2025/02/17 by Mansoor Ahmed, Sarwan Ali, Avais Jan +2 · 1 voice
Biochemistry, Genetics and Molecular Biology · Medicine · #Antibody #Artificial intelligence #Biology #Chemical Synthesis and Analysis #Computational biology #Computer science #Epitope #Glycosylation and Glycoproteins Research #Graph #Immunology #Language model #Monoclonal and Polyclonal Antibodies Research #Natural language processing #Theoretical computer science
paper · doi:10.1101/2025.02.12.637989
published in bioRxiv (Cold Spring Harbor Laboratory) (Cold Spring Harbor Laboratory)
openalex publication_date 2025/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14
Abstract The accurate identification of B-cell epitopes is critical in antibody design, diagnostics, and immunotherapies. Many in silico approaches have recently been proposed to predict epitopes, but these approaches struggle primarily because of the variational and conformational nature of epitopes. However, deep learning-based approaches have recently shown great promise in achieving better performance at the epitope prediction task. In this paper, we employ a graph convolutional network (GCN) coupled with pre-trained protein language model (PLM)-based embeddings for epitope prediction on a benchmark antibody-specific epitope prediction (AsEP) dataset. We explore the use of different PLM-embedding methods on the epitope prediction task and show that the choice of PLM embeddings impacts the performance. Specifically, we find that antibody-specific PLMs such as AntiBERTy and general PLMs such as ProtTrans and ESM-2 for antigens provide improved epitope prediction performance with an AUCROC of 0.65, precision of 0.28, and recall of 0.46. The source code is available at: https://github.com/mansoor181/walle-pp.git .