Single particle analysis integrated with microscopy: a high-throughput approach for reconstructing icosahedral particles

Automated Single-Particle Reconstruction of Heterogeneous Inorganic Nanoparticles

Single-particle reconstruction can be used to perform three-dimensional (3D) imaging of homogeneous populations of nano-sized objects, in particular viruses and proteins. Here, it is demonstrated that it can also be used to obtain 3D reconstructions of heterogeneous populations of inorganic nanoparticles. An automated acquisition scheme in a scanning transmission electron microscope is used to collect images of thousands of nanoparticles. Particle images are subsequently semi-automatically clustered in terms of their properties and separate 3D reconstructions are performed from selected particle image clusters. The result is a 3D dataset that is representative of the full population. The study demonstrates a methodology that allows 3D imaging and analysis of inorganic nanoparticles in a fully automated manner that is truly representative of large particle populations.

Breaking Symmetry in Viral Icosahedral Capsids as Seen through the Lenses of X-ray Crystallography and Cryo-Electron Microscopy

The majority of viruses on Earth form capsids built by multiple copies of one or more types of a coat protein arranged with 532 symmetry, generating an icosahedral shell. This highly repetitive structure is ideal to closely pack identical protein subunits and to enclose the nucleic acid genomes. However, the icosahedral capsid is not merely a passive cage but undergoes dynamic events to promote packaging, maturation and the transfer of the viral genome into the host. These essential processes are often mediated by proteinaceous complexes that interrupt the shell’s icosahedral symmetry, providing a gateway through the capsid. In this review, we take an inventory of molecular structures observed either internally, or at the 5-fold vertices of icosahedral DNA viruses that infect bacteria, archea and eukaryotes. Taking advantage of the recent revolution in cryo-electron microscopy (cryo-EM) and building upon a wealth of crystallographic structures of individual components, we review the design principles of non-icosahedral structural components that interrupt icosahedral symmetry and discuss how these macromolecules play vital roles in genome packaging, ejection and host receptor-binding.

A Two-Phase Improved Correlation Method for Automatic Particle Selection in Cryo-EM

Particle selection from cryo-electron microscopy (Cryo-EM) images is very important for high-resolution reconstruction of macromolecular structure. The methods of particle selection can be roughly grouped into two classes, template-matching methods and feature-based methods. In general, template-matching methods usually generate better results than feature-based methods. However, the accuracy of template-matching methods is restricted by the noise and low contrast of Cryo-EM images. Moreover, the processing speed of template-matching methods, restricted by the random orientation of particles, further limits their practical applications. In this paper, combining the advantages of feature-based methods and template-matching methods, we present a two-phase improved correlation method for automatic, fast particle selection. In Phase I, we generate a preliminary particle set using rotation-invariant features of particles. In Phase II, we filter the preliminary particle set using a correlation method to reduce the interference of the high noise background and improve the precision of particle selection. We apply several optimization strategies, including a modified adaboost algorithm, Divide and Conquer technique, cascade strategy and graphics processing unit parallel technique, to improve feature recognition ability and reduce processing time. In addition, we developed two correlation score functions for different correlation situations. Experimental results on the benchmark of Cryo-EM images show that our method can improve the accuracy and processing speed of particle selection significantly.

cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination

Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for determining the structures of biological macromolecules. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resolution frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calculations on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound maximum likelihood optimization algorithms permit the major steps in cryo-EM structure determination to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated analysis and discovery of unexpected structures without bias from a reference map. These algorithms are combined in a user-friendly computer program named cryoSPARC (http://www.cryosparc.com).