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Workshop: Machine Learning in Structural Biology Workshop

Improved encoding of ensembles in PDBx/mmCIF

Stephanie Wankowicz · James Fraser


In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo electron microscopy (cryoEM). While new tools are available to detect conformational and compositional heterogeneity that exist within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the Macromolecular Crystallographic Information File (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable improved tools to capture macromolecular ensembles in a way that is human and machine interpretable, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to AlphaFold's achievements with single structure prediction.

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