Scalable emulation of protein equilibrium ensembles with generative deep learning
Paper cited by one technology page, indexed on Europe PMC as PubMed record 40638710 and published in Science; the citing page links this DOI, which is how the record was matched.
Overview
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 graphics processing unit (GPU). BioEmu integrates more than 200 milliseconds of molecular dynamics (MD) simulations, static structures, and experimental protein stabilities using new training algorithms. It captures diverse functional motions-including cryptic pocket formation, local unfolding, and domain rearrangements-and predicts relative free energies with 1 kilocalorie per mole accuracy compared with millisecond-scale MD and experimental data. BioEmu provides mechanistic insights by jointly modeling 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.
Indexed on Europe PMC as PubMed record 40638710 (DOI 10.1126/science.adv9817). Matched by DOI alone: one technology page cites this DOI among its external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor.
One technology page on OnCo cites this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing page listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
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