The authors tackle antibody design using a modified diffusion model that avoids simply memorizing germline (inherited template) sequences. Standard protein language models trained on antibody data tend to just reproduce germline sequences rather than learning the biologically meaningful mutations that happen during immune response. Their "germline-absorbing diffusion" uses the germline sequence as the starting/ending point of the diffusion process (instead of random noise), forcing the model to learn only the trajectory of somatic mutations. This dramatically improves the model's ability to predict non-germline mutations (from 26% to 46% accuracy) and enables better conditional generation of antibodies with desired properties like reduced hydrophobicity.
Main takeaways:
- Standard protein language models memorize germline (inherited) antibody sequences rather than learning the somatic mutations that create useful antibodies
- Germline-absorbing diffusion modifies the noise process so the model learns the trajectory from germline to mutated sequence, excluding genetic recombination statistics
- This biological inductive bias improves non-germline residue prediction from 26% to 46%, approaching the theoretical limit set by natural biological variability
- The model supports classifier-guided generation: you can condition antibody design on any property predicted by an off-the-shelf classifier
- Demonstrates improved trade-offs between target properties (hydrophobicity, binding affinity) and sample quality compared to baselines