Markus Gerstel

RADDOSE-3D: time- and space-resolved modelling of dose in macromolecular crystallography

RADDOSE-3D allows the macroscopic modelling of an X-ray diffraction experiment for the purpose of better predicting radiation-damage progression. The distribution of dose within the crystal volume is calculated for a number of iterations in small angular steps across one or more data collection wedges, providing a time-resolved picture of the dose state of the crystal. The code is highly modular so that future contributions from the community can be easily integrated into it, in particular to incorporate online methods for determining the shape of macromolecular crystals and better protocols for imaging real experimental X-ray beam profiles.

Optimizing the spatial distribution of dose in X-ray macromolecular crystallography

X-ray data collection for macromolecular crystallography can lead to highly inhomogeneous distributions of dose within the crystal volume for cases when the crystal is larger than the beam or when the beam is non-uniform (gaussian-like), particularly when crystal rotation is fully taken into account. Here the spatial distribution of dose is quantitatively modelled in order to compare the effectiveness of two dose-spreading data-collection protocols: helical scanning and translational collection. Their effectiveness in reducing the peak dose per unit diffraction is investigated via simulations for four common crystal shapes (cube, plate, long and short needles) and beams with a wide range of full width half maximum values. By inspection of the chosen metric, it is concluded that the optimum strategy is always to use as flat (top-hat) a beam as possible and to either match the beam size in both dimensions to the crystal, or to perform a helical scan with a beam which is narrow along the rotation axis and matched to the crystal size along the perpendicular axis. For crystal shapes where this is not possible, the reduction in peak dose per unit diffraction achieved through dose spreading is quantified and tabulated as a reference for experimenters.

Identifying and quantifying radiation damage at the atomic level

A metric indicating the relative level of specific radiation damage for individual atoms, that can be calculated from refined and deposited protein structure models, is presented.

DIALS: implementation and evaluation of a new integration package

A new X-ray diffraction data-analysis package is presented with a description of the algorithms and examples of its application to biological and chemical crystallography.

RNA protects a nucleoprotein complex against radiation damage

Systematic analysis of radiation damage within a protein–RNA complex over a large dose range (1.3–25 MGy) reveals significant differential susceptibility of RNA and protein. A new method of difference electron-density quantification is presented.

Treatment of X-ray diffraction data at Diamond Light Source

In any experimental discipline, raw data represents the source from which all discoveries are derived. A more strict interpretation in X-ray diffraction experiments may refer to this as primary data since any pixel counts will have been manipulated (e.g. analogue to digital conversion, dark current correction, interpolation of pixels etc.); however the fundamental idea remains: this is the closest it is possible to get to the original experimental measurements.

BDamage: Quantifying radiation damage in MX structures

Radiation damage is a limiting factor in macromolecular X-ray crystallography diffraction experiments. Global damage leads to a unit cell size increase and non-isomorphism, and to a loss of long range crystal order which is visible in the decay of the diffraction pattern and loss of high resolution information during data collection. Specific damage causes detectable changes at particularly susceptible sites in the protein structure [1,2], such as the reduction of metallo-centres, elongation and subsequent breaking of disulphide bonds and decarboxylation of aspartate and glutamate residues. Between and within these groups the decay does not happen uniformly at equal rates throughout the protein, leading to preferential specific damage. Specific damage can result in misleading biological conclusions on protein mechanism and function being drawn. We have defined a new atom-specific metric, BDamage, which facilitates the identification of protein regions susceptible to specific radiation damage as well as the quantification of the susceptibility, allowing further investigations into preferential specific damage. BDamage has been validated using a paired set of lowdose/high-dose protein structures [3]. Results show that BDamage successfully separates susceptible residues from stable parts of the protein. A non-redundant subset of previously refined structures submitted to the PDB was then analyzed for indications of specific radiation damage. BDamage indicates that the distribution of specific damage is independent of secondary protein structure or disulphide bond configuration, but shows a correlation with solvent accessibility. Results indicate a possible use of BDamage as a quality control metric for structure submission. Further research into an alternative quantification of real-space specific radiation damage, using the decay of electron density over multiple datasets, is outlined.

Quantitative radiation damage studies in macromolecular X-ray crystallography

Radiation damage is a limiting factor in macromolecular X-ray crystallography diffraction experiments. Global damage leads to loss of long range crystal order, decay of the diffraction pattern and loss of high resolution information and non-isomorphism. Specific damage causes detectable changes in the protein structure [13], such as the reduction of metallo-centres, breaking of disulphide bonds, decarboxylation of aspartate and glutamate residues, and leads to misleading biological conclusions on protein mechanism and function being drawn. Two different approaches for quantifying radiation damage in MX are presented: a statistical analysis of the Protein Data Bank (PDB) and the tracking of electron density throughout a crystallographic experiment. The first approach applies a statistical model defining specific damage by changes in the distribution of relative atomic B-factors stratified by the packing density of the corresponding residue in the protein environment. This model was tested against a paired set of good/damaged protein structures [4]. A nonredundant subset of previously refined structures submitted to the PDB was then analyzed for indications of specific radiation damage. Results suggest that the distribution of specific damage is independent of secondary protein structure, solvent accessibility, protein residue count and isulphide bond configuration. There appears to be some correlation between the damage metric and the types of neighbouring amino acids. The power of an analysis of refined structures for signs of radiation damage is inherently limited: during refinement strong predictors of specific damage, such as the bond length of disulphides [3], will be optimized towards undamaged, theoretical values. The second approach to quantify radiation damage observes per-residue damage metrics based on the decay of real space electron density obtained from consecutive data sets compared to the first data set of the same protein crystal. The validity of the method is demonstrated by reanalyzing the specific damage data set of Torpedo californica acetylcholinesterase obtained by Weik et al. [1]. In our experiments we used human signaling protein inhibitor (rhoGDI) mutants [5] obtained by surface-entropy reduction [6]. These mutants have very high sequence identity, but, having different crystal contacts, readily crystallize in different space groups. Specific damage progression across different rhoGDI mutants can be compared by tracking the spatial dose distribution within each crystal and thus establishing the corresponding dose of the data sets using the newly available RADDOSE-3D [7].