2.1. How to spot it

The most obvious sign of radiation decay during the experiment is the gradual fading of diffraction intensity on the raw images as they appear. However, if using a modern photon-counting detector (EIGER and PILATUS) and fine-φ (oscillation angle) slicing, for example φ < 0.2°, this can be challenging. These detectors differ substantially from the integrating charge-coupled device (CCD) detectors in that they convert X-rays directly into electronic charge, which is immediately processed electronically with no added noise (i.e. there is no `readout noise'). Individual photons are counted as they arrive instead of the charge being integrated for a certain time and then read out, and when there are no X-rays they register no background counts (i.e. they have no so-called `dark current'). When using these detectors, by the time that reflection-intensity fading becomes obvious to the experimenter, it is already too late to avoid deleterious effects on the data. This is due to a number of factors, including the sheer speed of data collection, which means it is not feasible to inspect and compare more than a few diffraction images. In addition, it is hard to judge the true resolution of the data by human eye due to the partial nature of individual reflection intensities per fine-sliced image, since the reflection intensity is split across multiple images. Crystals usually diffract better and can be processed to higher resolution than reflections can be seen on the images.

A better strategy is to monitor the automatic data-processing output as it appears, as well as keeping an eye on the display of the individual images. Signs of radiation decay can be identified during data collection from the per-image plots of the number of Bragg spots and the data resolution, and also from the scale and merging statistic graphs. Fig. 2(f) shows examples of `good' and `RD compromised' output from DIALS (Winter et al., 2022) as run within xia2. As the volume of the sample exposed to the beam changes during the goniometer rotation, the number of Bragg peaks oscillates and then returns (Fig. 2f, i) or does not return (Fig. 2f, ii) to the maximum as the sample becomes damaged. The scaling outputs for undamaged (Fig. 2g, i) and damaged (Fig. 2g, ii) crystals show similar oscillatory behaviour as the sample is rotated. The characteristic `W' shape mentioned above is clearly visible in Fig. 2(g)(ii) as the software attempts to optimize the scales for the majority of the 360° data sweep. Note the correlation between the scales and the number of reflections. R_merge is also displayed and its sharp increase near the end of the sweep is an obvious sign of significant RD having been suffered by the crystal.

2.2. What to do about it

What can an experimenter do to minimize the effects of RD in reciprocal space?

The obvious first step is to try cryocooling the crystal in liquid nitrogen at 77 K, if necessary soaking it first in cryoprotectant (Garman & Schneider, 1997; Pflugrath, 2015). Diffraction data are then collected with the crystal bathed in an open cold nitrogen stream. The temperature of the nitrogen should be held below 110 K to prevent the movement of most radical species produced by the absorption of X-rays, including hydroxyl radicals from the radiolysis of water. Above 110 K these latter radicals are thought to become mobile (Owen et al., 2012) and are the major cause of most RT damage since they are highly reactive and, if mobile, can initiate radiation chemistry reactions in the solvent channels (Southworth-Davies et al., 2007).

Cryocooling to prolong crystal lifetime has many advantages over RT data collection, including gentler mounting methods, lower background scattering and higher resolution data (since higher resolution reflections do not fade so fast); fewer crystals are required and crystals can easily be shipped in transport Dewars to synchrotrons and can be cryocooled when in peak condition (for example before crystal degradation or before a `skin' appears over the crystal drop which can indicate the formation of oxidized or denatured protein at the air–drop interface) for later data collection. However, it is clear that structures determined at 100 K can lack some relevant structural conformations that only occur at RT (Fraser et al., 2011), so if these are important to answer the biological question at hand then RT data collection should be carried out. For a recent review discussing the practical aspects of RT data collection and giving information on data-analysis strategies, see Fischer (2021).

