PLMABFW: A Deep Learning Framework for Predicting Antibody-Antigen Interactions Using Protein Language Model

The emergence of SARS-CoV-2 has highlighted the need for computational methods to identify neutralizing antibodies. Existing sequence-based tools for predicting antigen-antibody interactions struggle to effectively identify antibodies capable of neutralizing different variants due to high sequence similarity among SARS-CoV-2 strains and the similarity in the framework regions of antibodies. To address this challenge, particularly the issue of high sequence similarity among homologous antigens that impedes accurate prediction of antigen-antibody interactions, we developed a deep learning framework named PLMABFW. It differentiates homologous antigens using encoding techniques and network architecture design. It employs pre-trained protein language models ESM-2 for antigens and AntiBERTy for antibodies to encode sequences and capture additional features. The framework also incorporates both antigen features and their transposed versions to enhance antigen information capture. To validate the performance of PLMABFW, we collected a SARS-CoV-2 neutralization dataset. PLMABFW outperformed existing neutralizing antibody prediction tools (AbAgIntPre, DeepAAI) and docking tools (HDOCK, LSTM-PHV) in predicting neutralizing antibodies for homologous antigens. Furthermore, it effectively learned the interactions between the antibody's CDR-H3 region and antigens via a partial masking strategy. The model code is available on GitHub for customization and adaptation to diverse research needs.