ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis

Haodong Xu (), Ruifeng Hu, Xianjun Dong, Lan Kuang, Wenchao Zhang, Chao Tu, Zhihong Li () and Zhongming Zhao ()
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Haodong Xu: Central South University
Ruifeng Hu: The University of Texas Health Science Center at Houston
Xianjun Dong: Harvard Medical School
Lan Kuang: Central South University
Wenchao Zhang: Central South University
Chao Tu: Central South University
Zhihong Li: Central South University
Zhongming Zhao: The University of Texas Health Science Center at Houston

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

Date: 2024
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DOI: 10.1038/s41467-024-53296-0

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