AntiDIF: Accurate and Diverse Antibody Specific Inverse Folding with Discrete Diffusion

Nikhil Branson, Charlotte Deane

Keywords: Diffusion, Discrete Diffusion, Antibody, Protein, Inverse Folding, Structural Biology

Abstract:

Inverse folding is an important step in current
computational antibody design. Recently deep
learning methods have made impressive progress
in improving the sequence recovery of antibod-
ies given their 3D backbone structure. However,
inverse folding is often a one-to-many problem,
i.e. there are multiple sequences that fold into the
same structure. Previous methods have not taken
into account the diversity between the predicted
sequences for a given structure. Here we create
AntiDIF an Antibody-specific discrete Diffusion
model for Inverse Folding. Compared with state-
of-the-art methods we show that AntiDIF im-
proves diversity between predictions while keep-
ing high sequence recovery rates. Furthermore,
forward folding of the generated sequences shows
good agreement with the target 3D structure.

Submission Number: 19