ArticleBioinformatics (Oxford, England)2022
DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- PepGen: conditional generation of peptides for MHC binding.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking sequence-based and AlphaFold-based methods for pMHC-II binding core prediction: distinct strengths and consensus approaches.BMC medical genomics · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Rational Immunogenicity Modulation of Virus-Like Particles by VLPIM.Computational and structural biotechnology journal · 2026Article
- Computational design of class II MHC binding peptide with sequence-based evolution information.Bioinformatics advances · 2026Article
- DSCA-HLAII: A dual-stream cross-attention model for predicting peptide-HLA class II interaction and presentation.PLoS computational biology · 2026Article
- Deep learning in next-generation vaccine development for infectious diseases.Molecular therapy. Nucleic acids · 2025Review
- pMHChat, characterizing the interactions between major histocompatibility complex class II molecules and peptides with large language models and deep hypergraph learning.Briefings in bioinformatics · 2025Article
- Framework for analyzing MAE-derived immunopeptidomes from cell lines with shared HLA haplotypes.PloS one · 2025Article
- Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens.Vaccines · 2024Review
- ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.Briefings in bioinformatics · 2024Article
- RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.Bioinformatics (Oxford, England) · 2024Article
- Review
- GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction.Frontiers in genetics · 2024Article
- Therapeutic cancer vaccines: advancements, challenges, and prospects.Signal transduction and targeted therapy · 2023Review
- DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction.Bioinformatics (Oxford, England) · 2023Article
- HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.Immunity · 2023Article
- Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.NPJ precision oncology · 2023Article
- Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationComputationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with a binding interaction convolution layer, which allows to integrate all potential binding cores (in a given peptide) with the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels.
resultsExtensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as 5-fold cross-validation, leave one molecule out, validation with independent testing sets and binding core prediction. All these results and visualization of the predicted binding cores indicate the effectiveness of our model, DeepMHCII, and the importance of properly modeling biological facts in deep learning for high predictive performance and efficient knowledge discovery. AVAILABILITY AND IMPLEMENTATION: DeepMHCII is publicly available at https://github.com/yourh/DeepMHCII. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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