If cryocooled crystals show RD and crystal supply is not an issue, data can be collected from multiple crystals (at cryo-temperature or RT) to reduce the dose absorbed by individual crystals. Excellent software allowing small sections of data or even individual images to be efficiently combined together is now available (for example xia2.multiplex in DIALS; Gildea et al., 2022) and makes multiple crystal data collection to obtain one complete data set a realistic option. An extreme application of this is expedited at X-ray free-electron laser (XFEL) sources, where a series of X-ray pulses each lasting a few tens of femtoseconds hits a moving stream of RT crystals supplied to the beam (Barends et al., 2022) and `diffraction before destruction' (Chapman et al., 2014) usually allows RD-free data to be collected if the pulse is short enough (Nass et al., 2020). In fact, the so-called `serial data collection' method is increasingly being employed for both structure solution and time-resolved studies (Pearson & Mehrabi, 2020) and is also employed with sample holders consisting of chips holding thousands of crystals which are moved across the beam (see, for example, Owen et al., 2017). This modality is likely to grow in popularity with the advent of more fourth-generation sources providing even higher X-ray flux densities.

A very important method to avoid RD is to adopt a data-collection strategy that takes into account the dose absorbed by the samples using the characteristics of the beamline. Several utilities are now available to estimate the dose absorbed during a data collection so that a collection strategy that keeps the dose as low as possible for the whole data set can be adopted. These tools include DOZOR at the Massively Automated Sample Selection Integrated Facility (MASSIF-1) beamline of the European Synchrotron Radiation Facility (Svensson et al., 2015), RADDOSE-3D (Zeldin, Gerstel et al., 2013; Bury et al., 2018) and James Holton's `expected crystal lifetime calculator' (https://bl831.als.lbl.gov/xtallife.html). In fact, RADDOSE-3D is now available on several beamlines (for example I04 at Diamond Light Source and all of the MX beamlines at the Stanford Synchrotron Radiation Lightsource) so that exposures can be collected for a specified absorbed dose value rather than for a specified time.

Some rules of thumb born out of experience suggest that if phases are not required, a reasonable data-collection strategy would be to spread a maximum of 10–15 MGy dose over 360–720° in rotation angle, but to not exceed 7 MGy if the crystal diffracts to 1.4 Å resolution or better. If anomalous data are being collected to obtain phase information, however, doses should be kept much lower. Above all, the temptation to increase the exposure time or to reduce the beam attenuation and hence increase the dose should be resisted.

If the crystallization buffer contains any heavier elements such as arsenic (which is present in cacodylate), it can be well worth back-soaking the crystals in a buffer containing lighter elements in which the crystals are stable before cryocooling them or irradiating them at RT. This reduces the absorption coefficient of the crystal and thus lowers the dose for the same irradiation regime, giving more time for data collection before the dose limit is reached and the effects of damage become evident.

Another way to reduce the rate of radiation damage is to use higher incident X-ray energies, as suggested by Nave & Hill (2005). As the X-ray energy increases, photoelectric cross sections decrease. In addition, there is a higher probability that photoelectrons can escape, especially from microcrystals, and thus not contribute to the absorbed dose. With the recent availability of new CdTe pixel detectors that are able to detect higher energy X-rays with good efficiency, this idea has been experimentally validated (Storm et al., 2020, 2021). The observed improvement in diffraction efficiency (the total number of elastically scattered photons/absorbed dose in MGy) was in line with earlier Monte Carlo simulation predictions (Dickerson & Garman, 2019), and the resolution to which data could be collected for the same dose was better.

If the data-processing statistics show a loss of high-resolution reflections, it is well worth reprocessing the images after cutting out the damaged ones, provided that the data completeness will not suffer too much by doing so. Removing compromised data can lead to substantial improvements in the final electron-density map (see Section 4).

