Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity.

ABSTRACT: High throughput sequencing of B cell receptors (BCRs) is increasingly applied to study the immense diversity of antibodies. Learning biologically meaningful embeddings of BCR sequences is beneficial for predictive modeling. Several embedding methods have been developed for BCRs, but no direct performance benchmarking exists. Moreover, the impact of the input sequence length and paired-chain information on the prediction remains to be explored. We evaluated the performance of multiple embedding models to predict BCR sequence properties and receptor specificity. Despite the differences in model architectures, most embeddings effectively capture BCR sequence properties and specificity. BCR-specific embeddings slightly outperform general protein language models in predicting specificity. In ad

SUBMITTER: Wang M 

PROVIDER: S-EPMC10810273 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature