Protein Inpainting Co-Design with ProtFill

Published: 27 Oct 2023, Last Modified: 10 Nov 2023DGM4H NeurIPS 2023 BestPaperEveryoneRevisionsBibTeX
Keywords: protein design, co-design, GNN, diffusion
TL;DR: We present ProtFill, a new protein sequence and structure co-design diffusion model that works with antibodies as well as other proteins and outperforms existing alternatives on sequence prediction.
Abstract: Designing new proteins with specific binding capabilities is a challenging task that has the potential to revolutionize many fields, including medicine and material science. Here we introduce ProtFill, a novel method for the simultaneous design of protein structures and sequences. Employing an $SE(3)$ equivariant diffusion graph neural network, our method excels in both sequence prediction and structure recovery compared to SOTA models. We incorporate edge feature updates in GVP-GNN message passing layers to refine our design process. The model's applicability for the interface redesign task is showcased for antibodies as well as other proteins. The code is available at
Submission Number: 33