A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps

Cryo electron microscopy (cryo-EM) is used by biological research to visualize biomolecular complexes in 3D, but the heterogeneity of cryo-EM reconstructions is not easily estimated. Current processing paradigms nevertheless exert great effort to reduce flexibility and heterogeneity to improve the quality of the reconstruction. Clustering algorithms are typically employed to identify populations of data with reduced variability, but lack assessment of remaining heterogeneity. We have developed a fast and simple algorithm based on spatial filtering to estimate the heterogeneity of a reconstruction. In the absence of flexibility, this estimate approximates macromolecular component occupancy. We show that our implementation can derive reliable input parameters automatically, that the resulting estimate is accurate, and the reconstruction can be modified accordingly to emulate altered constituent occupancy, which may benefit conventionally employed maximum-likelihood classification methods. Here, we demonstrate the utility of this method for cryo-EM map interpretation, quantification, and particle-image signal subtraction.