Exploring protein conformational ensembles using evolutionary conditional diffusion

Abstract

Protein conformational ensembles encode the dynamic landscapes underlying biological function, regulation, and allostery. Accurately reconstructing such ensembles while balancing conformational distributions accuracy and physical plausibility remains a fundamental challenge in structural biology, particularly when dynamic data is scarce. Here, we propose DiffEnsemble, a diffusion-based framework designed for modeling protein conformational ensembles. DiffEnsemble learns latent dynamical representations from static protein structures in the Protein Data Bank, integrated with the structural profile derived from the AlphaFold Protein Structure Database as conditional guidance during the diffusion process. Benchmarking on 72 protein targets from the ATLAS molecular dynamics simulation dataset demonstrates that DiffEnsemble outperforms existing methods, including BioEmu and AlphaFLOW. Compared with AlphaFLOW, DiffEnsemble achieves improvements of 28.9% and 11.3% in Pearson correlation coefficients for ensemble pairwise root mean square deviation and root mean square fluctuation, respectively. Importantly, DiffEnsemble successfully captures the dominant motions for 42% of the targets. These results demonstrate that latent dynamical information embedded in static structural data can effectively support the modeling of protein conformational ensembles.