ArticleBMC genomics2024
Structure-aware deep model for MHC-II peptide binding affinity prediction.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dynamic Fourier wavelet positional encoding for anticancer peptide prediction via transfer learning.iScience · 2026Article
- Structural Remodeling of TCR-HLA-DQ8 Recognition by a β-Cell Stress-Associated C19S Insulin Neoepitope in Type 1 Diabetes.International journal of molecular sciences · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- Machine-guided dual-objective protein engineering for deimmunization and therapeutic functions.Cell systems · 2025Article
- OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction.Frontiers in immunology · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.
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Registered trials
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