetics, 61(1):1--13, Jan 2009.Google ScholarCross Ref
- A. L. Hughes. Looking for darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level. Heredity, 99(4):364--73, Oct 2007.Google ScholarCross Ref
- A. L. Hughes and M. Nei. Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection. Nature, 335(6186):167--70, Sep 1988.Google ScholarCross Ref
- A. L. Hughes, B. Packer, R. Welch, S. J. Chanock, and M. Yeager. High level of functional polymorphism indicates a unique role of natural selection at human immune system loci. Immunogenetics, 57(11):821--7, Dec 2005.Google ScholarCross Ref
- N. Jojic, M. Reyes-Gomez, D. Heckerman, C. Kadie, and O. Schueler-Furman. Learning MHC I--peptide binding. Bioinformatics, 22(14):e227--35, Jul 2006. Google ScholarDigital Library
- F. Li, U. Malhotra, P. B. Gilbert, N. R. Hawkins, A. C. Duerr, J. M. McElrath, L. Corey, and S. G. Self. Peptide selection for human immunodeficiency virus type 1 CTL-based vaccine evaluation. Vaccine, 24:6893--6904, Nov 2006.Google ScholarCross Ref
- H. Lin, S. Ray, S. Tongchusak, E. L. Reinherz, and V. Brusic. Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research. BMC Immunol, 9(1):8, Jan 2008.Google ScholarCross Ref
- A. Llano, N. Frham, and C. Brander. How to optimally define optimal cytotoxic t lymphocyte epitopes in hiv infection? HIV Molecular Immunology, pages I--3--5, 2009.Google Scholar
- H. Mamitsuka. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins, 33(4):460--474, 1998.Google ScholarCross Ref
- M. J. McElrath, S. C. D. Rosa, Z. Moodie, S. Dubey, L. Kierstead, H. Janes, O. D. Defawe, D. K. Carter, J. Hural, R. Akondy, S. P. Buchbinder, M. N. Robertson, D. V. Mehrotra, S. G. Self, L. Corey, J. W. Shiver, D. R. Casimiro, and S. S. P. Team. Hiv-1 vaccine-induced immunity in the test-of-concept step study: a case-cohort analysis. Lancet, 372(9653):1894--905, Nov 2008.Google ScholarCross Ref
- D. Meyer, R. M. Single, S. J. Mack, H. A. Erlich, and G. Thomson. Signatures of demographic history and natural selection in the human major histocompatibility complex loci. Genetics, 173(4):2121--42, Aug 2006.Google ScholarCross Ref
- M. Moutaftsi, B. Peters, V. Pasquetto, D. C. Tscharke, J. Sidney, H.-H. Bui, H. Grey, and A. Sette. A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol, 24(7):817--9, Jul 2006.Google ScholarCross Ref
- M. Nielsen, C. Lundegaard, P. Worning, S. L. LauemÃÿller, K. Lamberth, S. Buus, S. Brunak, and O. Lund. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci., 12:1007--1017, May 2003.Google ScholarCross Ref
- B. Peters, H.-H. Bui, S. Frankild, M. Nielson, C. Lundegaard, E. Kostem, D. Basch, K. Lamberth, M. Harndahl, W. Fleri, S. S. Wilson, J. Sidney, O. Lund, S. Buus, and A. Sette. A community resource benchmarking predictions of peptide binding to MHC-i molecules. PLoS Computational Biology, 2(6):e65, Jun 2006.Google ScholarCross Ref
- B. Peters and A. Sette. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics, 6:132, 2005.Google ScholarCross Ref
- B. Peters and A. Sette. Integrating epitope data into the emerging web of biomedical knowledge resources. Nature Reviews Immunology, 7(6):485--90, Jun 2007.Google ScholarCross Ref
- B. Peters, J. Sidney, P. Bourne, H.-H. Bui, S. Buus, G. Doh, W. Fleri, M. Kronenberg, R. Kubo, O. Lund, D. Nemazee, J. V. Ponomarenko, M. Sathiamurthy, S. P. Schoenberger, S. Stewart, P. Surko, S. Way, S. Wilson, and A. Sette. The design and implementation of the immune epitope database and analysis resource. Immunogenetics, 57(5):326--336, Jun 2005.Google ScholarCross Ref
- F. Prugnolle, A. Manica, M. Charpentier, J. F. Guégan, V. Guernier, and F. Balloux. Pathogen-driven selection and worldwide HLA class I diversity. Curr Biol, 15(11):1022--7, Jun 2005.Google ScholarCross Ref
- P. A. Reche, J. P. Glutting, H. Zhang, and E. L. Reinher. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics, 26(6):405--419, 2004.Google Scholar
- A. B. Riemer, D. B. Keskin, G. Zhang, M. Handley, K. S. Anderson, V. Brusic, B. Reinhold, and E. L. Reinherz. A conserved e7-derived cytotoxic t lymphocyte epitope expressed on human papillomavirus 16-transformed hla-a2+ epithelial cancers. J Biol Chem, 285(38):29608--22, Sep 2010.Google ScholarCross Ref
- O. Schueler-Furman, Y. Altuvia, A. Sette, and H. Margalit. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci, 9(9):1838--1846, 2000.Google ScholarCross Ref
- N. Takahata and M. Nei. Allelic genealogy under overdominant and frequency-dependent selection and polymorphism of major histocompatibility complex loci. Genetics, 124(4):967--78, Apr 1990.Google Scholar
- N. Zaitlen, M. Reyes-Gomez, D. Heckerman, and N. Jojic. Shift-invariant adaptive double threading: learning MHC II-peptide binding. J Comput Biol, 15(7):927--42, Sep 2008.Google ScholarCross Ref
- G. L. Zhang, A. M. Khan, K. N. Srinivasan, J. T. August, and V. Brusic. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res., 33:W172--179, Jul 2005.Google ScholarCross Ref
- H. Zhang, C. Lundegaard, and M. Nielsen. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics, 25(1):83--9, Jan 2009. Google ScholarDigital Library
Index Terms
- Using HLA binding prediction algorithms for epitope mapping in HIV vaccine clinical trials
Recommendations
Interpreting Negative Data on Antipeptide Paratope Binding to Support Development of B-Cell Epitope Prediction for Vaccine Design and Other Translational Applications
BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health InformaticsThe design of synthetic vaccine peptides and other constructs (e.g., for developing immunodiagnostics) is informed by B-cell epitope prediction for antipeptide paratopes, which crucially depends on physicochemically and biologically meaningful ...
Quantitative Prediction of Peptide Binding to HLA-DP1 Protein
The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The ...
Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design
The paper proposes a new approach using a Genetic Algorithm for Predicting the Epitope Structure (GAPES).The proposed tertiary structure prediction of epitopes in GAPES is based on Ab-initio Empirical Conformational Energy Program for Peptides (ECEPP) ...
Comments