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

Keywords: diffusion models, protein flexibility

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.

Submission Number: 68