An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies.

ABSTRACT: Despite decades of antibody research, it remains challenging to predict the specificity of an antibody solely based on its sequence. Two major obstacles are the lack of appropriate models and the inaccessibility of datasets for model training. In this study, we curated >5,000 influenza hemagglutinin (HA) antibodies by mining research publications and patents, which revealed many distinct sequence features between antibodies to HA head and stem domains. We then leveraged this dataset to develop a lightweight memory B cell language model (mBLM) for sequence-based antibody specificity prediction. Model explainability analysis showed that mBLM could identify key sequence features of HA stem antibodies. Additionally, by applying mBLM to HA antibodies with unknown epitopes, we discovered and exp

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11464180 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature