Scalable emulation of protein equilibrium ensembles with generative deep learning - Lewis, Sarah; Hempel, Tim; Jimenez-Luna, Jose; Gastegger, Michael; Xie, Yu; Foong, Andrew Y. K.; Satorras, Victor Garcia; Abdin, Osama; Veeling, Bastiaan S.; Zaporozhets, I.; Chen, Yaoyi; Yang, Soojung; et al. (Science, 2025)
Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. We introduce BioEmu, a deep learning system that emulates protein equilibrium ensembles by generating thousands of statistically independent structures per hour on a single GPU. BioEmu integrates over 200 milliseconds of molecular dynamics (MD) simulations, static structures and experimental protein stabilities using novel training algorithms. It captures diverse functional motions—including cryptic pocket formation, local unfolding, and domain rearrangements—and predicts relative free energies with 1 kcal/mol accuracy compared to millisecond-scale MD and experimental data. BioEmu provides mechanistic insights by jointly modelling structural ensembles and thermodynamic properties. This approach amortizes the cost of MD and experimental data generation, demonstrating a scalable path toward understanding and designing protein function.