BeeTLe: An Imbalance-Aware Deep Sequence Model for Linear B-Cell Epitope Prediction and Classification with Logit-Adjusted Losses

TL;DR

This paper introduces BeeTLe, a deep learning framework that improves linear B-cell epitope prediction and classification by addressing class imbalance with logit-adjusted losses and utilizing advanced neural network architectures.

Contribution

The paper presents a novel multi-task deep learning model with eigen decomposition encoding and logit-adjusted loss functions for better epitope prediction and classification.

Findings

Outperforms existing methods on large public datasets.

Effectively handles class imbalance in epitope data.

Achieves higher accuracy in both prediction and classification tasks.

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.