Scalable emulation of protein equilibrium ensembles with BioEmu

  • Frank Noé, Microsoft

Following the sequence and structure revolutions, predicting functionally relevant protein structure changes at scale remains an outstanding challenge. Microsoft Research AI for Science introduces 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 towards understanding and designing protein function.

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