Characterization of the peptide binding specificity of the HLA class I alleles B*38:01 and B*39:06

Sidney, John; Schloss, Jennifer; Moore, Carrie; Lindvall, Mikaela; Wriston, Amanda; Hunt, Donald F.; Shabanowitz, Jeffrey; DiLorenzo, Teresa P.; Sette, Alessandro

doi:10.1007/s00251-015-0898-2

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…Our finding that T1D‐predisposing subtype B*39:06 preferred small hydrophobic residues, while B*39:01 and B*38:01 had larger hydrophobic residues at PΩ is consistent with previous findings [15, 16, 34]. Here, we have also focused on identifying the underlying molecular mechanisms that result in this difference in F‐pocket preference as well as in differential association with T1D.…”

Section: Discussionsupporting

confidence: 91%

Polymorphism in F pocket affects peptide selection and stability of type 1 diabetes‐associated HLA‐B39 allotypes

Amarajeewa,

Özcan,

Mukhtiar

et al. 2024

Eur J Immunol

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HLA‐B*39:06, HLA‐B*39:01, and HLA‐B*38:01 are closely related HLA allotypes differentially associated with type 1 diabetes (T1D) risk and progression. B*39:06 is highly predisposing, while B*39:01 and B*38:01 are weakly predisposing and protective allotypes, respectively. Here, we aimed to decipher molecular mechanisms underlying the differential association of these allotypes with T1D pathogenesis. We addressed peptide binding and conformational stability of HLA‐B allotypes using computational and experimental approaches. Computationally, we found that B*39:06 and B*39:01 allotypes had more rigid peptide‐binding grooves and were more promiscuous in binding peptides than B*38:01. Peptidomes of B*39:06 and B*39:01 contained fewer strong binders and were of lower affinity than that of B*38:01. Experimentally, we demonstrated that B*39:06 and B*39:01 had a higher capacity to bind peptides and exit to the cell surface but lower surface levels and were degraded faster than B*38:01. In summary, we propose that promiscuous B*39:06 and B*39:01 may bind suboptimal peptides and transport them the cell surface, where such unstable complexes may contribute to the pathogenesis of T1D.

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Section: Discussionsupporting

confidence: 91%

Polymorphism in F pocket affects peptide selection and stability of type 1 diabetes‐associated HLA‐B39 allotypes

Amarajeewa,

Özcan,

Mukhtiar

et al. 2024

Eur J Immunol

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“…This is in a proven practical manner in vaccine development. 19,37,[42][43][44][45][46][47][48][49][50][51][52] The issues of genetic polymorphism, as well as potential harmful constituents, are amplified in full-length natural proteins (Tables 1-4; Fig. 1, S1-S5).…”

Section: Discussionmentioning

confidence: 99%

Protein nanovaccine confers robust immunity against Toxoplasma

Bissati

Zhou

Paulillo³

et al. 2017

npj Vaccines

Self Cite

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We designed and produced a self-assembling protein nanoparticle. This self-assembling protein nanoparticle contains five CD8+ HLA-A03-11 supertypes-restricted epitopes from antigens expressed during Toxoplasma gondii’s lifecycle, the universal CD4+ T cell epitope PADRE, and flagellin as a scaffold and TLR5 agonist. These CD8+ T cell epitopes were separated by N/KAAA spacers and optimized for proteasomal cleavage. Self-assembling protein nanoparticle adjuvanted with TLR4 ligand-emulsion GLA-SE were evaluated for their efficacy in inducing IFN-γ responses and protection of HLA-A*1101 transgenic mice against T. gondii. Immunization, using self-assembling protein nanoparticle-GLA-SE, activated CD8+ T cells to produce IFN-γ. Self-assembling protein nanoparticle-GLA-SE also protected HLA-A*1101 transgenic mice against subsequent challenge with Type II parasites. Hence, combining CD8+ T cell-eliciting peptides and PADRE into a multi-epitope protein that forms a nanoparticle, administered with GLA-SE, leads to efficient presentation by major histocompatibility complex Class I and II molecules. Furthermore, these results suggest that activation of TLR4 and TLR5 could be useful for development of vaccines that elicit T cells to prevent toxoplasmosis in humans.

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“…In addition, two independent sets of binding affinity measurement for 9-mer peptides to HLA-B2705 and B3801alleles were also identified from literature and added to the MHC I cohort. [ 38 , 39 ] These steps resulted in two test datasets including 32 and 24 unique HLA type I and II alleles with 2827 and 15691 binding affinity values, respectively (Tables 2 and S1 ). MHC class I testing data are more heavily distributed in strong binder (IC50 < 50 nM) and non-binder (IC50 > 500 nM) regimes, while MHC class II are heavily distributed in non-binder (IC50 > 1000 nM) regime.…”

Section: Methodsmentioning

confidence: 99%

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes

2018

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A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.

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Characterization of the peptide binding specificity of the HLA class I alleles B38:01 and B39:06

Cited by 7 publications

References 38 publications

Polymorphism in F pocket affects peptide selection and stability of type 1 diabetes‐associated HLA‐B39 allotypes

Polymorphism in F pocket affects peptide selection and stability of type 1 diabetes‐associated HLA‐B39 allotypes

Protein nanovaccine confers robust immunity against Toxoplasma

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes

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