Visualizing conformational space of functional biomolecular complexes by deep manifold learning

The cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving conformational continuum of important biomolecular complexes at atomic level is essential to understand their functional mechanisms and to guide structure-based drug discovery. Here we introduce a deep learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy reconstructions of conformational continuum. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of energy landscapes, which directs 3D clustering of markedly improved accuracy via an energy-based particle-voting algorithm. By applications of this approach to analyze five experimental datasets, we examine its generality in breaking resolution limit of visualizing dynamic components of functional complexes, in discovering 'invisible' lowly populated intermediates and in exploring their hidden conformational space. Our approach expands the realm of structural ensemble determination to the nonequilibrium regime at atomic level, thus potentially transforming biomedical research and therapeutic development.