Hybrid methods for macromolecular structure determination: experiment with expectations

• Modeling with low-resolution data benefits from structure prediction.
• Different choices exist for combining simulation and experiment.
• Cryo-EM and X-ray diffraction provide information on dynamics.
• Model ensembles represent uncertainty and/or dynamics.

Studies of large and heterogeneous macromolecules often yield low-resolution data that alone does not suffice to build accurate atomic models. Adding information from molecular simulation or other structure prediction methods can lead to models with significantly better quality. Different strategies are discussed to combine experimental data with results from simulation and prediction. This review describes recent approaches for building atomic models with a focus on X-ray diffraction and single-particle cryo-electron microscopy (cryo-EM) data. In addition, both cryo-EM and X-ray diffraction provide information on molecular dynamics. Therefore, the best description of molecular structures is often by an ensemble of models. It furthermore becomes apparent that using raw data for the modeling ensures that all information obtained by the experiment can be fully exploited. It is also important to quantify the errors of both experiment and simulation to correctly weigh their different contributions.