Automated multi-model reconstruction from single-particle electron microscopy data

Abstract

Biological macromolecules can adopt multiple conformational and compositional states due to structural flexibility and alternative subunit assemblies. This structural heterogeneity poses a major challenge in the study of macromolecular structure using single particle electron microscopy. We propose a fully automated, unsupervised method for the three-dimensional reconstruction of multiple structural models from heterogeneous data. As a starting reference, our method employs an initial structure that does not account for any heterogeneity. Then, a multi-stage clustering is used to create multiple models representative of the heterogeneity within the sample. The multi-stage clustering combines an existing approach based on Multivariate Statistical Analysis to perform clustering within individual Euler angles, and a newly developed approach to sort out class-averages from individual Euler angles into homogeneous groups. Structural models are computed from individual clusters. The whole data classification is further refined using an iterative multi-model projection matching approach. We tested our method on one synthetic and three distinct experimental datasets. The tests include the cases where a macromolecular complex exhibits structural flexibility and cases where a molecule is found in ligand-bound and unbound states. We propose the use of our approach as an efficient way to reconstruct distinct multiple models from heterogeneous data.

Robust w-Estimators for Cryo-EM Class Means

“…We also compared the performance of wme with the common-lines (cl) based approach in [16]. Two sets of 900 class means from uniform projections of the north hemisphere of the simulated 50S ribosome subunit were generated, with SNRs equivalent to averaging 30 images with SNRs of −8dB and −16dB.…”

“…Common-lines based outlier detection methods correlate images with particle images from all projection directions on the common-lines [16]. Although such a method incorporates more images than the classical approach, it has similar drawbacks resulting from using thresholds for outlier rejection.…”

Cryogenic Electron Microscopy and Single-Particle Analysis

“…However, there is no guarantee that independent refinement of dataset halves eliminates bias, given that the two starting volumes are not independently generated. Multiparticle reconstruction results provided by different groups on the same publicly available experimental ribosome data set (18,54,(85)(86)(87)(89)(90)(91). The methods that solve the problem ab initio are indicated.…”

Uncertainty in integrative structural modeling

“…Protein dynamics in solution, as measured by SAXS and NMR spectroscopy, can be modeled by fitting multiple weighted conformations to the data [30–34]. In EM single particle reconstruction, heterogeneity can be addressed by multi-model reconstruction using multi-stage clustering [35]. …”