Hierarchical Protein Representation for Interface Co-Design with HICON

Abstract

Protein-protein interactions (PPIs) are essential for many biological processes, but their design is challenging due to their complex and dynamic nature. We propose a new model called Hierarchical Interface CO-design Network (HICON) that can jointly generate the sequence and 3D structure of protein interfaces. HICON uses a novel hierarchical architecture that combines atomic and amino acid resolutions in an equivariant manner and leverages Large Protein Language Models for sequence initialization. We evaluate HICON on a variety of biological interfaces, including protein-protein, enzyme-ligand, and antibody paratope-epitope interfaces. Our results show that HICON outperforms state-of-the-art models on sequence prediction and paratope co-design on several computational metrics.

Cite

Text

Khadhraoui et al. "Hierarchical Protein Representation for Interface Co-Design with HICON." NeurIPS 2023 Workshops: DGM4H, 2023.

Markdown

[Khadhraoui et al. "Hierarchical Protein Representation for Interface Co-Design with HICON." NeurIPS 2023 Workshops: DGM4H, 2023.](https://mlanthology.org/neuripsw/2023/khadhraoui2023neuripsw-hierarchical/)

BibTeX

@inproceedings{khadhraoui2023neuripsw-hierarchical,
  title     = {{Hierarchical Protein Representation for Interface Co-Design with HICON}},
  author    = {Khadhraoui, Aous and Gutierrez, Daniel Nakhaee-Zadeh and Kozlova, Elizaveta},
  booktitle = {NeurIPS 2023 Workshops: DGM4H},
  year      = {2023},
  url       = {https://mlanthology.org/neuripsw/2023/khadhraoui2023neuripsw-hierarchical/}
}