Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction Using Diffusion Models
Abstract
Predicting protein conformational changes driven by binding of small molecular ligands is imperative to accelerate drug discovery for protein targets with no established binders. This work presents a novel method to capture such conformational changes: given a protein apo conformation (unbound state), we propose an equivariant conditional diffusion model to predict its holo conformations (bound state with external small molecular ligands). We design a novel variant of the EGNN architecture for the score network (score-informed EGNN), which is able to exploit conditioning information in the form of the reference (apo) structure to guide the diffusion's sampling process. Learning from experimentally determined apo/holo conformations, we observe that our model can generate conformations close to holo conditioned only on apo state.
Cite
Text
Zhang et al. "Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction Using Diffusion Models." NeurIPS 2023 Workshops: GenBio, 2023.Markdown
[Zhang et al. "Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction Using Diffusion Models." NeurIPS 2023 Workshops: GenBio, 2023.](https://mlanthology.org/neuripsw/2023/zhang2023neuripsw-bending/)BibTeX
@inproceedings{zhang2023neuripsw-bending,
title = {{Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction Using Diffusion Models}},
author = {Zhang, Xuejin and Geffner, Tomas and McPartlon, Matt and Akdel, Mehmet and Abramson, Dylan and Holt, Graham and Goncearenco, Alexander and Naef, Luca and Bronstein, Michael},
booktitle = {NeurIPS 2023 Workshops: GenBio},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/zhang2023neuripsw-bending/}
}