HLABrew for Human Leukocyte Antigen Class I-Presented Epitope Recognition and Mimotope Discovery

Accurate identification of human leukocyte antigen (HLA) class I-presented epitopes is essential for developing personalized vaccines and immunotherapies. Here, we present HLABrew, a unified deep learning framework for HLA class I epitope prediction and allele-specific mimotope design. By integrating the Transformer, variational autoencoder, and dual cross-attention, HLABrew achieves state-of-the-art performance with hierarchical and allele-aware representations. HLABrew accurately deconvolves multiallelic immunopeptidomics data to recover allele-specific binding motifs and expand epitope coverage. Leveraging its generative capacity, HLABrew further designs mimotopes guided by learned peptide-HLA binding preferences, with the potential to enhance antigen presentation and provide candidates for downstream immunogenicity evaluation.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-04-30
DOI
https://doi.org/10.1021/acs.jcim.6c00337
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
0.00

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article

HLABrew for Human Leukocyte Antigen Class I-Presented Epitope Recognition and Mimotope Discovery

L Zhang, Jiani Ma, Yumeng Zhang, Jiangning Song et al.
Journal of Chemical Information and Modeling
vaccines and immunoinformatics approaches
article

HLABrew for Human Leukocyte Antigen Class I-Presented Epitope Recognition and Mimotope Discovery

L Zhang, Jiani Ma, Yumeng Zhang, Jiangning Song, Hui Liu
article en

Abstract

Accurate identification of human leukocyte antigen (HLA) class I-presented epitopes is essential for developing personalized vaccines and immunotherapies. Here, we present HLABrew, a unified deep learning framework for HLA class I epitope prediction and allele-specific mimotope design. By integrating the Transformer, variational autoencoder, and dual cross-attention, HLABrew achieves state-of-the-art performance with hierarchical and allele-aware representations. HLABrew accurately deconvolves multiallelic immunopeptidomics data to recover allele-specific binding motifs and expand epitope coverage. Leveraging its generative capacity, HLABrew further designs mimotopes guided by learned peptide-HLA binding preferences, with the potential to enhance antigen presentation and provide candidates for downstream immunogenicity evaluation.

Journal of Chemical Information and ModelingVol. 66(9)
China University of Mining and Technology (CN), Discovery Institute (US), Australian Regenerative Medicine Institute (AU), Monash University (AU), Monash Institute of Medical Research (AU)
Monash University, China Postdoctoral Science Foundation, Australian Research Council, National Health and Medical Research Council
Openalex Percentile: Top 16%
vaccines and immunoinformatics approaches
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