3.1. What is specific damage?

Specific damage affects the individual copies of the asymmetric unit, inducing both chemical and structural changes. At cryo-temperatures, specific damage artefacts in protein crystal structures have been observed to occur in a reproducible order with increasing dose (Ravelli & McSweeney, 2000; Burmeister, 2000; Weik et al., 2000). Firstly, metal ions and cofactors are reduced, at doses as low as tens or hundreds of kGy (Beitlich et al., 2007; Corbett et al., 2007; Horrell et al., 2016; Ueno et al., 2019), before disulfide bonds start to be reduced at doses of approximately 0.5 MGy (Sutton et al., 2013; De la Mora et al., 2020). Aspartate and glutamate side chains are then decarboxylated, typically at around doses of 3–4 MGy (Fioravanti et al., 2007; Bury et al., 2018). Additional specific RD artefacts include cleavage of the methylthio/methylseleno group from methionine/selenomethionine side chains (Holton, 2007; Bury et al., 2015), plus conformational disordering of side chains such as tyrosine, lysine and histidine (Yabukarski et al., 2022). Moreover, local environment factors such as solvent accessibility, conformational strain, location at an active site and location at a crystal contact have all been found to influence the susceptibility of a residue to these various specific damage artefacts (Dubnovitsky et al., 2005; Fioravanti et al., 2007; Holton, 2009; Gerstel et al., 2015; Bhattacharyya et al., 2020). Fig. 3(a) shows examples of some of these damage artefacts in electron-density maps, whilst Fig. 3(b) shows an example case in which a disulfide bond has been modelled in both oxidized and reduced conformations to account for RD.

Figure 3 (a) Examples of (i) disulfide-bond cleavage, (ii) glutamate decarboxylation and (iii) methionine methylthio group disordering in an Fobs(late) − Fobs(early) difference map calculated for a structure of Torpedo californica acetylcholinesterase (PDB entry 1qid; Weik et al., 2000). The maps are contoured at ±4σ, with positive and negative difference density coloured green and red, respectively. This figure was adapted from Bury, Carmichael et al. (2016). (b) A disulfide bond modelled in both oxidized and reduced conformations to account for radiation damage in a structure of the tumour necrosis factor protein BAFF bound to a bhpBR3 peptide (PDB entry 3v56; Smart et al., 2012). The oxidized and reduced conformers of the cysteine side chain that undergoes a large conformational change are indicated with arrows. The 2mFobs − DFcalc map (blue) is contoured at 1.5 r.m.s.d.; the Fobs − Fcalc difference density map is contoured at ± 3.0 r.m.s.d., with positive and negative density coloured green and red, respectively. (c) A representation of the specific damage suffered by low-dose and high-dose data sets collected for the C.Esp1396 protein in complex with its target DNA sequence. Damage artefacts are represented as spheres: the spheres are coloured blue and red depending on whether they are within 2 Å of or further than 2 Å from the DNA, respectively. The radius of each sphere is proportional to the electron-density loss (electrons per Å3). This figure was adapted from Bury et al. (2015). All data leading to these structures were collected at 100 K.

At cryo-temperatures, the onset of specific damage has been observed at much lower doses than that of global damage (Teng & Moffat, 2000; Owen et al., 2006; Gotthard et al., 2019; De la Mora et al., 2020). It is therefore possible to collect a data set that contains none of the pathologies described in the preceding section on global damage, yet still contains specific damage artefacts. Conversely, at RT the onset of specific and global damage seems to be less decoupled, although the precise order of onset remains under debate (Roedig et al., 2016; Gotthard et al., 2019; De la Mora et al., 2020). The reason for this difference is that in cryocooled crystals, only the free electrons (plus the propagating holes they leave behind) resulting from inelastic scattering, and also perhaps hydrogen atoms, are able to move (primarily by quantum-mechanical tunnelling) and thus damage the crystal. In contrast, at RT the free-radical species, which are predominantly hydroxyl radicals generated from the radiolysis of water, and solvated electrons produced are able to move as well (Jones et al., 1987; Symons, 1995; Zakurdaeva et al., 2005; Garman, 2010; Owen et al., 2012).

