Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction.

TLDR: Non-uniform refinement, an algorithm based on cross-validation optimization, is introduced, which automatically regularizes 3D density maps during refinement to account for spatial variability and yields dramatically improved resolution and 3D map quality in many cases.

Abstract: Cryogenic electron microscopy (cryo-EM) is widely used to study biological macromolecules that comprise regions with disorder, flexibility or partial occupancy. For example, membrane proteins are often kept in solution with detergent micelles and lipid nanodiscs that are locally disordered. Such spatial variability negatively impacts computational three-dimensional (3D) reconstruction with existing iterative refinement algorithms that assume rigidity. We introduce non-uniform refinement, an algorithm based on cross-validation optimization, which automatically regularizes 3D density maps during refinement to account for spatial variability. Unlike common shift-invariant regularizers, non-uniform refinement systematically removes noise from disordered regions, while retaining signal useful for aligning particle images, yielding dramatically improved resolution and 3D map quality in many cases. We obtain high-resolution reconstructions for multiple membrane proteins as small as 100 kDa, demonstrating increased effectiveness of cryo-EM for this class of targets critical in structural biology and drug discovery. Non-uniform refinement is implemented in the cryoSPARC software package. Membrane proteins exhibit spatial variation in rigidity and disorder, which poses a challenge for traditional cryo-EM reconstruction algorithms. Non-uniform refinement accounts for this spatial variability, yielding improved 3D reconstruction quality even for small membrane proteins.