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Poster
in
Workshop: Generative AI and Biology (GenBio@NeurIPS2023)

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.

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