From lows to highs: using low-resolution models to phase X-ray data

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure.

Using a partial atomic model from medium-resolution cryo-EM to solve a large crystal structure

Medium-resolution cryo-electron microscopy maps, in particular when they include a significant number of α-helices, may allow the building of partial models that are useful for molecular-replacement searches in large crystallographic structures when the structures of homologs are not available and experimental phasing has failed. Here, as an example, the solution of the structure of a bacteriophage portal using a partial 30% model built into a 7.8 Å resolution cryo-EM map is shown. Inspection of the self-rotation function allowed the correct oligomerization state to be determined, and density-modification procedures using rotation matrices and a mask based on the cryo-EM structure were critical for solving the structure. A workflow is described that may be applicable to similar cases and this strategy is compared with direct use of the cryo-EM map for molecular replacement.

How cryo-electron microscopy and X-ray crystallography complement each other

With the ability to resolve structures of macromolecules at atomic resolution, X-ray crystallography has been the most powerful tool in modern structural biology. At the same time, recent technical improvements have triggered a resolution revolution in the single particle cryo-EM method. While the two methods are different in many respects, from sample preparation to structure determination, they both have the power to solve macromolecular structures at atomic resolution. It is important to understand the unique advantages and caveats of the two methods in solving structures and to appreciate the complementary nature of the two methods in structural biology. In this review we provide some examples, and discuss how X-ray crystallography and cryo-EM can be combined in deciphering structures of macromolecules for our full understanding of their biological mechanisms.

Structure of the mouse Toll-like receptor 13 ectodomain in complex with a conserved sequence from bacterial 23S ribosomal RNA

Toll-like receptor 13 (TLR13) recognizes a conserved 10-nucleotide sequence from bacterial 23S ribosomal RNA, and binding of TLR13 to the target rRNA molecule triggers immune responses. Recently, the crystal structure of the TLR13 ectodomain bound by a 13-nucleotide single-stranded RNA (ssRNA13) was determined by using the initial phases provided by a cryo-electron microscopy map of TLR13 in complex with a 25-nucleotide ssRNA (ssRNA25). This structural snapshot describes a unique method for solving the crystal structure of the TLR13-ssRNA13 complex based on medium-resolution reconstruction of TLR13-ssRNA25 cryo-electron microscopy images.

X-ray structure determination using low-resolution electron microscopy maps for molecular replacement

Structures of multisubunit macromolecular machines are primarily determined either by electron microscopy (EM) or by X-ray crystallography. In many cases, a structure for a complex can be obtained at low resolution (at a coarse level of detail) with EM and at a higher resolution (with finer detail) by X-ray crystallography. The integration of these two structural techniques is becoming increasingly important for the generation of atomic models of macromolecular complexes. A low-resolution EM image can be a powerful tool for obtaining the 'phase' information that is missing from an X-ray crystallography experiment; however, integration of EM and X-ray diffraction data has been technically challenging. Here we present a step-by-step protocol that explains how low-resolution EM maps can be placed in the crystallographic unit cell by molecular replacement, and how initial phases computed from the placed EM density are extended to high resolution by averaging maps over noncrystallographic symmetry. As the resolution gap between EM and X-ray crystallography continues to narrow, the use of EM maps to help with X-ray crystal structure determination, as described in this protocol, will become increasingly effective.

Iterative projection algorithms in protein crystallography. II. Application

Iterative projection algorithms (IPAs) are a promising tool for protein crystallographic phase determination. Although related to traditional density-modification algorithms, IPAs have better convergence properties, and, as a result, can effectively overcome the phase problem given modest levels of structural redundancy. This is illustrated by applying IPAs to determine the electron densities of two protein crystals with fourfold non-crystallographic symmetry, starting with only the experimental diffraction amplitudes, a low-resolution molecular envelope and the position of the non-crystallographic axes. The algorithm returns electron densities that are sufficiently accurate for model building, allowing automated recovery of the known structures. This study indicates that IPAs should find routine application in protein crystallography, being capable of reconstructing electron densities starting with very little initial phase information.

Molecular replacement then and now

The `phase problem' in crystallography results from the inability to directly measure the phases of individual diffracted X-ray waves. While intensities are directly measured during data collection, phases must be obtained by other means. Several phasing methods are available (MIR, SAR, MAD, SAD and MR) and they all rely on the premise that phase information can be obtained if the positions of marker atoms in the unknown crystal structure are known. This paper is dedicated to the most popular phasing method, molecular replacement (MR), and represents a personal overview of the development, use and requirements of the methodology. The first description of noncrystallographic symmetry as a tool for structure determination was explained by Rossmann and Blow [Rossmann & Blow (1962), Acta Cryst. 15, 24-31]. The term `molecular replacement' was introduced as the name of a book in which the early papers were collected and briefly reviewed [Rossmann (1972), The Molecular Replacement Method. New York: Gordon & Breach]. Several programs have evolved from the original concept to allow faster and more sophisticated searches, including six-dimensional searches and brute-force approaches. While careful selection of the resolution range for the search and the quality of the data will greatly influence the outcome, the correct choice of the search model is probably still the main criterion to guarantee success in solving a structure using MR. Two of the main parameters used to define the `best' search model are sequence identity (25% or more) and structural similarity. Another parameter that may often be undervalued is the quality of the probe: there is clearly a relationship between the quality and the correctness of the chosen probe and its usefulness as a search model. Efforts should be made by all structural biologists to ensure that their deposited structures, which are potential search probes for future systems, are of the best possible quality.