MetaMHC: a meta approach to predict peptides binding to MHC molecules

Hu, Xihao; Zhou, Wenjian; Udaka, Keiko; Mamitsuka, Hiroshi; Zhu, Shanfeng

doi:10.1093/nar/gkq407

Cited by 18 publications

(11 citation statements)

References 28 publications

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“…In this investigation, we have identified immunogenic T cell determinants using a library of overlapping peptides encompassing the entire sequence of AQP4. While this approach may be considered labor-intensive, several different in silico methods have recently been developed in order to efficiently identify potential MHC-restricted T cell determinants [26] , [27] , [28] , [29] . Many of these programs base predictions upon existing quantitative peptide binding affinities for specific MHC molecules [26] , [27] , [28] , [29] .…”

Section: Resultsmentioning

confidence: 99%

Immunodominant T Cell Determinants of Aquaporin-4, the Autoantigen Associated with Neuromyelitis Optica

et al. 2010

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Autoantibodies that target the water channel aquaporin-4 (AQP4) in neuromyelitis optica (NMO) are IgG1, a T cell-dependent Ig subclass. However, a role for AQP4-specific T cells in this CNS inflammatory disease is not known. To evaluate their potential role in CNS autoimmunity, we have identified and characterized T cells that respond to AQP4 in C57BL/6 and SJL/J mice, two strains that are commonly studied in models of CNS inflammatory diseases. Mice were immunized with either overlapping peptides or intact hAQP4 protein encompassing the entire 323 amino acid sequence. T cell determinants identified from examination of the AQP4 peptide (p) library were located within AQP4 p21-40, p91-110, p101-120, p166-180, p231-250 and p261-280 in C57BL/6 mice, and within p11-30, p21-40, p101-120, p126-140 and p261-280 in SJL/J mice. AQP4-specific T cells were CD4+ and MHC II-restricted. In recall responses to immunization with intact AQP4, T cells responded primarily to p21-40, indicating this region contains the immunodominant T cell epitope(s) for both strains. AQP4 p21-40-primed T cells secreted both IFN-γ and IL-17. The core immunodominant AQP4 21-40 T cell determinant was mapped to residues 24-35 in C57BL/6 mice and 23-35 in SJL/J mice. Our identification of the AQP4 T cell determinants and characterization of its immunodominant determinant should permit investigators to evaluate the role of AQP4-specific T cells in vivo and to develop AQP4-targeted murine NMO models.

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Section: Resultsmentioning

confidence: 99%

Immunodominant T Cell Determinants of Aquaporin-4, the Autoantigen Associated with Neuromyelitis Optica

et al. 2010

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“…The T cell epitopes were predicted using MetaMHC, a new web server which integrates the outputs of leading predictors by several popular ensemble strategies [63]. This was shown to generate statistically significant results that were more reliable than the individual predictors [63]. For the CD4 + T cell epitope prediction, F1 protein sequence was screened against 14 human MHC-II alleles.…”

Section: Methodsmentioning

confidence: 99%

Mutated and Bacteriophage T4 Nanoparticle Arrayed F1-V Immunogens from Yersinia pestis as Next Generation Plague Vaccines

et al. 2013

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Pneumonic plague is a highly virulent infectious disease with 100% mortality rate, and its causative organism Yersinia pestis poses a serious threat for deliberate use as a bioterror agent. Currently, there is no FDA approved vaccine against plague. The polymeric bacterial capsular protein F1, a key component of the currently tested bivalent subunit vaccine consisting, in addition, of low calcium response V antigen, has high propensity to aggregate, thus affecting its purification and vaccine efficacy. We used two basic approaches, structure-based immunogen design and phage T4 nanoparticle delivery, to construct new plague vaccines that provided complete protection against pneumonic plague. The NH2-terminal β-strand of F1 was transplanted to the COOH-terminus and the sequence flanking the β-strand was duplicated to eliminate polymerization but to retain the T cell epitopes. The mutated F1 was fused to the V antigen, a key virulence factor that forms the tip of the type three secretion system (T3SS). The F1mut-V protein showed a dramatic switch in solubility, producing a completely soluble monomer. The F1mut-V was then arrayed on phage T4 nanoparticle via the small outer capsid protein, Soc. The F1mut-V monomer was robustly immunogenic and the T4-decorated F1mut-V without any adjuvant induced balanced TH1 and TH2 responses in mice. Inclusion of an oligomerization-deficient YscF, another component of the T3SS, showed a slight enhancement in the potency of F1-V vaccine, while deletion of the putative immunomodulatory sequence of the V antigen did not improve the vaccine efficacy. Both the soluble (purified F1mut-V mixed with alhydrogel) and T4 decorated F1mut-V (no adjuvant) provided 100% protection to mice and rats against pneumonic plague evoked by high doses of Y. pestis CO92. These novel platforms might lead to efficacious and easily manufacturable next generation plague vaccines.

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“…“Benchmark” studies, that assess the accuracy of the predictions typically measured the ability of the algorithms to predict binding to MHC class II molecules, using existing or new data sets (25, 29, 32–34, 49–51). Evaluation of peptide performance using functional tests of the actual immunogenicity of the predicted peptides have been much more limited.…”

Section: Introductionmentioning

confidence: 99%

The Utility and Limitations of Current Web-Available Algorithms To Predict Peptides Recognized by CD4 T Cells in Response to Pathogen Infection

Chaves

Lee

Nayak

et al. 2012

The Journal of Immunology

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The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding the success of these algorithms in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple Web-available algorithms by comparing their predictions with our results—obtained by purely empirical methods for epitope discovery in influenza that used overlapping peptides and cytokine ELISPOTs—for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false-positive and false-negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC50 values and true dissociation rates of peptide–MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to “train” the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules.

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MetaMHC: a meta approach to predict peptides binding to MHC molecules

Cited by 18 publications

References 28 publications

Immunodominant T Cell Determinants of Aquaporin-4, the Autoantigen Associated with Neuromyelitis Optica

Immunodominant T Cell Determinants of Aquaporin-4, the Autoantigen Associated with Neuromyelitis Optica

Mutated and Bacteriophage T4 Nanoparticle Arrayed F1-V Immunogens from Yersinia pestis as Next Generation Plague Vaccines

The Utility and Limitations of Current Web-Available Algorithms To Predict Peptides Recognized by CD4 T Cells in Response to Pathogen Infection

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