Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

Structural insights into Ca(2+)-activated long-range allosteric channel gating of RyR1.

Ryanodine receptors (RyRs) are a class of giant ion channels with molecular mass over 2.2 mega-Daltons. These channels mediate calcium signaling in a variety of cells. Since more than 80% of the RyR protein is folded into the cytoplasmic assembly and the remaining residues form the transmembrane domain, it has been hypothesized that the activation and regulation of RyR channels occur through an as yet uncharacterized long-range allosteric mechanism. Here we report the characterization of a Ca(2+)-activated open-state RyR1 structure by cryo-electron microscopy. The structure has an overall resolution of 4.9 Å and a resolution of 4.2 Å for the core region. In comparison with the previously determined apo/closed-state structure, we observed long-range allosteric gating of the channel upon Ca(2+) activation. In-depth structural analyses elucidated a novel channel-gating mechanism and a novel ion selectivity mechanism of RyR1. Our work not only provides structural insights into the molecular mechanisms of channel gating and regulation of RyRs, but also sheds light on structural basis for channel-gating and ion selectivity mechanisms for the six-transmembrane-helix cation channel family.