Despite these differences in damage mechanism between cryo-temperature and room temperature, the specific damage artefacts observed at both temperatures are broadly similar. Metal-ion and cofactor reduction, as well as disulfide reduction, have been observed in multiple RT radiation-damage studies (Helliwell, 1988; Southworth-Davies et al., 2007; Ebrahim et al., 2019; De la Mora et al., 2020). Furthermore, the onset of these artefacts is typically observed at lower doses in RT compared with cryo-temperature data sets. The onset of cofactor damage has been observed at tens of kGy in RT data collection (Ebrahim et al., 2019); this is similar to the doses at which cofactor damage has been detected at cryo-temperatures (Ueno et al., 2019), but results from the fact that reducing the dose below a few kGy whilst still collecting a complete, high-resolution data set is currently impractical. However, at RT the onset of disulfide-bond damage has been observed at doses as low as 10 kGy (De la Mora et al., 2020), although notably there have been considerable discrepancies between the doses at which disulfide damage has been reported in different studies, with one study of insulin detecting no disulfide damage at doses as high as 500 kGy at RT (Roedig et al., 2016).

As yet, aspartate and glutamate decarboxylation has not been observed in RT data sets. It is hypothesized that this results from the fact that the onsets of global and specific damage are observed at more similar doses at RT, and so by the time that aspartate and glutamate residues begin to be decarboxylated the crystal lattice has degraded sufficiently such that diffraction data can no longer be collected. Likewise, side-chain disordering due to RD has also not been observed at RT. Consequently, RT data sets capture the conformational heterogeneity of protein side chains much better than cryo-temperature data sets, in which side-chain conformations are known to be affected by both the low temperature and RD (Fraser et al., 2011; Russi et al., 2017; Yabukarski et al., 2022).

In addition to proteins, nucleic acids are also known to suffer X-ray-induced radiation damage. However, to date only a small number of studies have examined the specific RD suffered by nucleic acids during MX data collection. Consequently, if an equivalent hierarchy of specific damage artefacts exists for nucleic acids as for proteins, it has not yet been established. Nonetheless, there is consensus between studies that nucleic acids are less susceptible to RD than proteins at cryo-temperatures (Fig. 3c): the onset of specific damage artefacts is observed at a higher dose for nucleic acids when comparing nucleic acid and protein crystals subjected to the same experimental conditions (Bugris et al., 2019) and in crystals of protein–nucleic acid complexes (Bury et al., 2015; Bury, McGeehan et al., 2016). These studies, in combination with theoretical and experimental radiation chemistry studies of nucleic acids in solution, also suggest that the sugar-phosphate backbone is more susceptible to RD than the bases (Sanche, 2005; Simons, 2007; Ptasińska & Sanche, 2007; Bury et al., 2015; Bury, McGeehan et al., 2016; Bugris et al., 2019). More research is required, however, to determine the specific RD artefacts suffered by nucleic acids and their relative order (if any) of susceptibility in cryo-temperature and especially in RT MX studies.

Specific RD artefacts can be difficult to distinguish from biologically relevant structural features. In particular, RD can make it very difficult to accurately determine the structure of a redox-sensitive cofactor, which is often highly important when deducing the mechanism of action of the protein or other macromolecule to which it is bound. Several recent studies have analysed the X-ray-induced changes to cofactors that occur, even when the crystal sample is cryocooled, at doses of just tens of kGy (Ueno et al., 2019; Zárate-Romero et al., 2019; Pfanzagl et al., 2020; Tandrup et al., 2022). Consequently, it is often informative to use complementary techniques, such as online UV–Vis or Raman spectroscopy, to track changes in the excitation state, redox state and/or structure of cofactors during X-ray diffraction data collection (Cohen et al., 2016; Gotthard et al., 2019). X-ray free-electron lasers (XFELs) are also becoming increasingly popular for the collection of damage-free diffraction data from macromolecular crystals that are particularly sensitive to RD (Hirata et al., 2014; Halsted et al., 2019; Rose et al., 2021): notably, however, RD has been observed in XFEL structures collected using longer and/or more intense pulses (Nass et al., 2015, 2020; Galli et al., 2015; Dickerson et al., 2020).

