BCRInsight: an antibody language model to decode biological signals from BCR sequences

The B-cell receptor (BCR) repertoire encodes not only antigen-binding specificity but also intrinsic signatures reflecting B-cell functional states and differentiation trajectories. Deciphering the intricate sequence semantics embedded within these repertoires is pivotal for elucidating immune dynamics and expediting antibody discovery. Although single-cell sequencing provides high-resolution insights, its scalability and cost remain major obstacles, leaving population-level repertoire data underexploited. Furthermore, conventional bioinformatics approaches struggle to model the high-order, non-linear semantic dependencies inherent in antibody sequences. To address these challenges, we present BCRInsight, an antibody-specific pretrained language model that integrates a Transformer architecture with phenotype-aware contrastive learning. Pretrained on 80 million human BCR sequences, BCRInsight learns biologically meaningful contextual representations that encode subtle signatures of B-cell activation, maturation, and clonal evolution. Extensive benchmarking demonstrates that BCRInsight achieves state-of-the-art performance across multiple downstream tasks, particularly in paratope prediction. Further evaluation on diverse single-cell immune cohorts, including healthy, neoplastic, and viral infection states, reveals cross-scenario robustness and superior generalization relative to existing methods. Notably, attention-based analyses show that high-attention regions correspond closely to physical antigen-contact residues, highlighting emergent structural interpretability derived solely from self-supervised learning. Collectively, BCRInsight establishes a new paradigm for decoding the "language" of antibodies, offering a scalable and interpretable framework for computational immunology and rational antibody engineering.