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PLMABFW: A deep learning framework for predicting Antibody-Antigen interactions using protein language model.

Yongbing Chen1,2,3, Qianyi Jia1, Xinyue Jia1

  • 1School of Information Science and Technology, and Center of AI for Science, Northeast Normal University, Changchun, China.

Journal of Bioinformatics and Computational Biology
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning framework, PLMABFW, accurately predicts neutralizing antibodies against SARS-CoV-2 variants. It overcomes challenges posed by high sequence similarity in homologous antigens, improving antibody discovery.

Keywords:
Antibody–antigen interactionsdeep learningneutralizing antibody predictionprotein language model

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Area of Science:

  • Computational biology
  • Immunoinformatics
  • Machine learning in drug discovery

Background:

  • The emergence of SARS-CoV-2 necessitates advanced computational methods for identifying neutralizing antibodies.
  • Existing sequence-based prediction tools face limitations in distinguishing between homologous antigens, hindering accurate prediction of antibody neutralization efficacy against viral variants.
  • High sequence similarity among SARS-CoV-2 strains and antibody framework regions (FWRs) complicates antigen-antibody interaction prediction.

Purpose of the Study:

  • To develop a novel deep learning framework, PLMABFW, for accurate prediction of neutralizing antibodies, specifically addressing the challenge of homologous antigen discrimination.
  • To enhance the prediction of antigen-antibody interactions by incorporating advanced encoding techniques and network architecture designs.
  • To improve the identification of antibodies capable of neutralizing diverse SARS-CoV-2 variants.

Main Methods:

  • Developed PLMABFW, a deep learning framework utilizing pre-trained protein language models ESM-2 for antigen encoding and AntiBERTy for antibody encoding.
  • Implemented encoding techniques and network architecture design to differentiate homologous antigens.
  • Incorporated antigen features and their transposed versions to enrich antigen information capture.
  • Utilized a SARS-CoV-2 neutralization dataset for model validation and employed a partial masking strategy to learn CDR-H3-antigen interactions.

Main Results:

  • PLMABFW demonstrated superior performance in predicting neutralizing antibodies against homologous antigens compared to existing tools like AbAgIntPre, DeepAAI, HDOCK, and LSTM-PHV.
  • The framework effectively captured complex antigen-antibody interactions, particularly highlighting the role of the antibody's CDR-H3 region.
  • Validated the model's efficacy using a curated SARS-CoV-2 neutralization dataset.

Conclusions:

  • PLMABFW offers a significant advancement in computational prediction of neutralizing antibodies, particularly for challenging homologous antigens.
  • The framework's ability to differentiate homologous antigens and capture intricate binding interactions paves the way for more effective antibody discovery and design.
  • The open availability of the model code facilitates broader application and customization for various research needs in antibody engineering and infectious disease research.