Analysis of metal-protein interaction distances, coordination numbers, B-factors (displacement parameters), and occupancies of metal binding sites in protein structures determined by X-ray crystallography and deposited in the PDB shows many unusual values and unexpected correlations. By measuring the frequency of each amino acid in metal ion binding sites, the positive or negative preferences of each residue for each type of cation were identified. Our approach may be used for fast identification of metal-binding structural motifs that cannot be identified on the basis of sequence similarity alone. The analysis compares data derived separately from high and medium resolution structures from the PDB with those from very high resolution small-molecule structures in the Cambridge Structural Database (CSD). For high resolution protein structures, the distribution of metal-protein or metal-water interaction distances agrees quite well with data from CSD, but the distribution is unrealistically wide for medium (2.0 -2.5 Å) resolution data. Our analysis of cation B-factors versus average B-factors of atoms in the cation environment reveals substantial numbers of structures contain either an incorrect metal ion assignment or an unusual coordination pattern. Correlation between data resolution and completeness of the metal coordination spheres is also found.

Metals play vital roles in both the mechanism and architecture of biological macromolecules. Yet structures of metal-containing macromolecules where metals are misidentified and/or suboptimally modeled are abundant in the Protein Data Bank (PDB). This shows the need for a diagnostic tool to identify and correct such modeling problems with metal binding environments. The "CheckMyMetal" (CMM) web server (http://csgid.org/csgid/metal_sites/) is a sophisticated, user-friendly web-based method to evaluate metal binding sites in macromolecular structures in respect to 7350 metal binding sites observed in a benchmark dataset of 2304 high resolution crystal structures. The protocol outlines how the CMM server can be used to detect geometric and other irregularities in the structures of metal binding sites and alert researchers to potential errors in metal assignment. The protocol also gives practical guidelines for correcting problematic sites by modifying the metal binding environment and/or redefining metal identity in the PDB file. Several examples where this has led to meaningful results are described in the anticipated results section. CMM was designed for a broad audience—biomedical researchers studying metal-containing proteins and nucleic acids—but is equally well suited for structural biologists to validate new structures during modeling or refinement. The CMM server takes the coordinates of a metal-containing macromolecule structure in the PDB format as input and responds within a few seconds for a typical protein structure modeled with a few hundred amino acids.

Metals are essential in many biological processes, and metal ions are modeled in roughly 40% of the macromolecular structures in the Protein Data Bank (PDB). However, a significant fraction of these structures contain poorly modeled metal-binding sites. CheckMyMetal (CMM) is an easy-to-use metal-binding site validation server for macromolecules that is freely available at http://csgid.org/ csgid/metal_sites. The CMM server can detect incorrect metal assignments as well as geometrical and other irregularities in the metal-binding sites. Guidelines for metal-site modeling and validation in macromolecules are illustrated by several practical examples grouped by the type of metal. These examples show CMM users (and crystallographers in general) problems they may encounter during the modeling of a specific metal ion.

The ubiquitous presence of magnesium ions in RNA has long been recognized as a key factor governing RNA folding, and is crucial for many diverse functions of RNA molecules. In this work, Mg2+-binding architectures in RNA were systematically studied using a database of RNA crystal structures from the Protein Data Bank (PDB). Due to the abundance of poorly modeled or incorrectly identified Mg2+ ions, the set of all sites was comprehensively validated and filtered to identify a benchmark dataset of 15 334 ‘reliable’ RNA-bound Mg2+ sites. The normalized frequencies by which specific RNA atoms coordinate Mg2+ were derived for both the inner and outer coordination spheres. A hierarchical classification system of Mg2+ sites in RNA structures was designed and applied to the benchmark dataset, yielding a set of 41 types of inner-sphere and 95 types of outer-sphere coordinating patterns. This classification system has also been applied to describe six previously reported Mg2+-binding motifs and detect them in new RNA structures. Investigation of the most populous site types resulted in the identification of seven novel Mg2+-binding motifs, and all RNA structures in the PDB were screened for the presence of these motifs.

The low reproducibility of published experimental results in many scientific disciplines has recently garnered negative attention in scientific journals and the general media. Public transparency, including the availability of `raw' experimental data, will help to address growing concerns regarding scientific integrity. Macromolecular X-ray crystallography has led the way in requiring the public dissemination of atomic coordinates and a wealth of experimental data, making the field one of the most reproducible in the biological sciences. However, there remains no mandate for public disclosure of the original diffraction data. The Integrated Resource for Reproducibility in Macromolecular Crystallography (IRRMC) has been developed to archive raw data from diffraction experiments and, equally importantly, to provide related metadata. Currently, the database of our resource contains data from 2920 macromolecular diffraction experiments (5767 data sets), accounting for around 3% of all depositions in the Protein Data Bank (PDB), with their corresponding partially curated metadata. IRRMC utilizes distributed storage implemented using a federated architecture of many independent storage servers, which provides both scalability and sustainability. The resource, which is accessibleviathe web portal at http://www.proteindiffraction.org, can be searched using various criteria. All data are available for unrestricted access and download. The resource serves as a proof of concept and demonstrates the feasibility of archiving raw diffraction data and associated metadata from X-ray crystallographic studies of biological macromolecules. The goal is to expand this resource and include data sets that failed to yield X-ray structures in order to facilitate collaborative efforts that will improve protein structure-determination methods and to ensure the availability of `orphan' data left behind for various reasons by individual investigators and/or extinct structural genomics projects.

Metals have crucial roles in many physiological, pathological, toxicological, pharmaceutical, and diagnostic processes. Proper handling of metal-containing macromolecule samples for structural studies is not trivial, and failure to handle them properly is often a source of irreproducibility caused by issues such as pH changes, incorporation of unexpected metals, or oxidization/reduction of the metal. This protocol outlines the guidelines and best practices for characterizing metal-binding sites in protein structures and alerts experimenters to potential pitfalls during the preparation and handling of metal-containing protein samples for X-ray crystallography studies. The protocol features strategies for controlling the sample pH and the metal oxidation state, recording X-ray fluorescence (XRF) spectra, and collecting diffraction data sets above and below the corresponding metal absorption edges. This protocol should allow experimenters to gather sufficient evidence to unambiguously determine the identity and location of the metal of interest, as well as to accurately characterize the coordinating ligands in the metal binding environment within the protein. Meticulous handling of metal-containing macromolecule samples as described in this protocol should enhance experimental reproducibility in biomedical sciences, especially in X-ray macromolecular crystallography. For most samples, the protocol can be completed within a period of 7-190 d, most of which (2-180 d) is devoted to growing the crystal. The protocol should be readily understandable to structural biologists, particularly protein crystallographers with an intermediate level of experience.

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A nonredundant set of 9081 protein crystal structures in the Protein Data Bank was used to examine the solvent content, the number of polypeptide chains, and the oligomeric states of proteins in crystals as a function of crystal symmetry (as classified by crystal systems and space groups). It was found that there is a correlation between solvent content and crystal symmetry. Surprisingly, proteins crystallizing in lower symmetry systems have lower solvent content compared to those crystallizing in higher symmetry systems. Nevertheless, there is no universal correlation between solvent content and preferences of macromolecules to crystallize in certain space groups. Crystal symmetry as a function of oligomeric state was examined, where trimers, tetramers, and hexamers were found to prefer to crystallize in systems where the oligomer symmetry could be incorporated in the crystal symmetry. Our analysis also shows that the frequency distribution within the enantiomorphous pairs of space groups does not differ significantly, in contrast to previous reports.Keywords: solvent content; Matthews coefficient; protein crystals; oligomerization; space group frequency Supplemental material: see www.proteinscience.orgWater plays an important role in the structure of biomolecules and often influences protein function. Water molecules not only affect protein folding, but also mediate biological processes such as enzymatic reactions and molecular recognition. Information about the fraction of water (solvent) plays a significant role in the X-ray structure determination process. First, knowledge of the solvent content helps to determine the number of molecules in the asymmetric unit (Matthews 1968), which is crucial in early stages of crystal structure determination. Second, an approximate value of solvent content is needed for significant phase improvement by solvent flattening methods (Wang 1985;Leslie 1987;Abrahams and Leslie 1996), which is necessary to resolve the inherent phase ambiguity in single anomalous diffraction (SAD) experiments. For both SAD and MAD (multiwavelength anomalous diffraction) (Hendrickson 1991;Hendrickson et al. 1990), phase improvement by solvent flattening is critical for low resolution data (Kirillova et al. 2007), especially when non-crystallographic symmetry cannot be applied.Matthews (1968) observed that the solvent content in protein crystals ranged from 27% to 65%, with an average of 43%. He also showed that the quantity V M (the Matthews coefficient, defined as the ratio of the volume of the asymmetric unit to the molecular weight of all