Squeezing the most from every crystal: the fine details of data collection

Macromolecular room temperature crystallography

X-ray crystallography enables detailed structural studies of proteins to understand and modulate their function. Conducting crystallographic experiments at cryogenic temperatures has practical benefits but potentially limits the identification of functionally important alternative protein conformations that can be revealed only at room temperature (RT). This review discusses practical aspects of preparing, acquiring, and analyzing X-ray crystallography data at RT to demystify preconceived impracticalities that freeze progress of routine RT data collection at synchrotron sources. Examples are presented as conceptual and experimental templates to enable the design of RT-inspired studies; they illustrate the diversity and utility of gaining novel insights into protein conformational landscapes. An integrative view of protein conformational dynamics enables opportunities to advance basic and biomedical research.

Rational Drug Design Using Integrative Structural Biology

Modern drug discovery and design approaches rely heavily on high-throughput methods and state-of-the-art infrastructures with robotic facilities and sophisticated platforms. However, the anticipated research output that would eventually lead to new drugs with minimal or no side effects to the market has not been achieved. Despite the vast amount of information generated, very little is converted to knowledge and even less is capitalized for cross-discipline research actions. Therefore, the need for re-launching rational approaches has become apparent. Here we present an overview of the new trends in rational drug design using integrative structural biology with emphasis on X-ray protein crystallography and small molecules as ligands. With the aim to increase researchers' awareness on the available possibilities to perform front line research, we also underline the benefits and enhanced prospects offered to the scientific community, through access to research infrastructures.

On the state of crystallography at the dawn of the electron microscopy revolution

While protein crystallography has, for many years, been the most used method for structural analysis of macromolecular complexes, remarkable recent advances in high-resolution electron cryo-microscopy led to suggestions that 'the revolution will not be crystallised'. Here we highlight the current success rate, speed and ease of modern crystallographic structure determination and some recent triumphs of both 'classical' crystallography and the use of X-ray free electron lasers. We also outline fundamental differences between structure determination using X-ray crystallography and electron microscopy. We suggest that crystallography will continue to co-exist with electron microscopy as part of an integrated array of methods, allowing structural biologists to focus on fundamental biological questions rather than being constrained by the methods available.

The impact of structural genomics: the first quindecennial

The period 2000-2015 brought the advent of high-throughput approaches to protein structure determination. With the overall funding on the order of $2 billion (in 2010 dollars), the structural genomics (SG) consortia established worldwide have developed pipelines for target selection, protein production, sample preparation, crystallization, and structure determination by X-ray crystallography and NMR. These efforts resulted in the determination of over 13,500 protein structures, mostly from unique protein families, and increased the structural coverage of the expanding protein universe. SG programs contributed over 4400 publications to the scientific literature. The NIH-funded Protein Structure Initiatives alone have produced over 2000 scientific publications, which to date have attracted more than 93,000 citations. Software and database developments that were necessary to handle high-throughput structure determination workflows have led to structures of better quality and improved integrity of the associated data. Organized and accessible data have a positive impact on the reproducibility of scientific experiments. Most of the experimental data generated by the SG centers are freely available to the community and has been utilized by scientists in various fields of research. SG projects have created, improved, streamlined, and validated many protocols for protein production and crystallization, data collection, and functional analysis, significantly benefiting biological and biomedical research.

An overview of heavy-atom derivatization of protein crystals

Heavy-atom derivatization is one of the oldest techniques for obtaining phase information for protein crystals and, although it is no longer the first choice, it remains a useful technique for obtaining phases for unknown structures and for low-resolution data sets. It is also valuable for confirming the chain trace in low-resolution electron-density maps. This overview provides a summary of the technique and is aimed at first-time users of the method. It includes guidelines on when to use it, which heavy atoms are most likely to work, how to prepare heavy-atom solutions, how to derivatize crystals and how to determine whether a crystal is in fact a derivative.

Can I solve my structure by SAD phasing? Anomalous signal in SAD phasing

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. A simple theoretical framework for describing measurements of anomalous differences and the resulting useful anomalous correlation and anomalous signal in a SAD experiment is presented. Here, the useful anomalous correlation is defined as the correlation of anomalous differences with ideal anomalous differences from the anomalous substructure. The useful anomalous correlation reflects the accuracy of the data and the absence of minor sites. The useful anomalous correlation also reflects the information available for estimating crystallographic phases once the substructure has been determined. In contrast, the anomalous signal (the peak height in a model-phased anomalous difference Fourier at the coordinates of atoms in the anomalous substructure) reflects the information available about each site in the substructure and is related to the ability to find the substructure. A theoretical analysis shows that the expected value of the anomalous signal is the product of the useful anomalous correlation, the square root of the ratio of the number of unique reflections in the data set to the number of sites in the substructure, and a function that decreases with increasing values of the atomic displacement factor for the atoms in the substructure. This means that the ability to find the substructure in a SAD experiment is increased by high data quality and by a high ratio of reflections to sites in the substructure, and is decreased by high atomic displacement factors for the substructure.

Structural basis for histone H2B deubiquitination by the SAGA DUB module

Monoubiquitinated histone H2B plays multiple roles in transcription activation. H2B is deubiquitinated by the Spt-Ada-Gcn5 acetyltransferase (SAGA) coactivator, which contains a four-protein subcomplex known as the deubiquitinating (DUB) module. The crystal structure of the Ubp8/Sgf11/Sus1/Sgf73 DUB module bound to a ubiquitinated nucleosome reveals that the DUB module primarily contacts H2A/H2B, with an arginine cluster on the Sgf11 zinc finger domain docking on the conserved H2A/H2B acidic patch. The Ubp8 catalytic domain mediates additional contacts with H2B, as well as with the conjugated ubiquitin. We find that the DUB module deubiquitinates H2B both in the context of the nucleosome and in H2A/H2B dimers complexed with the histone chaperone, FACT, suggesting that SAGA could target H2B at multiple stages of nucleosome disassembly and reassembly during transcription.

Lessons from ten years of crystallization experiments at the SGC

Although protein crystallization is generally considered more art than science and remains significantly trial-and-error, large-scale data sets hold the promise of providing general learning. Observations are presented here from retrospective analyses of the strategies actively deployed for the extensive crystallization experiments at the Oxford site of the Structural Genomics Consortium (SGC), where comprehensive annotations by SGC scientists were recorded on a customized database infrastructure. The results point to the importance of using redundancy in crystallizing conditions, specifically by varying the mixing ratios of protein sample and precipitant, as well as incubation temperatures. No meaningful difference in performance could be identified between the four most widely used sparse-matrix screens, judged by the yield of crystals leading to deposited structures; this suggests that in general any comparison of screens will be meaningless without extensive cross-testing. Where protein sample is limiting, exploring more conditions has a higher likelihood of being informative by yielding hits than does redundancy of either mixing ratio or temperature. Finally, on the logistical question of how long experiments should be stored, 98% of all crystals that led to deposited structures appeared within 30 days. Overall, these analyses serve as practical guidelines for the design of initial screening experiments for new crystallization targets.

Assessing and maximizing data quality in macromolecular crystallography

The quality of macromolecular crystal structures depends, in part, on the quality and quantity of the data used to produce them. Here, we review recent shifts in our understanding of how to use data quality indicators to select a high resolution cutoff that leads to the best model, and of the potential to greatly increase data quality through the merging of multiple measurements from multiple passes of single crystals or from multiple crystals. Key factors supporting this shift are the introduction of more robust correlation coefficient based indicators of the precision of merged data sets as well as the recognition of the substantial useful information present in extensive amounts of data once considered too weak to be of value.