Crystal transport

One practical benefit of cryogenic methods becomes immediately apparent when pre-grown crystals need to be transported to a synchrotron – we have to find an alternative to the convenience of sending stably frozen crystals in a cryogenic shipping container. A few popular choices for sizeable (macro) crystals include classic capillary mounting (King, Reference King1954; Basavappa et al., Reference Basavappa, Petri and Tolbert2003) (Fig. 2c), using pins that hold pre-mounted crystals (Fig. 2b), or juggling crystals to the synchrotron in their crystallization tray. Note that pre-mounted crystals protected by a thin-walled polyester sleeve (MiTeGen) with stabilizing solution (Fig. 2b) should be sent via ground transit because pressure changes during a flight can have undesirable effects, including dislodging the sleeve that protects crystals from dehydration. To prevent splashing in crystal trays upfront, it is worth exploring if crystallization is transferable to an in situ crystallization tray format. As the name indicates, in situ plates allow the screening of crystals without removing them from the tray. The In Situ-1^TM plate (MiTeGen) allows crystals to be grown, transported and collected directly in the plate. Given its construction, the In Situ-1^TM plate promises optional long-distance shipping with reduced cross-contamination of fluid from reservoirs to the protein growth areas due to micro ledges that are absent in conventional plates (Fig. 2d). Alternatively, the Stanford Synchrotron Radiation Lightsource (SSRL) Crystallization Plate Kit (Crystal Positioning Systems; MiTeGen) allows in situ crystallization of proteins on substrates affixed to magnetic sample pin bases (Martiel et al., Reference Martiel, Müller-Werkmeister and Cohen2019). This setup enables the transport of crystals mounted in loops, grids or capillaries in a controlled humidity environment to the synchrotron beamline for robotic sample mounting. As three-dimensional (3D) printers become more accessible, custom 3D printed trays provide a viable alternative (Monteiro et al., Reference Monteiro, Von Stetten, Stohrer, Sans, Pearson, Santoni, Van Der Linden and Trebbin2020).

Fig. 2.

BEFORE – Preparing macroscopic crystals for room temperature data collection. (a) Mother liquor (dark grey oval) surrounding a crystal in a large drop of oil (light grey box) is sequentially removed, leading to a visually ‘disappearing’ crystal due to the similar refractive indices of protein crystal and oil. (b) A mounted crystal is covered by a thin-walled polyester sleeve that contains a stabilizing solution. (c) In classic capillary mounting, the experimenter tries to achieve a solvent meniscus to hold the protein crystal in place. (d) MiTeGen's In Situ-1^TM plate has microchannels and ledges that facilitate growing, shipping, and in situ data collection of crystals. A picture of the SSRL crystallization plate setup (Crystal Positioning Systems) can be found in the supplementary information of Martiel et al. (Reference Martiel, Müller-Werkmeister and Cohen2019). For sample delivery methods of microcrystals that enable serial synchrotron crystallography see the section ‘sample delivery for serial crystallography’.

If crystallization and travel kinetics are favorable, setting trays up locally is another option. However, this comes at the risk of wasting precious beamtime if crystallization does not turn out to be reproducible onsite. While this review focuses on macro-crystals of >100 μm, transferable sample delivery methods for microcrystals are emerging from efforts at X-ray free-electron laser facilities (XFEL). The required slew of microcrystals can simply be transported in a test tube to a suitable microfocus beamline that is equipped with a sample delivery option – reviewed below in the section ‘sample delivery for serial crystallography’.

