Modeling experimental image formation for likelihood-based classification of electron microscopy data

Maximum likelihood refinement of electron microscopy data with normalization errors

Commonly employed data models for maximum likelihood refinement of electron microscopy images behave poorly in the presence of normalization errors. Small variations in background mean or signal brightness are relatively common in cryo-electron microscopy data, and varying signal-to-noise ratios or artifacts in the images interfere with standard normalization procedures. In this paper, a statistical data model that accounts for normalization errors is presented, and a corresponding algorithm for maximum likelihood classification of structurally heterogeneous projection data is derived. The extended data model has general relevance, since similar algorithms may be derived for other maximum likelihood approaches in the field. The potentials of this approach are illustrated for two structurally heterogeneous data sets: 70S E.coli ribosomes and human RNA polymerase II complexes. In both cases, maximum likelihood classification based on the conventional data model failed, whereas the new approach was capable of revealing previously unobserved conformations.

The structural dynamics of macromolecular processes

Dynamic processes involving macromolecular complexes are essential to cell function. These processes take place over a wide variety of length scales from nanometers to micrometers, and over time scales from nanoseconds to minutes. As a result, information from a variety of different experimental and computational approaches is required. We review the relevant sources of information and introduce a framework for integrating the data to produce representations of dynamic processes.

Single-molecule observations of ribosome function

Single-molecule investigations promise to greatly advance our understanding of basic and regulated ribosome functions during the process of translation. Here, recent progress towards directly imaging the elemental translation elongation steps using fluorescence resonance energy transfer (FRET)-based imaging methods is discussed, which provide striking evidence of the highly dynamic nature of the ribosome. In this view, global rates and fidelities of protein synthesis reactions may be regulated by interactions of the ribosome with mRNA, tRNA, translation factors and potentially many other cellular ligands that modify intrinsic conformational equilibria in the translating particle. Future investigations probing this model must aim to visualize translation processes from multiple structural and kinetic perspectives simultaneously, to provide direct correlations between factor binding and conformational events.

Image processing for electron microscopy single-particle analysis using XMIPP

We describe a collection of standardized image processing protocols for electron microscopy single-particle analysis using the XMIPP software package. These protocols allow performing the entire processing workflow starting from digitized micrographs up to the final refinement and evaluation of 3D models. A particular emphasis has been placed on the treatment of structurally heterogeneous data through maximum-likelihood refinements and self-organizing maps as well as the generation of initial 3D models for such data sets through random conical tilt reconstruction methods. All protocols presented have been implemented as stand-alone, executable python scripts, for which a dedicated graphical user interface has been developed. Thereby, they may provide novice users with a convenient tool to quickly obtain useful results with minimum efforts in learning about the details of this comprehensive package. Examples of applications are presented for a negative stain random conical tilt data set on the hexameric helicase G40P and for a structurally heterogeneous data set on 70S Escherichia coli ribosomes embedded in vitrified ice.

Molecular electron microscopy: state of the art and current challenges

The objective of molecular electron microscopy (EM) is to use electron microscopes to visualize the structure of biological molecules. This Review provides a brief overview of the methods used in molecular EM, their respective strengths and successes, and current developments that promise an even more exciting future for molecular EM in the structural investigation of proteins and macromolecular complexes, studied in isolation or in the context of cells and tissues.