3.2. How to identify specific damage artefacts in your data

Given the aforementioned difficulties in distinguishing specific damage artefacts from structurally relevant features, it is important for crystallographers to be aware of the confounding effects that RD can have on MX structures. To help with this, several programs have been released to enable users to identify specific RD artefacts within their own data and those of others. In order to distinguish RD from other artefacts, these programs require as input a model that fits well overall to its corresponding electron-density map and hence to the diffraction data. Accordingly, these programs should only be run towards the end of refinement.

Programs written to detect conformational heterogeneity/mismodelling have proven to be useful in the detection of conformational disorder induced by radiation damage in 2mF_obs – DF_calc maps (Lang et al., 2010; Yabukarski et al., 2022) and using atomic B-factor values (Masmaliyeva et al., 2020). However, the most common method of detecting specific RD artefacts is to measure differences between successive diffraction data sets collected from the same crystal(s). Owing to the amount of time required to collect data, it used to be the case that unless they were studying radiation damage, a crystallographer would not usually collect more degrees of data than were required for a complete data set. However, the greatly increased speed of data collection enabled by the new generation of pixel detectors has allowed the collection of 360° of data to become standard practice, meaning that most data sets now contain a minimum of twice the amount of data required for completeness.

To calculate a difference map, a crystallographer requires a model that has been well refined to a data set A of scaled and merged reflections, plus a second data set B of scaled and merged reflections (which may overlap with/be a subset of data set A). As an example, data set A could correspond to the complete 360° of data collected, whilst data set B could correspond to the first 180°. A difference map is then calculated by subtracting the electron-density map calculated for the model and data set B from the map calculated for the model and data set A. This difference map can be inspected for the specific damage artefacts illustrated in Fig. 3(a): disulfide-bond reduction, for instance, is characterized by negative difference density around the bond between the two sulfurs (and sometimes in higher resolution maps positive difference density can indicate where one or both of the cysteine side chains have moved following bond cleavage if they have adopted defined conformations), whilst aspartate and glutamate decarboxylation is characterized by negative difference density around the side-chain carboxyl group.

As its name suggests, a difference map enables a researcher to measure the difference in damage between two data sets. Consequently, in the example above, the difference map calculated by subtracting data set B from data set A allows the identification of damage accrued in the second 180° of data collection; however, it does not provide information about any damage that may have occurred during the first 180° of data collection. Nevertheless, since nowadays most crystallo­graphers use 360° of data for structure solution, a difference map calculated against the first X° of data (where the minimum angle for a complete data set ≤ X < 360°) is a useful tool to check that the 360° of data that they are using does not contain damaged images. If the difference map does show evidence of damaged residues, however, this only tells the crystallographer that the first X° of data are less damaged than the subsequent (360 − X)° of data; it does not indicate whether the first X° of data contain damage artefacts or not.

A range of software now exists to allow the calculation and inspection of difference maps. The autoPROC software (Vonrhein et al., 2011), an automated pipeline for processing diffraction data from raw images into scaled and merged intensity values, has recently been updated to include calculation of F_obs(early) and F_obs(late) data sets (Vonrhein et al., 2024). The F_obs(early) and F_obs(late) data sets comprise images collected at the beginning and end, respectively, of the entire data collection, with each data set incorporating the minimum number of images required for completeness. This approach maximizes the distance in image space (and thus in dose) between the two data sets and hence maximizes the ease of detection of any RD artefacts that are acquired during the entire data collection. The BUSTER model-refinement pipeline (Bricogne et al., 2023) can subsequently combine these data sets with a refined model to generate an F_obs(early) − F_obs(late) difference map for the user to inspect. Note that the difference maps presented in RD studies are typically F_obs(late) − F_obs(early) maps, hence damage artefacts in F_obs(early) − F_obs(late) maps display the inverse difference density changes to those shown in Fig. 3(a): i.e. in an F_obs(early) − F_obs(late) map a damaged disulfide bond will show positive difference density around the sulfur–sulfur bond and negative density where the sulfurs have moved to, and so forth. AutoPROC and BUSTER are both available as part of the CCP4 software suite (Agirre et al., 2023), as well as being available to download from the Global Phasing website (https://www.globalphasing.com).