Mitigating dehydration

Capillaries

Capillaries are an alternative method to prevent crystal dehydration. Traditional mounting of crystals in borosilicate glass or quartz capillaries is remarkably watertight but can be difficult to master (King, Reference King1954; Basavappa et al., Reference Basavappa, Petri and Tolbert2003). The mechanical stress of mounting a flat crystal onto the curved inner surface of a capillary is also not to be underestimated. Curvature incompatibilities were avoided in a contraption that resembles the ‘grandfather’ of the MiTeGen sleeve, which was used to study the stress-induced modulation of the soybean lipoxygenase L3 structure (Skrzypczak-Jankun et al., Reference Skrzypczak-Jankun, Bianchet, Mario Amzel and Funk1996). However, the biggest disadvantage of quartz and glass capillary mounts is absorption. Per unit volume, glass scatters and absorbs roughly 10× that of the protein crystal itself. Therefore, for a 100 μm crystal, a 10 μm glass window starts to dominate the noise in the experiment. MiTeGen has now largely supplanted this method with a handy polyester tubing (Kalinin et al., Reference Kalinin, Kmetko, Bartnik, Stewart, Gillilan, Lobkovsky and Thorne2005) (Fig. 2b). Polyester, and indeed most plastics, scatter and absorb about as much as the same volume of protein, so the trade-off in thickness is much more forgiving. To stabilize fragile crystals, approximately 10 μl of environment-stabilizing solution (mother liquor or well solution) is injected into the sealed end of the 25 μm transparent polyester tube by using a gel-loading tip (Fig. 3). In fact, vapor diffusion can be used favorably to equilibrate the humidity inside the tube by using a mix that is compatible with the protein, such as 80% mother liquor to 20% water. The tube can be cut to size with a razor blade (it does not shatter like 10 μm glass capillaries) while reducing background scatter by about 60%. The crystal is then harvested with a loop that matches its size (Fig. 3a). Excess liquid is removed and the polyester tubing is drawn over the pin that holds the crystal, either with a steady hand or with the help of the MicroRT^TM Aligner (MiTeGen). The base can be additionally sealed with Dow Corning High-Vacuum Grease, which mitigates most evaporation except the approximately 80 nL h⁻¹ that evaporates through the thin polyester tubing.

Fig. 3.

DURING – Considerations for collecting room temperature data. (a) At RT, a protein crystal is mounted in a size-matched loop and protected from dehydration by a sleeve filled with reservoir solution. To reduce radiation damage, data are collected with an attenuated beam and potentially from different parts of the crystal using a helical scan or stepwise increments; small-molecule scavengers may help protect the crystal from radicals formed during data collection. (b) The crystal volume doubles with each increase in crystal size indicated on the x-axis for a cubic crystal; e.g. a crystal of 101 × 101 × 101 μm has twice the volume of a crystal of 80 × 80 × 80 μm. Note that in practice such crystals are often visually indistinguishable.

Data collection on bare crystals without protective sleeve shielding may be facilitated by humidity-control devices such as the original Proteros Free Mounting System (Kiefersauer et al., Reference Kiefersauer, Than, Dobbek, Gremer, Melero, Strobl, Dias, Soulimane and Huber2000). To address the problem of dehydration, several European synchrotrons have formed a collaboration to implement a designated humidity-control device – the HC1 Dehydration Device (Sanchez-Weatherby et al., Reference Sanchez-Weatherby, Bowler, Huet, Gobbo, Felisaz, Lavault, Moya, Kadlec, Ravelli and Cipriani2009; Russi et al., Reference Russi, Juers, Sanchez-Weatherby, Pellegrini, Mossou, Forsyth, Huet, Gobbo, Felisaz, Moya, McSweeney, Cusack, Cipriani and Bowler2011). The commercial version HC-Lab Humidity Controller (Arinax) is used across many European synchrotrons and at the SSRL (Stanford, USA). Although initially used for controlled crystal dehydration, humidity control devices were adopted to replace the cryojet during RT data collection. Excess mother liquor around crystals that are harvested onto MicroMeshes (MiTeGen) can be wicked away from the other side of the mesh support, even when they are mounted in the humidity stream. At the bench, the humid-air and glue-coating method (Baba et al., Reference Baba, Shimada, Mizuno, Baba, Ago, Yamamoto and Kumasaka2019) and MiTeGen's commercial solution, Watershed^TM, provide custom benchtop workstations to prevent drop dehydration during handling.

