An antibody binds its target through six tiny hypervariable loops — and for decades the only way to improve one was to make thousands and screen them. Today’s digest is about the deep-learning models that learned to read, write, and refine antibodies. Ruffolo et al.’s DeepAb tackled structure with ‘interpretable deep learning,’ then IgFold scaled it — ‘a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks,’ predicting structures ‘of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s).’ Olsen et al.’s AbLang showed that for ‘antibody specific problems … a model trained solely on antibodies may be more powerful,’ restoring missing residues better than ’the general protein language model ESM-1b.’ Shuai et al.’s IgLM turned it generative, ‘a deep generative language model for creating synthetic antibody libraries’ via ’text-infilling.’ Hie et al. proved general PLMs ‘can efficiently evolve human antibodies … despite providing the model with no information about the target antigen,’ improving affinities ‘up to 160-fold’ with ‘20 or fewer variants.’ And Mason et al. attacked the screening bottleneck, ‘predicting antigen specificity from antibody sequence via deep learning.’ Reading, writing, and refining the immune system’s keys.