A disadvantage of manual map inspection is that it introduces human bias into the detection and quantification of damage artefacts. It can also be intractable for very large proteins. To overcome these challenges, the RIDL software was written to systematically inspect difference density maps for RD artefacts (Bury & Garman, 2018). RIDL takes as input two scaled and merged data sets collected from the same crystal(s), plus a well refined model, from which it calculates a difference map. The program then divides the map into voxels and identifies the voxels within a sphere of radius r around each atom, with the value of r being determined from the B-factor value of the atom (Fig. 4a). The D_neg metric for atom j is calculated as the weighted average of the negative difference density across its corresponding sphere of voxels (voxels with positive difference density are discarded from the calculation), with the negative difference density measured at each voxel being weighted by how much the density of atom j contributes to the total density measurement in an F_calc map (Fig. 4a). Users can then rank atoms by their D_neg values to enable unbiased, rapid inspection of the difference map for RD artefacts. Fig. 4 shows examples of the RIDL output for xylose isomerase (Fig. 4b; Taberman et al., 2019) and a DNA 16-mer (Fig. 4c; Bugris et al., 2019), highlighting damage to the active site of the protein and to the sugar-phosphate backbone, respectively. RIDL is available on GitHub (https://github.com/GarmanGroup/RIDL).

Figure 4 Identifying damage in Fobs(late) − Fobs(early) difference maps with RIDL. (a) RIDL calculates difference maps and divides them into voxels. The program measures the damage to atom j by first identifying the voxels within a radius rj of atom j and then calculating the Dneg metric (as well as several other metrics). ρΔ(v) and ρcalc(v) are the density values at voxel v in an Fobs(late) − Fobs(early) map and a map calculated directly from Fcalc, respectively, whilst refers to all of the voxels with negative difference density in the Fobs(late) − Fobs(early) map within a radius rj of atom j. This figure was adapted from Bury et al. (2018). (b) Difference density maps produced by RIDL for a low-dose and a high-dose data set collected from a xylose isomerase crystal (PDB entry 6qrr; Taberman et al., 2019); positive/negative difference density (contoured at ±3σ) is coloured green/red and shown separately in the top/bottom images for clarity. The xylose isomerase active site is shown, demonstrating the accrual of radiation damage in the higher relative to the lower dose data set. This figure was adapted from Taberman et al. (2019). (c) Difference density maps produced by RIDL for four data sets of increasing dose collected from a crystal of a DNA 16-mer (PDB entry 6qt1; Bugris et al., 2019). Maps are contoured at ±3σ, with positive/negative density coloured green/red. Arrows indicate examples of sites where damage accumulates with increasing dose; damage artefacts are predominantly localized around the sugar-phosphate backbone. This figure was adapted from Bugris et al. (2019). All data leading to the structures presented in (b) and (c) were collected at 100 K.

There are several advantages in using difference maps to identify RD artefacts accrued within a data set. One of these is that difference maps can be calculated for data sets collected at any temperature; likewise, they can be calculated for a data set collected from any crystal species, irrespective of whether the crystal is a protein, a nucleic acid or another type of molecule. Accordingly, difference maps are the method of choice for researchers studying specific RD in MX.

There are, however, some drawbacks to difference maps. As described above, they can only be calculated for data sets that include more images than are required for completeness, and they are used to identify damage accrued between the F_obs(late) and F_obs(early) data sets; they do not provide information about the extent to which the F_obs(early) data set itself is damaged (if at all). Furthermore, difference map calculation requires access either to two different scaled and merged data sets or to the full unmerged data set: this is straightforward when a crystallographer is examining their own data, but is often not the case when examining the data of others. Since February 2008, crystallographers have been required to submit their scaled and merged reflection data alongside their refined MX model when depositing a crystal structure in the Protein Data Bank (PDB; wwPDB, 2007). However, they have the choice of whether they also submit their unmerged data (wwPDB Consortium, 2019). Most users just submit the merged data set, which precludes difference-map calculation. It is therefore not possible to run difference-map calculation software to assess all MX models in the PDB (Berman et al., 2003) for damage artefacts.