Avoiding cryoprotectants is another benefit of RTX data collection. Cryoprotectants often need to be optimized to obtain high-resolution data (Juers et al., Reference Juers, Farley, Saxby, Cotter, Cahn, Holton-Burke, Harrison and Wu2018). At high concentrations, fragment-sized cryoprotectants (like glycerol) are often found in protein structures. In a cautionary tale, Aggarwal et al. (Reference Aggarwal, Boone, Kondeti, Tu, Silverman and Mckenna2013) investigated the effect of cryoprotectants on carbonic anhydrase II. They found that, although neither protein thermostability nor the inhibitor binding are affected, glycerol does affect the kinetics of carbonic anhydrase II, presumably by displacing active-site solvent.

Reducing radiation damage

The other major crystal killer is radiation damage. At RT, radiation damage is time-dependent and mainly depends on flux density (Southworth-Davies et al., Reference Southworth-Davies, Medina, Carmichael and Garman2007; Warkentin et al., Reference Warkentin, Badeau, Hopkins and Thorne2011, Reference Warkentin, Hopkins, Badeau, Mulichak, Keefe and Thorne2013). Dark progression, i.e., the progression of damage to the crystal after the X-rays have been turned off (Blundell and Johnson, Reference Blundell and Johnson1976), was observed at temperatures above 180 K on timescales of 3–20 min (Warkentin et al., Reference Warkentin, Badeau, Hopkins and Thorne2011). The use of small-molecule radical scavengers, such as substituted benzoquinones [see (Allan et al., Reference Allan, Kander, Carmichael and Garman2013) for an extensive list], can mitigate radiation damage during data collection (De la Mora et al., Reference De la Mora, Carmichael and Garman2011) (Fig. 3). Unfortunately, none of these protective effects is very large. An improvement by a factor of two or more would be enough to overcome sample-to-sample variability and inspire a change in data collection strategy, but a reproducible demonstration of such a large radiation protection factor has yet to be reported. Exceptions to this rule are ultrafast X-ray pulses produced at XFELs where a diffraction pattern is produced before the structure can react to radiation exposure (Chapman, Reference Chapman2019). At RT the experimental limit of radiation damage is typically less than 400 kGy (Schubert et al., Reference Schubert, Kapis, Gicquel, Bourenkov, Schneider, Heymann, Betzel and Perbandt2016; De la Mora et al., Reference De la Mora, Coquelle, Bury, Rosenthal, Holton, Carmichael, Garman, Burghammer, Colletier and Weik2020), which is about one to two orders of magnitude lower than at cryo where the calculated Henderson limit is 20 MGy (Henderson, Reference Henderson1990; Nave and Garman, Reference Nave and Garman2005; Garman and Owen, Reference Garman and Owen2006) and the upper experimental limit is 30 MGy (Owen et al., Reference Owen, Rudiño-Piñera and Garman2006). In practice, values vary considerably depending on the diffraction limit and chemical composition of the sample and dose rate (Teng and Moffat, Reference Teng and Moffat2000; Liebschner et al., Reference Liebschner, Rosenbaum, Dauter and Dauter2015; Atakisi et al., Reference Atakisi, Conger, Moreau and Thorne2019; Ebrahim et al., Reference Ebrahim, Moreno-Chicano, Appleby, Chaplin, Beale, Sherrell, Duyvesteyn, Owada, Tono, Sugimoto, Strange, Worrall, Axford, Owen and Hough2019b). A more elaborate crystal lifetime estimator, RADDOSE-3D, that considers some of these factors are presented in the section ‘optimizing data collection parameters’. Considering a practical RT limit of 400 kGy provides a rough guideline for expected crystal lifetime, required beam attenuation during RT data collection, or whether data merging to improve data completeness may be advisable (see the section ‘collecting and merging data from multiple crystals’). Typically, R _merge values of less than 10% indicate that damage-induced changes are significantly smaller than the modeling error represented by R _work and R _free (Fraser et al., Reference Fraser, Van Den Bedem, Samelson, Lang, Holton, Echols and Alber2011).

