A critical examination of the recently reported crystal structures of the human SMN protein

Using deep-learning predictions of inter-residue distances for model validation

Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org).

Sequence assignment for low-resolution modelling of protein crystal structures

The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

Findable Accessible Interoperable Re-usable (FAIR) diffraction data are coming to protein crystallography

The policy of IUCr Journals on diffraction data is defined.

The science is in the data

Understanding published research results should be through one's own eyes and include the opportunity to work with raw diffraction data to check the various decisions made in the analyses by the original authors. Today, preserving raw diffraction data is technically and organizationally viable at a growing number of data archives, both centralized and distributed, which are empowered to register data sets and obtain a preservation descriptor, typically a 'digital object identifier'. This introduces an important role of preserving raw data, namely understanding where we fail in or could improve our analyses. Individual science area case studies in crystallography are provided.

New developments in crystallography: exploring its technology, methods and scope in the molecular biosciences

Since the Protein Data Bank (PDB) was founded in 1971, there are now over 120,000 depositions, the majority of which are from X-ray crystallography and 90% of those made use of synchrotron beamlines. At the Cambridge Structure Database (CSD), founded in 1965, there are more than 800,000 'small molecule' crystal structure depositions and a very large number of those are relevant in the biosciences as ligands or cofactors. The technology for crystal structure analysis is still developing rapidly both at synchrotrons and in home labs. Determination of the details of the hydrogen atoms in biological macromolecules is well served using neutrons as probe. Large multi-macromolecular complexes cause major challenges to crystallization; electrons as probes offer unique advantages here. Methods developments naturally accompany technology change, mainly incremental but some, such as the tuneability, intensity and collimation of synchrotron radiation, have effected radical changes in capability of biological crystallography. In the past few years, the X-ray laser has taken X-ray crystallography measurement times into the femtosecond range. In terms of applications many new discoveries have been made in the molecular biosciences. The scope of crystallographic techniques is indeed very wide. As examples, new insights into chemical catalysis of enzymes and relating ligand bound structures to thermodynamics have been gained but predictive power is seen as not yet achieved. Metal complexes are also an emerging theme for biomedicine applications. Our studies of coloration of live and cooked lobsters proved to be an unexpected favourite with the public and schoolchildren. More generally, public understanding of the biosciences and crystallography's role within the field have been greatly enhanced by the United Nations International Year of Crystallography coordinated by the International Union of Crystallography. This topical review describes each of these areas along with illustrative results to document the scope of each methodology.

SMN - A chaperone for nuclear RNP social occasions?

Survival Motor Neuron (SMN) protein localizes to both the nucleus and the cytoplasm. Cytoplasmic SMN is diffusely localized in large oligomeric complexes with core member proteins, called Gemins. Biochemical and cell biological studies have demonstrated that the SMN complex is required for the cytoplasmic assembly and nuclear transport of Sm-class ribonucleoproteins (RNPs). Nuclear SMN accumulates with spliceosomal small nuclear (sn)RNPs in Cajal bodies, sub-domains involved in multiple facets of snRNP maturation. Thus, the SMN complex forms stable associations with both nuclear and cytoplasmic snRNPs, and plays a critical role in their biogenesis. In this review, we focus on potential functions of the nuclear SMN complex, with particular emphasis on its role within the Cajal body.

Resolving the cofactor-binding site in the proline biosynthetic enzyme human pyrroline-5-carboxylate reductase 1

Pyrroline-5-carboxylate reductase (PYCR) is the final enzyme in proline biosynthesis, catalyzing the NAD(P)H-dependent reduction of Δ1-pyrroline-5-carboxylate (P5C) to proline. Mutations in the PYCR1 gene alter mitochondrial function and cause the connective tissue disorder cutis laxa. Furthermore, PYCR1 is overexpressed in multiple cancers, and the PYCR1 knock-out suppresses tumorigenic growth, suggesting that PYCR1 is a potential cancer target. However, inhibitor development has been stymied by limited mechanistic details for the enzyme, particularly in light of a previous crystallographic study that placed the cofactor-binding site in the C-terminal domain rather than the anticipated Rossmann fold of the N-terminal domain. To fill this gap, we report crystallographic, sedimentation-velocity, and kinetics data for human PYCR1. Structures of binary complexes of PYCR1 with NADPH or proline determined at 1.9 Å resolution provide insight into cofactor and substrate recognition. We see NADPH bound to the Rossmann fold, over 25 Å from the previously proposed site. The 1.85 Å resolution structure of a ternary complex containing NADPH and a P5C/proline analog provides a model of the Michaelis complex formed during hydride transfer. Sedimentation velocity shows that PYCR1 forms a concentration-dependent decamer in solution, consistent with the pentamer-of-dimers assembly seen crystallographically. Kinetic and mutational analysis confirmed several features seen in the crystal structure, including the importance of a hydrogen bond between Thr-238 and the substrate as well as limited cofactor discrimination.

Structure determination of contaminant proteins using the MarathonMR procedure

In the recent decades, essential steps of protein structure determination such as phasing by multiple isomorphous replacement and multi wave length anomalous dispersion, molecular replacement, refinement of the structure determined and its validation have been fully automated. Several computer program suites that execute all these steps as a pipeline operation have been made available. In spite of these great advances, determination of a protein structure may turn out to be a challenging task for a variety of reasons. It might be difficult to obtain multiple isomorphous replacement or multi wave length anomalous dispersion data or the crystal may have defects such as twinning or pseudo translation. Apart from these usual difficulties, more frequent difficulties have been encountered in recent years because of the large number of projects handled by structural biologists. These new difficulties usually result from contamination of the protein of interest by other proteins or presence of proteins from pathogenic organisms that could withstand the antibiotics used to prevent bacterial contamination. It could also be a result of poor book keeping. Recently, we have developed a procedure called MarathonMR that has the power to resolve some of these problems automatically. In this communication, we describe how the MarathonMR was used to determine four different protein structures that had remained elusive for several years. We describe the plausible reasons for the difficulties encountered in determining these structures and point out that the method presented here could be a validation tool for protein structures deposited in the protein data bank.

ContaMiner and ContaBase: a webserver and database for early identification of unwantedly crystallized protein contaminants

Solving the phase problem in protein X-ray crystallography relies heavily on the identity of the crystallized protein, especially when molecular replacement (MR) methods are used. Yet, it is not uncommon that a contaminant crystallizes instead of the protein of interest. Such contaminants may be proteins from the expression host organism, protein fusion tags or proteins added during the purification steps. Many contaminants co-purify easily, crystallize and give good diffraction data. Identification of contaminant crystals may take time, since the presence of the contaminant is unexpected and its identity unknown. A webserver (ContaMiner) and a contaminant database (ContaBase) have been established, to allow fast MR-based screening of crystallographic data against currently 62 known contaminants. The web-based ContaMiner (available at http://strube.cbrc.kaust.edu.sa/contaminer/) currently produces results in 5 min to 4 h. The program is also available in a github repository and can be installed locally. ContaMiner enables screening of novel crystals at synchrotron beamlines, and it would be valuable as a routine safety check for 'crystallization and preliminary X-ray analysis' publications. Thus, in addition to potentially saving X-ray crystallographers much time and effort, ContaMiner might considerably lower the risk of publishing erroneous data.