@inproceedings{
zhang2023bending,
title={Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction using Diffusion Models},
author={Xuejin Zhang and Tomas Geffner and Matt McPartlon and Mehmet Akdel and Dylan Abramson and Graham Holt and Alexander Goncearenco and Luca Naef and Michael Bronstein},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=PQa3giMLZp}
}
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.
Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction using Diffusion Models
Xuejin Zhang Tomas Geffner Matt McPartlon Mehmet Akdel Dylan Abramson Graham Holt Alexander Goncearenco Luca Naef Michael Bronstein