tetano
Editor, Senior Moderator
Immunity
. 2024 Aug 15:S1074-7613(24)00371-6.
doi: 10.1016/j.immuni.2024.07.022. Online ahead of print. An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies
Yiquan Wang[SUP] 1 [/SUP], Huibin Lv[SUP] 2 [/SUP], Qi Wen Teo[SUP] 2 [/SUP], Ruipeng Lei[SUP] 1 [/SUP], Akshita B Gopal[SUP] 1 [/SUP], Wenhao O Ouyang[SUP] 1 [/SUP], Yuen-Hei Yeung[SUP] 3 [/SUP], Timothy J C Tan[SUP] 4 [/SUP], Danbi Choi[SUP] 1 [/SUP], Ivana R Shen[SUP] 1 [/SUP], Xin Chen[SUP] 4 [/SUP], Claire S Graham[SUP] 1 [/SUP], Nicholas C Wu[SUP] 5 [/SUP]
Affiliations
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 experimentally validated many HA stem antibodies. Overall, this study not only advances our molecular understanding of the antibody response to the influenza virus but also provides a valuable resource for applying deep learning to antibody research.
Keywords: antibody; data mining; deep learning; hemagglutinin; influenza virus; language model; somatic hypermutations.
. 2024 Aug 15:S1074-7613(24)00371-6.
doi: 10.1016/j.immuni.2024.07.022. Online ahead of print. An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies
Yiquan Wang[SUP] 1 [/SUP], Huibin Lv[SUP] 2 [/SUP], Qi Wen Teo[SUP] 2 [/SUP], Ruipeng Lei[SUP] 1 [/SUP], Akshita B Gopal[SUP] 1 [/SUP], Wenhao O Ouyang[SUP] 1 [/SUP], Yuen-Hei Yeung[SUP] 3 [/SUP], Timothy J C Tan[SUP] 4 [/SUP], Danbi Choi[SUP] 1 [/SUP], Ivana R Shen[SUP] 1 [/SUP], Xin Chen[SUP] 4 [/SUP], Claire S Graham[SUP] 1 [/SUP], Nicholas C Wu[SUP] 5 [/SUP]
Affiliations
- PMID: 39163866
- DOI: 10.1016/j.immuni.2024.07.022
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 experimentally validated many HA stem antibodies. Overall, this study not only advances our molecular understanding of the antibody response to the influenza virus but also provides a valuable resource for applying deep learning to antibody research.
Keywords: antibody; data mining; deep learning; hemagglutinin; influenza virus; language model; somatic hypermutations.