Hierarchical autoclassification of cryo-EM samples and macromolecular energy landscape determination

BACKGROUND AND OBJECTIVE

Cryo-electron microscopy using single particle analysis is a powerful technique for obtaining 3D reconstructions of macromolecules in near native conditions. One of its major advances is its capacity to reveal conformations of dynamic molecular complexes. Most popular and successful current approaches to analyzing heterogeneous complexes are founded on Bayesian inference. However, these 3D classification methods require the tuning of specific parameters by the user and the use of complicated 3D re-classification procedures for samples affected by extensive heterogeneity. Thus, the success of these approaches highly depends on the user experience. We introduce a robust approach to identify many different conformations presented in a cryo-EM dataset based on Bayesian inference through Relion classification methods that does not require tuning of parameters and reclassification strategies.

METHODS

The algorithm allows both 2D and 3D classification and is based on a hierarchical clustering approach that runs automatically without requiring typical inputs, such as the number of conformations present in the dataset or the required classification iterations. This approach is applied to robustly determine the energy landscapes of macromolecules.

RESULTS

We tested the performance of the methods proposed here using four different datasets, comprising structurally homogeneous and highly heterogeneous cases. In all cases, the approach provided excellent results. The routines are publicly available as part of the CryoMethods plugin included in the Scipion package.

CONCLUSIONS

Our results show that the proposed method can be used to align and classify homogeneous and heterogeneous datasets without requiring previous alignment information or any prior knowledge about the number of co-existing conformations. The approach can be used for both 2D and 3D autoclassification and only requires an initial volume. In addition, the approach is robust to the "attractor" problem providing many different conformations/views for samples affected by extensive heterogeneity. The obtained 3D classes can render high resolution 3D structures, while the obtained energy landscapes can be used to determine structural trajectories.

A robust approach to ab initio cryo-electron microscopy initial volume determination

Structural information from macromolecules provides key insights into the way complexes perform their biological functions. The reconstruction process leading to the final three-dimensional (3D) map is iterative and requires an initial volume to prime the refinement procedure. Particle images are aligned to this first reference and subsequently a new map is calculated from these particles. The accurate determination of an ab initio initial volume is still a challenging and open problem in cryo-electron microscopy (cryo-EM). Different algorithms are available to estimate an initial volume from the dataset. Some of these methods provide multiple candidate initial maps and users looking for robustness typically run different approaches. In this case, users arbitrarily evaluate the different obtained candidate maps, as we lack robust methods to objectively assess the accuracy of initial references. This workflow is subjective and error-prone preventing implementation of high-throughput data processing procedures. In this work, we present a robust method to determine the best initial map or maps from a set of ab initio initial volumes obtained from one or multiple different approaches. The method is based on evaluating multiple small subsets of candidate initial volumes and particle images through reference-based 3D classifications. Obtained 3D classes of accurate initial maps will result majoritarian and the respective attracted particles will be aligned with high angular accuracies. We have tested the proposed approach with structurally homogeneous and heterogeneous datasets providing satisfactory results with both type of data.

Foil-hole and data image quality assessment in 3DEM: Towards high-throughput image acquisition in the electron microscope

Automatic or semiautomatic data collection approaches on a transmission electron microscope (TEM) for Single Particle Analysis, capable of acquiring large datasets composed of only high quality images, are of great importance to obtain 3D density maps with the highest resolution possible. Typically, this task is performed by an experienced microscopist, who manually decides to keep or discard images according to subjective criteria. Therefore, this methodology is slow, intensive in human work and subjective. In this work, we propose a method to automatically or semiautomatically perform this image selection task. The approach is based on some simple, fast and effective image quality descriptors, which can be computed during acquisition, to characterize foil-hole and data images. The proposed approach has been used to evaluate the quality of different datasets consisting of foil-hole and data images obtained with a FEI Titan Krios electron microscope. The results show that the proposed method is very effective evaluating the quality of foil-hole and data images, as well as predicting the quality of the data images from the foil-hole images.