@inproceedings{
chu2023generative,
title={Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model},
author={Simon Chu and Kathy Wei},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=QrH4bhWhwY}
}
Current protein language models (pLMs) predominantly focus on single-chain protein sequences and often have not accounted for constraints on generative design imposed by protein-protein interactions. To address this gap, we present paired Antibody T5 (pAbT5), an encoder-decoder model to generate complementary heavy or light chain from its pairing partner. We show that our model respects conservation in framework regions and variability in hypervariable domains, demonstrated by agreement with sequence alignment and variable-length CDR loops. We also show that our model captures chain pairing preferences through the recovery of ground-truth chain type and gene families. Our results showcase the potential of pAbT5 in generative antibody design, incorporating biological constraints from chain pairing preferences.
Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model
Simon Chu Kathy Wei