Whilst only a minority of structures are deposited with the necessary data for difference-map calculation, in contrast every structure is deposited with an atomic coordinate model. In this model, B-factor values record the uncertainty in the coordinates of each atom. Consequently, B-factor values contain information about RD artefacts: when an atom is damaged it moves, increasing the uncertainty in its position and hence also increasing its B-factor value. However, B factors are affected by numerous other variables, in particular conformational heterogeneity between the asymmetric unit copies in the crystal, which prevents B-factor values from being used as a direct readout of RD (Fig. 5a).

Figure 5 Identifying specific radiation damage with the BDamage, Bnet and Bnet-percentile metrics in cryo-temperature protein crystal structures. (a) The BDamage metric identifies atoms with high B-factor values relative to other atoms in a similar local packing-density environment in the crystal. (i) Definition of the BDamage metric. (ii) Box plots of the isotropic atomic B-factor and BDamage values for glutamate side-chain O atoms in low-dose and high-dose data sets collected from lysozyme and ribonuclease A (PDB entries 2blx, 2bly, 2blp and 2blz; Nanao et al., 2005). Boxes represent the median and interquartile range (IQR), outliers are represented as black dots, and whiskers represent the range (excluding any outliers). This figure was adapted from Gerstel et al. (2015). (b) The Bnet metric identifies structures whose aspartate and glutamate side-chain O atoms have high BDamage values in comparison to the rest of the structure. Bnet is calculated by plotting a kernel density estimate (KDE) plot of the BDamage values of aspartate and glutamate side-chain O atoms. The area under the curve is calculated to the left (A) and right (B) of the median BDamage value of all atoms in the structure; Bnet is then calculated by dividing B by A. (i) Definition of the Bnet metric; KDE plots demonstrating the Bnet calculation are shown for (ii) a low-dose structure and (iii) a high-dose structure (PDB entries 5mcc and 5mcn, respectively; Bury et al., 2017). (iv) Bnet is correlated with dose. The plot shows Bnet values calculated for nine radiation-damage series (PDB codes and references are provided in Shelley & Garman, 2022). These figures were adapted from Shelley & Garman (2022). (c) wwPDB validation statistics, models and density maps for two of the most damaged cryo-temperature protein crystal structures deposited in the PDB, as determined using the Bnet and Bnet-percentile metrics. 2mFobs − DFcalc maps (blue) are contoured at 1.5 r.m.s.d.; Fobs − Fcalc difference density maps are contoured at ±3.0 r.m.s.d., with positive and negative density coloured green and red, respectively. This figure was adapted from Shelley & Garman (2022).

The conformational heterogeneity of an atom is correlated with its packing density (the number of atoms in its local environment; Weiss, 2007). Gerstel and coworkers found that when they corrected B-factor values for packing density, the resulting `B_Damage' values of known sites of specific damage were correlated with dose (Fig. 5a). B_Damage is calculated as the ratio of the isotropic B-factor value of an atom to the mean average isotropic B factor of the other atoms in the structure that are identified to be in a similar local packing-density environment (Gerstel et al., 2015). Accordingly, the B_Damage metric identifies the atoms within a model that have the highest B-factor values in relation to their packing density. If a structure is damaged the atoms with highest B_Damage values will be sites known to be susceptible to damage, such as disulfide-bond sulfurs and glutamate/aspartate side-chain carboxyl groups. Conversely, if it is undamaged these sites will not be overrepresented amongst the atoms with the highest B_Damage values. B_Damage values can be calculated for MX structures collected at any temperature. Note, however, that because it is a B-factor-derived metric, B_Damage values should not be calculated for components whose occupancy (across all modelled conformers) could be less than one, such as small-molecule ligands.

