Dataset Information

High-Throughput Identification of MHC Class I Binding Peptides Using an Ultradense Peptide Array.

ABSTRACT: Rational vaccine development and evaluation requires identifying and measuring the magnitude of epitope-specific CD8 T cell responses. However, conventional CD8 T cell epitope discovery methods are labor intensive and do not scale well. In this study, we accelerate this process by using an ultradense peptide array as a high-throughput tool for screening peptides to identify putative novel epitopes. In a single experiment, we directly assess the binding of four common Indian rhesus macaque MHC class I molecules (Mamu-A1*001, -A1*002, -B*008, and -B*017) to ?61,000 8-mer, 9-mer, and 10-mer peptides derived from the full proteomes of 82 SIV and simian HIV isolates. Many epitope-specific CD8 T cell responses restricted by these four MHC molecules have already been identified in SIVmac239, providing an ideal dataset for validating the array; up to 64% of these known epitopes are found in the top 192 SIVmac239 peptides with the most intense MHC binding signals in our experiment. To assess whether the peptide array identified putative novel CD8 T cell epitopes, we validated the method by IFN-? ELISPOT assay and found three novel peptides that induced CD8 T cell responses in at least two Mamu-A1*001-positive animals; two of these were validated by ex vivo tetramer staining. This high-throughput identification of peptides that bind class I MHC will enable more efficient CD8 T cell response profiling for vaccine development, particularly for pathogens with complex proteomes for which few epitope-specific responses have been defined.

SUBMITTER: Haj AK

PROVIDER: S-EPMC7065948 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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High-Throughput Identification of MHC Class I Binding Peptides Using an Ultradense Peptide Array.

Haj Amelia K AK Breitbach Meghan E ME Baker David A DA Mohns Mariel S MS Moreno Gage K GK Wilson Nancy A NA Lyamichev Victor V Patel Jigar J Weisgrau Kim L KL Dudley Dawn M DM O'Connor David H DH

Journal of immunology (Baltimore, Md. : 1950) 20200214 6

Rational vaccine development and evaluation requires identifying and measuring the magnitude of epitope-specific CD8 T cell responses. However, conventional CD8 T cell epitope discovery methods are labor intensive and do not scale well. In this study, we accelerate this process by using an ultradense peptide array as a high-throughput tool for screening peptides to identify putative novel epitopes. In a single experiment, we directly assess the binding of four common Indian rhesus macaque MHC cl ...[more]

PMID: 32060132

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Project description:BACKGROUND:Finding peptides with high binding affinity to Class I major histocompatibility complex (MHC-I) attracts intensive research, and it serves a crucial part of developing a better vaccine for precision medicine. Traditional methods cost highly for designing such peptides. The advancement of computational approaches reduces the cost of new drug discovery dramatically. Compared with flourishing computational drug discovery area, the immunology area lacks tools focused on in silico design for the peptides with high binding affinity. Attributed to the ever-expanding amount of MHC-peptides binding data, it enables the tremendous influx of deep learning techniques for modeling MHC-peptides binding. To leverage the availability of these data, it is of great significance to find MHC-peptides binding specificities. The binding motifs are one of the key components to decide the MHC-peptides combination, which generally refer to a combination of some certain amino acids at certain sites which highly contribute to the binding affinity. RESULT:In this work, we propose the Motif Activation Mapping (MAM) network for MHC-I and peptides binding to extract motifs from peptides. Then, we substitute amino acid randomly according to the motifs for generating peptides with high affinity. We demonstrated the MAM network could extract motifs which are the features of peptides of highly binding affinities, as well as generate peptides with high-affinities; that is, 0.859 for HLA-A*0201, 0.75 for HLA-A*0206, 0.92 for HLA-B*2702, 0.9 for HLA-A*6802 and 0.839 for Mamu-A1*001:01. Besides, its binding prediction result reaches the state of the art. The experiment also reveals the network is appropriate for most MHC-I with transfer learning. CONCLUSIONS:We design the MAM network to extract the motifs from MHC-peptides binding through prediction, which are proved to generate the peptides with high binding affinity successfully. The new peptides preserve the motifs but vary in sequences.

Dataset Information

High-Throughput Identification of MHC Class I Binding Peptides Using an Ultradense Peptide Array.

Publications

High-Throughput Identification of MHC Class I Binding Peptides Using an Ultradense Peptide Array.

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