The goal of this paper is to construct accurate three-dimensional models from atomic structures that can be compared to Electron Microscopy volumes. They, in their turn, can be used to solve fitting and docking problems as well as to generate ground-truth models for the development of image processing algorithms. We use Electron Atomic Scattering Factor (EASF) along with a careful filter design to greatly accelerate the current use of these functions and strongly reduce computer memory requirements. We show that our method is 3 orders of magnitude faster than existing implementations of EASFs in Fourier space as well as more accurate than their standard implementations. The algorithm is freely available in the software package Xmipp (http://xmipp.cnb.csic.es) [1].

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.