Optimizing data collection parameters

During data collection, it is key to strike the balance between crystal lifetime and crystal diffraction. Intense beams produce more intense spots at high resolution but kill crystals faster. In turn, crystals survive much longer in attenuated beams but may not reach their full diffractive potential. An ideal data collection strategy maximizes diffraction to high resolution at high redundancy before the crystal suffers from radiation damage. In practice, the best way to approach this optimum is through trial and error, and focusing on a couple of parameters that increase the odds of squeezing the best data out of the crystal (Krojer et al., Reference Krojer, Pike and Von Delft2013). First, match the crystal to the beam size or vice versa (i.e., adjust the beam aperture) to optimize the signal-to-noise ratio. Generally, exposing a larger crystal volume to the same X-ray dose fares better because of the number of molecules exposed to the beam scales with volume – by the third power of its linear size (Fig. 3b). For example, the volume, and hence the number of useful photons, double for two visually indistinguishable crystals of 80 and 101 μm (Holton, Reference Holton2009).

Second, the beam can be attenuated by various means. The simplest way is to add an aluminum filter that attenuates the beam to a set percentage and extends crystal lifetime. By using attenuation, diffraction data can be collected at a slower rate for the same X-ray dose, which has the advantage of reducing the impact of time-dependent errors, such as beam flicker, shutter jitter, and the approximately 2 ms read-out gap between images of a PILATUS (Dectris) detector. It is important to familiarize oneself with the X-ray beam properties at each beamline. The beamline scientists should be able to provide information about the X-ray dose at different energies and beam sizes. Top-off mode of modern synchrotron sources, shutterless data collection with Pixel Array Detectors (PAD) and reduced dark time of faster PADs such as the EIGER or PILATUS (Dectris) enable faster data collection rates with good statistics (Rajendran et al., Reference Rajendran, Dworkowski, Wang and Schulze-Briese2011; Owen et al., Reference Owen, Paterson, Axford, Aishima, Schulze-Briese, Ren, Fry, Stuart and Evans2014). Fine-slicing data collection (Pflugrath, Reference Pflugrath1999; Krojer et al., Reference Krojer, Pike and Von Delft2013) reduces spot overlap and allows more precise truncation of data once datasets fall below an acceptable threshold for radiation damage and resolution. If needed, a beamline scientist can advise how to explore other methods of reducing flux density that can improve the beam characteristics and hence data quality. These include the following: lowering the divergence will make the spots on the detector smaller and sharper; lowering the dispersion or bandpass can improve high-angle spot shape; and de-focusing the beam will lower the dose profile across the crystal face and minimize lattice strain propagating to nearby areas. The RADDOSE-3D calculator can help to predict the lifetime of protein crystals (Zeldin et al., Reference Zeldin, Brockhauser, Bremridge, Holton and Garman2013a) and estimate the effective X-ray dose the crystal has endured during data collection (Zeldin et al., Reference Zeldin, Gerstel and Garman2013b; Bury et al., Reference Bury, Brooks-Bartlett, Walsh and Garman2018). Ideally, a ‘top hat’ beam profile enables homogeneous sample exposure and better assessment of the damage. Recently, the B _Damage metric was introduced to identify potential sites of specific damage by comparing the atomic B-factors of atoms with a similar local packing density environment (Gerstel et al., Reference Gerstel, Deane and Garman2015). For guidance on radiation damage that informs the practicalities of data collection, consult Holton's review and references therein (Holton, Reference Holton2009).

Third, to counter fading peak intensities, consider collecting data from different parts of the crystal by translating (manually or automatically in segments) or helically scanning the crystal along the goniometer axis (Fig. 3). Grid scans on crystals have indicated that crystal quality is often heterogeneous (Thompson et al., Reference Thompson, Cascio and Yeates2018). Collecting data from multiple crystals is another option discussed below.

