Improving antibody language models with native pairing.

ABSTRACT: Existing antibody language models are limited by their use of unpaired antibody sequence data. A recently published dataset of ∼1.6 × 10⁶ natively paired human antibody sequences offers a unique opportunity to evaluate how antibody language models are improved by training with native pairs. We trained three baseline antibody language models (BALM), using natively paired (BALM-paired), randomly-paired (BALM-shuffled), or unpaired (BALM-unpaired) sequences from this dataset. To address the paucity of paired sequences, we additionally fine-tuned ESM (evolutionary scale modeling)-2 with natively paired antibody sequences (ft-ESM). We provide evidence that training with native pairs allows the model to learn immunologically relevant features that span the light and heavy chains, whic

SUBMITTER: Burbach SM 

PROVIDER: S-EPMC11117052 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature