De novo design of antibody heavy chains with SE(3) diffusion

Frederic A Dreyer, Daniel Cutting, David Errington, Constantin Schneider, Charlotte Deane

Keywords: protein design, generative AI, diffusion, antibody

TL;DR: Protein backbone diffusion model applied to de novo antibody heavy chain generation

Abstract:

We introduce VH-Diff, an antibody heavy chain variable domain diffusion model.
This model is based on FrameDiff, a general protein backbone diffusion framework, which was fine-tuned on antibody structures.
The backbone dihedral angles of sampled structures show good agreement with a reference antibody distribution.
We use an antibody-specific inverse folding model to recover sequences corresponding to the predicted structures, and study their validity with an antibody numbering tool.
Assessing the designability and novelty of the structures generated with our heavy chain model we find that VH-Diff produces highly designable structures that can contain novel binding regions.
Finally, we compare our model with a state-of-the-art sequence-based generative model and show more consistent preservation of the conserved framework region with our structure-based method.

Submission Number: 14