Collecting and merging data from multiple crystals

Another prevailing RTX myth is that many crystals are needed to collect one complete dataset at RT. However, we routinely collect single crystal RTX datasets without radiation damage impeding electron density map interpretation or resulting in a dramatic decay in resolution (Fischer et al., Reference Fischer, Shoichet and Fraser2015). For samples where crystal damage due to dehydration or radiation damage is unavoidable, combining partial data from multiple crystals is an option. To obtain a dataset with desirable redundancy and completeness, multiple ‘wedges’ of data collected from often random orientations of multiple isomorphous crystals need to be merged. Redundancy increases accuracy, whereas completeness is needed for reliable model building and refinement. Since all reflections contribute to the electron density map, incomplete data lead to poor map quality (Wlodawer et al., Reference Wlodawer, Minor, Dauter and Jaskolski2008). While spot indexing and integration usually work well if a sufficient number of spots with high signal-to-noise ratios are recorded, merging can be challenging. Luckily, RTX data are usually more isomorphous than cryogenic datasets (Giordano et al., Reference Giordano, Leal, Bourenkov, McSweeney and Popov2012), leading to better statistics and final models (Fischer et al., Reference Fischer, Shoichet and Fraser2015). Manual merging can be tedious as it requires human intervention by cycling between integrated and scaled data and monitoring the merging statistics to decide which data to include or exclude (Evans, Reference Evans2006). The BLEND algorithm uses clustering based on unit cell parameters to facilitate the selection of optimal groups of multi-crystal data for scaling to achieve high completeness (Foadi et al., Reference Foadi, Aller, Alguel, Cameron, Axford, Owen, Armour, Waterman, Iwata and Evans2013). When executed in ‘graphics mode’, annotated dendrograms with merging statistics can be displayed and inspected. Excluding the last recorded segments of the data that have suffered obvious radiation damage will help improve merging statistics and success. Conveniently, BLEND is also integrated into DIALS (Winter et al., Reference Winter, Waterman, Parkhurst, Brewster, Gildea, Gerstel, Fuentes-Montero, Vollmar, Michels-Clark, Young, Sauter and Evans2018; Beilsten-Edmands et al., Reference Beilsten-Edmands, Winter, Gildea, Parkhurst, Waterman and Evans2020), a collaborative software suite that provides a framework to automate processing diffraction images into MTZ files. Another program that uses hierarchical clustering from multi-crystal experiments is ccCluster, which also features an interactive graphical user interface for the analysis of dendrograms (Santoni et al., Reference Santoni, Zander, Mueller-Dieckmann, Leonard and Popov2017). Local scaling, dataset weighting, and merging data from multiple data files are also implemented in the phenix.scale_and_merge tool (Akey et al., Reference Akey, Terwilliger and Smith2016) within Phenix (Liebschner et al., Reference Liebschner, Afonine, Baker, Bunkóczi, Chen, Croll, Hintze, Hung, Jain, Mccoy, Moriarty, Oeffner, Poon, Prisant, Read, Richardson, Richardson, Sammito, Sobolev, Stockwell, Terwilliger, Urzhumtsev, Videau, Williams and Adams2019). Finally, it is worth noting that the nature of the intensity decay is different at cryo versus RT (De la Mora et al., Reference De la Mora, Coquelle, Bury, Rosenthal, Holton, Carmichael, Garman, Burghammer, Colletier and Weik2020). At cryo, high-resolution reflection intensities decay first as lattice strain increases. At RT all intensities appear to fade at once as the crystal lattice transitions to non-crystallinity, Wilson B-factors increase, and scattering power is lost (Leal et al., Reference Leal, Bourenkov, Russi and Popov2013). Specific and global radiation damage are less decoupled at RT than at cryo, where specific damage affects certain amino acids (Gotthard et al., Reference Gotthard, Aumonier, De Sanctis, Leonard, Von Stetten and Royant2019).