A drawback of B_Damage values is that owing to the variability in the relationship between B factor and dose, B_Damage values cannot be fairly compared between different MX structures. The B_net metric was therefore developed to enable this comparison (Shelley & Garman, 2022). To achieve this, the B_net calculation plots the B_Damage values of the aspartate and glutamate side-chain O atoms of a protein alongside the median B_Damage value of all atoms in the model: the larger the values of the former relative to the latter, the greater the damage suffered by the structure (Fig. 5b, i–iii). Since B_net uses damage to aspartate and glutamate side chains to summarize the extent of damage suffered by a model, it is consequently only suitable for assessing damage to protein structures collected at cryo-temperatures. In addition, for reliable results the analysis should only be carried out for models that contain more than ten glutamate/aspartate residues in the asymmetric unit.

B_net has been validated to be correlated with dose (Fig. 5b, iv) plus found to be independent of the majority of variables other than dose that can affect B-factor values. However, B_net was found to be correlated with resolution and hence the B_net-percentile metric was defined: B_net-percentile is calculated as the percentile ranking of the B_net value of a model within the subset of models derived from cryo-data sets which are closest in resolution (the 1000 structures closest in resolution in the PDB are first identified, and subsequently all structures falling within this resolution range are included in the percentile calculation). B_net-percentile is not correlated with resolution, and hence is the recommended metric for damage comparison between structures.

The B_net and B_net-percentile metrics were used to analyse damage to 93 978 cryo-temperature protein crystal structures deposited in the PDB (Berman et al., 2003) and PDB-REDO (van Beusekom et al., 2018) repositories as of 19th November 2020. This study identified numerous damaged structures (Fig. 5c), including several that are so damaged that the artefacts that are typically observed in difference maps were detectable in their individual F_obs − F_calc maps (damage artefacts are usually obscured in these maps due to additional noise; Shelley & Garman, 2022).

From this analysis, a B_net-percentile value of 95 was found to correspond to a B_net value of approximately 3.0. These values are suggested as general thresholds above which a cryo-temperature protein crystal structure should be inspected carefully for RD artefacts: structures with B_net-percentile values above 95 are amongst the top 5% most damaged cryo-structures in the PDB. However, the B_net and B_net-percentile values that are acceptable to a user will depend on the application for which they are intending to use a structure. As an example, an analysis of dose versus B_net for 15 RD data-set series has revealed that a dose of 3 MGy, the dose around which the onset of aspartate/glutamate side-chain damage is observed, corresponds to B_net-percentile and B_net values of approximately 85 and 2.5, respectively. A crystallographer wanting to draw conclusions about the apparent conformational heterogeneity of a catalytic aspartate residue might therefore wish to study structures with metric values below these thresholds. Researchers studying a redox-sensitive cofactor would need to select even lower thresholds: in this case it would be advisable to use complementary methods such as online UV–Vis or Raman spectroscopy to check for damage to the cofactor during data collection.

The B_Damage, B_net and B_net-percentile metrics can be calculated using the RABDAM software (Shelley et al., 2018). RABDAM is available on GitHub (https://github.com/GarmanGroup/RABDAM) and is distributed as part of CCP4 (Agirre et al., 2023). B_net values will also soon be reported in the PDB-REDO database (van Beusekom et al., 2018).

If a crystallographer detects specific damage in their structure, they can often remove these artefacts by excluding images collected at the end of the data set – whilst maintaining sufficient completeness for structure solution – and then re-refining their model to this truncated data set. Damaged images can be identified using the metrics described in Section 2.2; however, since these metrics detect global damage, sometimes it may also be necessary to discard apparently undamaged images in order to remove specific damage artefacts. In many cases, this truncation will substantially improve the quality of the refined maps. Occasionally, though, it will not prove possible to remove all damaged images whilst maintaining sufficient completeness, in which case it would be advisable to collect a new data set.