Cryo-EM and the elucidation of new macromolecular structures: Random Conical Tilt revisited.

Cryo-Electron Microscopy (cryo-EM) of macromolecular complexes is a fundamental structural biology technique which is expanding at a very fast pace. Key to its success in elucidating the three-dimensional structure of a macromolecular complex, especially of small and non-symmetric ones, is the ability to start from a low resolution map, which is subsequently refined with the actual images collected at the microscope. There are several methods to produce this first structure. Among them, Random Conical Tilt (RCT) plays a prominent role due to its unbiased nature (it can create an initial model based on experimental measurements). In this article, we revise the fundamental mathematical expressions supporting RCT, providing new expressions handling all key geometrical parameters without the need of intermediate operations, leading to improved automation and overall reliability, essential for the success of cryo-EM when analyzing new complexes. We show that the here proposed RCT workflow based on the new formulation performs very well in practical cases, requiring very few image pairs (as low as 13 image pairs in one of our examples) to obtain relevant 3D maps.

A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs.

MOTIVATION: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives. RESULTS: In this article, we introduce an automatic particle picker that learns from the user the kind of particles he is interested in. Particle candidates are quickly and robustly classified as particles or non-particles. A number of new discriminative shape-related features as well as some statistical description of the image grey intensities are used to train two support vector machine classifiers. Experimental results demonstrate that the proposed method: (i) has a considerably low computational complexity and (ii) provides results better or comparable with previously reported methods at a fraction of their computing time. AVAILABILITY: The algorithm is fully implemented in the open-source Xmipp package and downloadable from http://xmipp.cnb.csic.es.

Semiautomatic, high-throughput, high-resolution protocol for three-dimensional reconstruction of single particles in electron microscopy.

In this chapter we describe the steps needed for reconstructing the three-dimensional structure of a macromolecular complex starting from its projections collected in electron micrographs. The concepts are shown through the use of Xmipp 3.0, a software suite specifically designed for the image processing of biological structures imaged with electron or X-ray microscopy. We illustrate the image processing workflow by applying it to the images of Bovine Papilloma virus published in Wolf et al. (Proc Natl Acad Sci USA 107:6298-6303, 2010). We show that in the case of high-quality, homogeneous datasets with a priori knowledge about the initial volume, we can have a high-resolution 3D reconstruction in less than 1 day using a computer cluster with only 32 processors.