Sample delivery for serial crystallography

While this review focuses on collecting data on larger crystals, an emerging RTX trend is serial crystallography in which thousands of diffraction images are collected on microcrystals (Beale et al., Reference Beale, Bolton, Marshall, Beale, Carr, Ebrahim, Moreno-Chicano, Hough, Worrall, Tews and Owen2019). Exposing each crystal only once mitigates radiation damage. In addition to the benefit of an undistorted RT image, time-resolved crystallography (Moffat, Reference Moffat1989) allows dynamic insights into protein function. Using XFELs, ‘molecular movies’ can be recorded with femtosecond time resolution (Chapman et al., Reference Chapman, Fromme, Barty, White, Kirian, Aquila, Hunter, Schulz, Deponte, Weierstall, Doak, Maia, Martin, Schlichting, Lomb, Coppola, Shoeman, Epp, Hartmann, Rolles, Rudenko, Foucar, Kimmel, Weidenspointner, Holl, Liang, Barthelmess, Caleman, Boutet, Bogan, Krzywinski, Bostedt, Bajt, Gumprecht, Rudek, Erk, Schmidt, Hömke, Reich, Pietschner, Strüder, Hauser, Gorke, Ullrich, Herrmann, Schaller, Schopper, Soltau, Kühnel, Messerschmidt, Bozek, Hau-Riege, Frank, Hampton, Sierra, Starodub, Williams, Hajdu, Timneanu, Seibert, Andreasson, Rocker, Jönsson, Svenda, Stern, Nass, Andritschke, Schröter, Krasniqi, Bott, Schmidt, Wang, Grotjohann, Holton, Barends, Neutze, Marchesini, Fromme, Schorb, Rupp, Adolph, Gorkhover, Andersson, Hirsemann, Potdevin, Graafsma, Nilsson and Spence2011; Chapman, Reference Chapman2019). However, with limited XFEL beamtime available, following dynamics at synchrotrons remains a viable alternative, while other options like microcrystal electron diffraction have not reached mainstream maturity (Wolff et al., Reference Wolff, Young, Sierra, Brewster, Martynowycz, Nango, Sugahara, Nakane, Ito, Aquila, Bhowmick, Biel, Carbajo, Cohen, Cortez, Gonzalez, Hino, Im, Koralek, Kubo, Lazarou, Nomura, Owada, Samelson, Tanaka, Tanaka, Thompson, Van Den Bedem, Woldeyes, Yumoto, Zhao, Tono, Boutet, Iwata, Gonen, Sauter, Fraser and Thompson2020).

