A protease in the common commercial enzyme cleaner Zymit used for the cleaning of crystallization robots cannot be completely inactivated by EDTA.
The protease in the commonly used commercial low-foam enzyme cleaner Zymit cannot be completely blocked by EDTA, a widely used inhibitor of metalloproteases, at concentrations of up to 5 mM. Severe protein degradation was observed in crystallization drops after EDTA-containing wash steps unless residual Zymit protease was removed with NaOH at a concentration of at least 0.1 M. Wash steps with 0.1% SDS were also ineffective in completely removing the remaining Zymit activity. Protocols including wash steps with at least 0.1 M NaOH, as for example specified in the original ZENM protocol, are recommended to completely deactivate Zymit protease activity.
crystallization robotics; protease; protease inhibitor; Zymit; cleaning protocol
Models of the VL1 glycosylated Fab fragment independently refined from two non-apparent (pseudo) isomorphous crystals show significant differences, allowing the meaning of accuracy in structure description to be revisited, while at the same time inviting reflections about the benefits and boundaries of complex solvent modelling and validation.
The monoclonal antibody N14 is used as a detection antibody in ELISA kits for the human glycoprotein afamin, a member of the albumin family, which has recently gained interest in the capture and stabilization of Wnt signalling proteins, and for its role in metabolic syndrome and papillary thyroid carcinoma. As a rare occurrence, the N14 Fab is N-glycosylated at Asn26L at the onset of the VL1 antigen-binding loop, with the α-1–6 core fucosylated complex glycan facing out of the L1 complementarity-determining region. The crystal structures of two non-apparent (pseudo) isomorphous crystals of the N14 Fab were analyzed, which differ significantly in the elbow angles, thereby cautioning against the overinterpretation of domain movements upon antigen binding. In addition, the map quality at 1.9 Å resolution was sufficient to crystallographically re-sequence the variable VL and VH domains and to detect discrepancies in the hybridoma-derived sequence. Finally, a conservatively refined parsimonious model is presented and its statistics are compared with those from a less conservatively built model that has been modelled more enthusiastically. Improvements to the PDB validation reports affecting ligands, clashscore and buried surface calculations are suggested.
antibody fragment; flexibility; variable-chain glycosylation; elbow angle; precision; accuracy; solvent; non-apparent isomorphism; solvent modelling
X-ray crystallography provides the most accurate models of protein–ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein–ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein–ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein–ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein–ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein–ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein–ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.
Crystal structure; Protein structure; Protein–ligand complex; Quality control; Structure validation; Structure-based drug design
On average, the mother liquor or solvent and its constituents occupy about 50% of a macromolecular crystal. Ordered as well as disordered solvent components need to be accurately accounted for in modelling and refinement, often with considerable complexity.
The mother liquor from which a biomolecular crystal is grown will contain water, buffer molecules, native ligands and cofactors, crystallization precipitants and additives, various metal ions, and often small-molecule ligands or inhibitors. On average, about half the volume of a biomolecular crystal consists of this mother liquor, whose components form the disordered bulk solvent. Its scattering contributions can be exploited in initial phasing and must be included in crystal structure refinement as a bulk-solvent model. Concomitantly, distinct electron density originating from ordered solvent components must be correctly identified and represented as part of the atomic crystal structure model. Herein, are reviewed (i) probabilistic bulk-solvent content estimates, (ii) the use of bulk-solvent density modification in phase improvement, (iii) bulk-solvent models and refinement of bulk-solvent contributions and (iv) modelling and validation of ordered solvent constituents. A brief summary is provided of current tools for bulk-solvent analysis and refinement, as well as of modelling, refinement and analysis of ordered solvent components, including small-molecule ligands.
macromolecular crystals; solvent content; bulk solvent; ordered solvent
An understanding of protein stability is essential for optimizing the expression, purification and crystallization of proteins. In this review, discussion will focus on factors affecting protein stability on a somewhat practical level, particularly from the view of a protein crystallographer.
Protein stability is a topic of major interest for the biotechnology, pharmaceutical and food industries, in addition to being a daily consideration for academic researchers studying proteins. An understanding of protein stability is essential for optimizing the expression, purification, formulation, storage and structural studies of proteins. In this review, discussion will focus on factors affecting protein stability, on a somewhat practical level, particularly from the view of a protein crystallographer. The differences between protein conformational stability and protein compositional stability will be discussed, along with a brief introduction to key methods useful for analyzing protein stability. Finally, tactics for addressing protein-stability issues during protein expression, purification and crystallization will be discussed.
protein stability; protein crystallization; protein disorder; crystallizability
The dual role of the Protein Data Bank as a repository of all macromolecular structures and as the major source of structural metadata for further analysis is discussed and suggestions are made on how to identify models that contain errors and could potentially degrade the quality of meta analyses.
Whereas the vast majority of the more than 85 000 crystal structures of macromolecules currently deposited in the Protein Data Bank are of high quality, some suffer from a variety of imperfections. Although this fact has been pointed out in the past, it is still worth periodic updates so that the metadata obtained by global analysis of the available crystal structures, as well as the utilization of the individual structures for tasks such as drug design, should be based on only the most reliable data. Here, selected abnormal deposited structures have been analysed based on the Bayesian reasoning that the correctness of a model must be judged against both the primary evidence as well as prior knowledge. These structures, as well as information gained from the corresponding publications (if available), have emphasized some of the most prevalent types of common problems. The errors are often perfect illustrations of the nature of human cognition, which is frequently influenced by preconceptions that may lead to fanciful results in the absence of proper validation. Common errors can be traced to negligence and a lack of rigorous verification of the models against electron density, creation of non-parsimonious models, generation of improbable numbers, application of incorrect symmetry, illogical presentation of the results, or violation of the rules of chemistry and physics. Paying more attention to such problems, not only in the final validation stages but during the structure-determination process as well, is necessary not only in order to maintain the highest possible quality of the structural repositories and databases but most of all to provide a solid basis for subsequent studies, including large-scale data-mining projects. For many scientists PDB deposition is a rather infrequent event, so the need for proper training and supervision is emphasized, as well as the need for constant alertness of reason and critical judgment as absolutely necessary safeguarding measures against such problems. Ways of identifying more problematic structures are suggested so that their users may be properly alerted to their possible shortcomings.
macromolecular crystallography; model validation; Protein Data Bank
The structure factors deposited with PDB entry 3k78 show properties inconsistent with experimentally observed diffraction data, and without uncertainty represent calculated structure factors. The refinement of the 3k78 model against these structure factors leads to an isomorphous structure different from the deposited model with an implausibly small R value (0.019).
Physically improbable features in the model of the birch pollen structure Bet v 1d (PDB entry 3k78) are faithfully reproduced in electron density generated with the deposited structure factors, but these structure factors themselves exhibit properties that are characteristic of data calculated from a simple model and are inconsistent with the data and error model obtained through experimental measurements. The refinement of the 3k78 model against these structure factors leads to an isomorphous structure different from the deposited model with an implausibly small R value (0.019). The abnormal refinement is compared with normal refinement of an isomorphous variant structure of Bet v 1l (PDB entry 1fm4). A variety of analytical tools, including the application of Diederichs plots, Rσ plots and bulk-solvent analysis are discussed as promising aids in validation. The examination of the Bet v 1d structure also cautions against the practice of indicating poorly defined protein chain residues through zero occupancies. The recommendation to preserve diffraction images is amplified.
protein structure; Bet V 1 birch pollen allergen; Diederichs plot; validation; bulk-solvent correction; refinement statistics; intensity statistics
An introduction to the proceedings of the CCP4 Study Weekend held at the University of Warwick on the 6–7 January 2011.
CCP4 Study Weekend
Crystal and solution structures of Rv1848 protein and their implications in the biological assembly of Mtb urease is presented.
The crystal structure of the urease γ subunit (UreA) from Mycobacterium tuberculosis, Rv1848, has been determined at 1.8 Å resolution. The asymmetric unit contains three copies of Rv1848 arranged into a homotrimer that is similar to the UreA trimer in the structure of urease from Klebsiella aerogenes. Small-angle X-ray scattering experiments indicate that the Rv1848 protein also forms trimers in solution. The observed homotrimer and the organization of urease genes within the M. tuberculosis genome suggest that M. tuberculosis urease has the (αβγ)3 composition observed for other bacterial ureases. The γ subunit may be of primary importance for the formation of the urease quaternary structure.
Mycobacterium tuberculosis; urease; structural genomics
The prototype of a universal micromanipulation robot for crystal harvesting is presented, and a robotically harvested trypsin crystal yields a high-resolution structure demonstrating the feasibility of robotic protein crystal harvesting.
High-throughput crystallography has reached a level of automation where complete computer-assisted robotic crystallization pipelines are capable of cocktail preparation, crystallization plate setup, and inspection and interpretation of results. While mounting of crystal pins, data collection and structure solution are highly automated, crystal harvesting and cryocooling remain formidable challenges towards full automation. To address the final frontier in achieving fully automated high-throughput crystallography, the prototype of an anthropomorphic six-axis universal micromanipulation robot (UMR) has been designed and tested; this UMR is capable of operator-assisted harvesting and cryoquenching of protein crystals as small as 10 µm from a variety of 96-well plates. The UMR is equipped with a versatile tool exchanger providing full operational flexibility. Trypsin crystals harvested and cryoquenched using the UMR have yielded a 1.5 Å structure demonstrating the feasibility of robotic protein crystal harvesting.
automated crystal harvesting; crystal mounting; cryoprotection; trypsin; protease; benzamidine complex; protamine; intermolecular contacts; crystallization additives