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SSRAAI: Learning Sequence and Structural Representations to Predict Antibody-Antigen Interactions
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DOI：10.1109/TCBBIO.2025.3558695.png)
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Abstract
En 中文
The specific binding between antibodies (Ab) and antigens (Ag) is crucial for developing drugs and vaccines to treat major diseases. Therefore, accurate identification of antibody-antigen interactions (AAI) is crucial for a comprehensive understanding of antibody therapeutic mechanisms. While wet-lab methods accurately characterize AAI, they require significant human, financial, and time costs. Traditional computational methods help to reduce the resource consumption of AAI identification, but suffer from several problems, such as (1) they rely solely on sequence data, ignoring critical 3D structural determinants; (2) the scarcity of data on antibody-antigen interactions severely limits existing methods’ ability to represent unseen antibodies; (3) they focus narrowly on paratope-epitope residues, overlooking the contextual information provided by distal non-binding regions that can influence interaction patterns. To address these issues, we present an innovative model that learns sequence and structural representations to predict antibody-antigen interactions (SSRAAI). We extracted structural features by constructing contact maps from predicted PDB 3D structures. Additionally, the integration of sequence features based on adaptive relational graphs led to enhanced prediction outcomes. Our approach offers a unique integration of 3D structural information from PDB with sequence data, applied directly to Ab and Ag. Comparative results on two datasets, HIV and SARS-CoV-2, demonstrate the validity of our approach in identifying AAIs.
Keywords:
Antibody-antigen interactions
deep learning
3D structures
adaptive relational graphs
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