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HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity.

ABSTRACT: Prediction of HLA binding affinity is widely used to identify candidate T cell epitopes, and an affinity of 500 nM is routinely used as a threshold for peptide selection. However, the fraction (percentage) of peptides predicted to bind with affinities of 500 nM varies by allele. For example, of a large collection of ~30,000 dengue virus-derived peptides only 0.3% were predicted to bind HLA A*0101, whereas nearly 5% were predicted for A*0201. This striking difference could not be ascribed to variation in accuracy of the algorithms used, as predicted values closely correlated with affinity measured in vitro with purified HLA molecules. These data raised the question whether different alleles would also vary in terms of epitope repertoire size, defined as the number of associated epitopes or, alternatively, whether alleles vary drastically in terms of the affinity threshold associated with immunogenicity. To address this issue, strains of HLA transgenic mice with wide (A*0201), intermediate (B*0702), or narrow (A*0101) repertoires were immunized with peptides of varying binding affinity and relative percentile ranking. The results show that absolute binding capacity is a better predictor of immunogenicity, and analysis of epitopes from the Immune Epitope Database revealed that predictive efficacy is increased using allele-specific affinity thresholds. Finally, we investigated the genetic and structural basis of the phenomenon. Although no stringent correlate was defined, on average HLA B alleles are associated with significantly narrower repertoires than are HLA A alleles.

SUBMITTER: Paul S

PROVIDER: S-EPMC3872965 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity.

Paul Sinu S Weiskopf Daniela D Angelo Michael A MA Sidney John J Peters Bjoern B Sette Alessandro A

Journal of immunology (Baltimore, Md. : 1950) 20131104 12

Prediction of HLA binding affinity is widely used to identify candidate T cell epitopes, and an affinity of 500 nM is routinely used as a threshold for peptide selection. However, the fraction (percentage) of peptides predicted to bind with affinities of 500 nM varies by allele. For example, of a large collection of ~30,000 dengue virus-derived peptides only 0.3% were predicted to bind HLA A*0101, whereas nearly 5% were predicted for A*0201. This striking difference could not be ascribed to vari ...[more]

PMID: 24190657

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Project description:Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in developing powerful antitumor vaccine for cancer immunotherapy. Currently available methods work quite well in predicting peptide binding affinity with HLA alleles such as HLA-A*0201, HLA-A*0101, and HLA-B*0702 in terms of sensitivity and specificity. However, quite a few types of HLA alleles that are present in the majority of human populations including HLA-A*0202, HLA-A*0203, HLA-A*6802, HLA-B*5101, HLA-B*5301, HLA-B*5401, and HLA-B*5701 still cannot be predicted with satisfactory accuracy using currently available methods. Furthermore, currently the most popularly used methods for predicting peptide binding affinity are inefficient in identifying neoantigens from a large quantity of whole genome and transcriptome sequencing data. Here we present a Position Specific Scoring Matrix (PSSM)-based software called PSSMHCpan to accurately and efficiently predict peptide binding affinity with a broad coverage of HLA class I alleles. We evaluated the performance of PSSMHCpan by analyzing 10-fold cross-validation on a training database containing 87 HLA alleles and obtained an average area under receiver operating characteristic curve (AUC) of 0.94 and accuracy (ACC) of 0.85. In an independent dataset (Peptide Database of Cancer Immunity) evaluation, PSSMHCpan is substantially better than the popularly used NetMHC-4.0, NetMHCpan-3.0, PickPocket, Nebula, and SMM with a sensitivity of 0.90, as compared to 0.74, 0.81, 0.77, 0.24, and 0.79. In addition, PSSMHCpan is more than 197 times faster than NetMHC-4.0, NetMHCpan-3.0, PickPocket, sNebula, and SMM when predicting neoantigens from 661 263 peptides from a breast tumor sample. Finally, we built a neoantigen prediction pipeline and identified 117 017 neoantigens from 467 cancer samples of various cancers from TCGA. PSSMHCpan is superior to the currently available methods in predicting peptide binding affinity with a broad coverage of HLA class I alleles.

Dataset Information

HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity.

Publications

HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity.

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