New computational methods toward atomic resolution in single particle cryo-electron microscopy

Abstract

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 22-06-2016Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. In turn, Electron microscopy (EM) is an essential tool to study the structure and function of biological macromolecules at a medium-high resolution. In this context, Single-Particle Analysis (SPA), as an EM modality, is able to yield Three-Dimensional (3-D) structural information for large biological complexes at near atomic resolution by combining many thousands of projection images. However, these views su er from low Signal-to-Noise Ratios (SNRs), since an extremely low total electron dose is used during exposure to reduce radiation damage and preserve the functional structure of macromolecules. In recent years, the emergence of Direct Detection Devices (DDDs) has opened up the possibility of obtaining images with higher SNRs. These detectors provide a set of frames instead of just one micrograph, which makes it possible to study the behavior of frozen hydrated specimens as a function of electron dose and rate. In this way, it has become apparent that biological specimens embedded in a solid matrix of amorphous ice move during imaging, resulting in Beam-Induced Motion (BIM). Therefore, alignment of frames should be added to the classical standard data processing work ow of single-particle reconstruction, which includes: particle selection, particle alignment, particle classi cation, 3-D reconstruction, and model re nement. In this thesis, we propose new algorithms and improvements for three important steps of this work ow: movie alignment, particles selection, and 3-D reconstruction. For movie alignment, a methodology based on a robust to noise optical ow approach is proposed that can e ciently correct for local movements and provide quantitative analysis of the BIM pattern. We then introduce a method for automatic particle selection in micrographs that uses some new image features to train two classi ers to learn from the user the kind of particles he is interested in. Finally, for 3-D reconstruction, we introduce a gridding-based direct Fourier method that uses a weighting technique to compute a uniform sampled Fourier transform. The algorithms are fully implemented in the open-source Xmipp package (http://xmipp.cnb.csic.es