In response to pathogens, the adaptive immune system generates specific
antibodies that bind and neutralize foreign antigens. Understanding the
composition of an individual's immune repertoire can provide insights into this
process and reveal potential therapeutic antibodies. In this work, we explore
the application of antibody-specific language models to aid understanding of
immune repertoires. We introduce AntiBERTy, a language model trained on 558M
natural antibody sequences. We find that within repertoires, our model clusters
antibodies into trajectories resembling affinity maturation. Importantly, we
show that models trained to predict highly redundant sequences under a multiple
instance learning framework identify key binding residues in the process. With
further development, the methods presented here will provide new insights into
antigen binding from repertoire sequences alone.

本文介绍一种名为 AntiBERTy 的语言模型，该模型基于 558M 个天然抗体序列进行训练，在免疫库中对抗体进行聚类，用于理解免疫应答的亲和力成熟的过程，针对高度冗余的序列进行多实例学习，以确定免疫库中关键的结合残基。该方法的进一步发展将能够仅从免疫库序列中提供新的抗原结合洞察。