Protein Inpainting Co-Design with ProtFill
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 https://github.com/adaptyvbio/ProtFill.
Cite
Text
Kozlova et al. "Protein Inpainting Co-Design with ProtFill." NeurIPS 2023 Workshops: DGM4H, 2023.Markdown
[Kozlova et al. "Protein Inpainting Co-Design with ProtFill." NeurIPS 2023 Workshops: DGM4H, 2023.](https://mlanthology.org/neuripsw/2023/kozlova2023neuripsw-protein/)BibTeX
@inproceedings{kozlova2023neuripsw-protein,
title = {{Protein Inpainting Co-Design with ProtFill}},
author = {Kozlova, Elizaveta and Valentin, Arthur and Gutierrez, Daniel Nakhaee-Zadeh},
booktitle = {NeurIPS 2023 Workshops: DGM4H},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/kozlova2023neuripsw-protein/}
}