RPDynaFlow: Generating RNA-Protein Conformational Ensembles by Atomic Conditional Flow Matching

Abstract

Conformation ensembles of biomolecules provide the basis for understanding structural transformations and drug design. Deep-learning generative models have advanced protein and small molecule ensemble generation, while RNA-Protein complexes remain unaddressed due to the chemical heterogeneity, limited dataset size and the different flexibility scales of RNA and protein components. We present RPDynaFlow, a flow-matching model to generate conformation ensembles of RNA-protein complexes, trained on 600 ns trajectories of molecular dynamics(MD) simulation. The results show our model extends the sampling range of the phase space compared to MD simulation, which couldbe treated as a rapid and efficient complement to MD trajectoriesfor studying RNA-protein interactions.