Biology subjects

Gopal, A. B.

Publications and source records attributed to Gopal, A. B..

2 recordsLinked to original sources

bioRxiv2024

Rapid synthesis and screening of natively paired antibodies against influenza hemagglutinin stem via oPool+ display

Antibody discovery is crucial for developing therapeutics and vaccines as well as understanding adaptive immunity. However, the lack of approaches to synthesize antibodies with defined sequences in a high-throughput manner represents a major bottleneck in antibody discovery. Here, we presented oPool+ display, a high-throughput cell-free platform that combined oligo pool synthesis and mRNA display to rapidly construct and characterize many natively paired antibodies in parallel. As a proof-of-concept, we applied oPool+ display to probe the binding specificity of >300 uncommon influenza hemagglutinin (HA) antibodies against 9 HA variants through 16 different screens. Over 5,000 binding tests were performed in 3-5 days with further scaling potential. Follow-up structural analysis of two HA stem antibodies revealed the previously unknown versatility of IGHD3-3 gene segment in recognizing the HA stem. Overall, this study established an experimental platform that not only accelerate antibody characterization, but also enable unbiased discovery of antibody molecular signatures.

immunology↗

bioRxiv2023

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

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 inaccessibility of datasets for model training. In this study, we curated a dataset of >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 captured key sequence motifs 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 antibody response to influenza virus, but also provides an invaluable resource for applying deep learning to antibody research.

immunology↗