BConformeR: A Conformer Based on Mutual Sampling for Unified Prediction of Continuous and Discontinuous Antibody Binding Sites

TL;DR

BConformeR is a deep learning model that combines CNNs and Transformers to improve the prediction of both linear and conformational antibody-binding sites on antigens, aiding vaccine and therapeutic development.

Contribution

The paper introduces a novel Conformer-based model trained on structural data, effectively predicting both types of epitopes with superior accuracy over existing methods.

Findings

CNN improves linear epitope prediction.

Transformer enhances conformational epitope prediction.

Model outperforms baselines in MCC, ROC-AUC, PR-AUC, and F1 scores.

Abstract

Accurate prediction of antibody-binding sites (epitopes) on antigens is crucial for vaccine design, immunodiagnostics, therapeutic antibody development, antibody engineering, research into autoimmune and allergic diseases, and advancing our understanding of immune responses. Despite in silico methods that have been proposed to predict both linear (continuous) and conformational (discontinuous) epitopes, they consistently underperform in predicting conformational epitopes. In this work, we propose Conformer-based models trained separately on AlphaFold-predicted structures and experimentally determined structures, leveraging convolutional neural networks (CNNs) to extract local features and Transformers to capture long-range dependencies within antigen sequences. Ablation studies demonstrate that CNN enhances the prediction of linear epitopes, and the Transformer module improves the prediction of conformational epitopes. Experimental results show that our model outperforms existing baselines in terms of MCC, ROC-AUC, PR-AUC, and F1 scores on both linear and conformational epitopes.