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.
Authors
- L Zhang
- Jiani Ma (ORCID: https://orcid.org/0000-0002-8126-0431)
- Yumeng Zhang
- Jiangning Song
- Hui Liu
Institutions
- China University of Mining and Technology (CN)
- Discovery Institute (US)
- Australian Regenerative Medicine Institute (AU)
- Monash University (AU)
- Monash Institute of Medical Research (AU)
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
Funders
- Monash University
- China Postdoctoral Science Foundation
- Australian Research Council
- National Health and Medical Research Council