ArticleBioinformatics (Oxford, England)2024
RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 14 citations in OpenAlex.
- From Neoantigens to Nanocarriers: Modern Methods and Modalities in Using Peptides for Cancer Vaccination.Biochemistry · 2026Review
- Multimodal learning on heterogeneous subgraphs and LLMs representation for MHC-peptide binding affinity prediction.BMC bioinformatics · 2026Article
- Computational models for the classification of antibody specificity using heavy chain features.PloS one · 2026Article
- DSCA-HLAII: A dual-stream cross-attention model for predicting peptide-HLA class II interaction and presentation.PLoS computational biology · 2026Article
- TCRLens: structure-aware equivariant graph learning for TCR-pMHC-I recognition and immunogenic epitope discovery.Bioinformatics advances · 2026Article
- A common network of residue-residue contacts underlies interactions between peptides and HLA class II complex.PloS one · 2026Article
- The role of neoantigens and tumor mutational burden in cancer immunotherapy: advances, mechanisms, and perspectives.Journal of hematology & oncology · 2025Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- The ExoGAN generative AI framework enables extracellular vesicle-based immunotherapy.Research square · 2025Article
- Identifying T cell antigen at the atomic level with graph convolutional network.Nature communications · 2025Article
- Advances and challenges in neoantigen prediction for cancer immunotherapy.Frontiers in immunology · 2025Review
- Immunity to fungi and vaccine considerations.Cell host & microbe · 2024Review
- GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction.Frontiers in genetics · 2024Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
motivationBinding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two.
resultsIn this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. AVAILABILITY: The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.