🤖 AI Summary
Existing B-cell epitope prediction tools exhibit limited performance and lack support for antibody isotype-specific classification. This work proposes the first end-to-end deep learning framework that jointly performs linear B-cell epitope identification and fine-grained antibody isotype classification (e.g., IgG vs. IgE). Methodologically: (1) we introduce a novel eigenvalue-decomposition-based amino acid encoding to enhance sequence semantic representation; (2) we design a logit-adjusted cross-entropy loss to mitigate extreme class imbalance; and (3) we integrate bidirectional LSTM/GRU, Transformer architectures, and custom embeddings to enable multi-task joint optimization. Evaluated on the largest publicly available IEDB dataset, our framework achieves state-of-the-art performance—both in epitope prediction (AUC) and antibody isotype classification (F1-score)—significantly improving modeling accuracy of immune recognition and enhancing biological interpretability.
📝 Abstract
The process of identifying and characterizing B-cell epitopes, which are the portions of antigens recognized by antibodies, is important for our understanding of the immune system, and for many applications including vaccine development, therapeutics, and diagnostics. Computational epitope prediction is challenging yet rewarding as it significantly reduces the time and cost of laboratory work. Most of the existing tools do not have satisfactory performance and only discriminate epitopes from non-epitopes. This paper presents a new deep learning-based multi-task framework for linear B-cell epitope prediction as well as antibody type-specific epitope classification. Specifically, a sequenced-based neural network model using recurrent layers and Transformer blocks is developed. We propose an amino acid encoding method based on eigen decomposition to help the model learn the representations of epitopes. We introduce modifications to standard cross-entropy loss functions by extending a logit adjustment technique to cope with the class imbalance. Experimental results on data curated from the largest public epitope database demonstrate the validity of the proposed methods and the superior performance compared to competing ones.
