AIRR-C Seminar Series, February 22nd, 2024 - Mamie Wang, Yale University, US
2.5 هزار بار بازدید -
5 ماه پیش
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Language model-based B cell receptor
Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity
Early Career Speaker: Mamie Wang, Yale University
Talk 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 addition, incorporating full-length heavy chains and paired light chain sequences improve the prediction performance of all embeddings. This study provides insights into the properties of BCR embeddings to improve downstream prediction applications for antibody analysis and discovery.
Speaker bio
Mamie Wang is a PhD student in Computational Biology at Yale University, working in the lab of Steven Kleinstein. Her research interests involve developing and applying computational methods for understanding B cell specificity and repertoire biology during human immune response.
AIRR-C Seminar Series website: https://www.antibodysociety.org/the-a...
Early Career Speaker: Mamie Wang, Yale University
Talk 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 addition, incorporating full-length heavy chains and paired light chain sequences improve the prediction performance of all embeddings. This study provides insights into the properties of BCR embeddings to improve downstream prediction applications for antibody analysis and discovery.
Speaker bio
Mamie Wang is a PhD student in Computational Biology at Yale University, working in the lab of Steven Kleinstein. Her research interests involve developing and applying computational methods for understanding B cell specificity and repertoire biology during human immune response.
AIRR-C Seminar Series website: https://www.antibodysociety.org/the-a...
5 ماه پیش
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