At synchrotron sources with monochromatic beams, the time resolution is currently limited to millisecond timescales (Stellato et al., Reference Stellato, Oberthür, Liang, Bean, Gati, Yefanov, Barty, Burkhardt, Fischer, Galli, Kirian, Meyer, Panneerselvam, Yoon, Chervinskii, Speller, White, Betzel, Meents and Chapman2014; Nogly et al., Reference Nogly, James, Wang, White, Zatsepin, Shilova, Nelson, Liu, Johansson, Heymann, Jaeger, Metz, Wickstrand, Wu, Båth, Berntsen, Oberthuer, Panneels, Cherezov, Chapman, Schertler, Neutze, Spence, Moraes, Burghammer, Standfuss and Weierstall2015; Jaeger et al., Reference Jaeger, Dworkowski, Nogly, Milne, Wang and Standfuss2016; Weinert et al., Reference Weinert, Olieric, Cheng, Brünle, James, Ozerov, Gashi, Vera, Marsh, Jaeger, Dworkowski, Panepucci, Basu, Skopintsev, Doré, Geng, Cooke, Liang, Prota, Panneels, Nogly, Ermler, Schertler, Hennig, Steinmetz, Wang and Standfuss2017). Synchrotrons with a polychromatic ‘pink’ beam offer increased photon flux and 100 ps X-ray pulse exposures (Meents et al., Reference Meents, Wiedorn, Srajer, Henning, Sarrou, Bergtholdt, Barthelmess, Reinke, Dierksmeyer, Tolstikova, Schaible, Messerschmidt, Ogata, Kissick, Taft, Manstein, Lieske, Oberthuer, Fischetti and Chapman2017; Martin-Garcia et al., Reference Martin-Garcia, Zhu, Mendez, Lee, Chun, Li, Hu, Subramanian, Kissick, Ogata, Henning, Ishchenko, Dobson, Zhang, Weierstall, Spence, Fromme, Zatsepin, Fischetti, Cherezov and Liu2019). The promise of the ‘pink’ beam to accelerate serial crystallography is offset by higher backgrounds and less straightforward data processing (Förster and Schulze-Briese, Reference Förster and Schulze-Briese2019). Creative ways to deposit microcrystals into the X-ray beam in random orientations are increasing steadily. While most of these were originally developed for XFEL beamlines, many are amenable to being adapted by microfocus synchrotron beamlines. These include high-viscosity injection (Botha et al., Reference Botha, Nass, Barends, Kabsch, Latz, Dworkowski, Foucar, Panepucci, Wang, Shoeman, Schlichting and Doak2015; Nogly et al., Reference Nogly, James, Wang, White, Zatsepin, Shilova, Nelson, Liu, Johansson, Heymann, Jaeger, Metz, Wickstrand, Wu, Båth, Berntsen, Oberthuer, Panneels, Cherezov, Chapman, Schertler, Neutze, Spence, Moraes, Burghammer, Standfuss and Weierstall2015; Kovácsová et al., Reference Kovácsová, Grünbein, Kloos, Barends, Schlesinger, Heberle, Kabsch, Shoeman, Doak and Schlichting2017; Martin-Garcia et al., Reference Martin-Garcia, Conrad, Nelson, Stander, Zatsepin, Zook, Zhu, Geiger, Chun, Kissick, Hilgart, Ogata, Ishchenko, Nagaratnam, Roy-Chowdhury, Coe, Subramanian, Schaffer, James, Ketwala, Venugopalan, Xu, Corcoran, Ferguson, Weierstall, Spence, Cherezov, Fromme, Fischetti and Liu2017), flowing crystals through quartz capillaries (Stellato et al., Reference Stellato, Oberthür, Liang, Bean, Gati, Yefanov, Barty, Burkhardt, Fischer, Galli, Kirian, Meyer, Panneerselvam, Yoon, Chervinskii, Speller, White, Betzel, Meents and Chapman2014), low sample consumption microfluidic flow-focusing (Pawate et al., Reference Pawate, Šrajer, Schieferstein, Guha, Henning, Kosheleva, Schmidt, Ren, Kenis and Perry2015; de Wijn et al., Reference De Wijn, Hennig, Roche, Engilberge, Rollet, Fernandez-Millan, Brillet, Betat, Mörl, Roussel, Girard, Mueller-Dieckmann, Fox, Olieric, Gavira, Lorber and Sauter2019; Monteiro et al., Reference Monteiro, Vakili, Harich, Sztucki, Meier, Horrell, Josts and Trebbin2019) using 3D printed devices (Monteiro et al., Reference Monteiro, Von Stetten, Stohrer, Sans, Pearson, Santoni, Van Der Linden and Trebbin2020) and stable ultrasonic acoustic levitation of microliter droplets (Tsujino and Tomizaki, Reference Tsujino and Tomizaki2016). Ongoing work addresses problems with clogging and flow-speed of microfluidics, bubble generation, viscous stream focusing, deposition of varied-sized crystals, and sample consumption to enable the routine, uninterrupted collection of serial crystallography data. Popular fixed-target approaches include in situ deposition of crystal-containing drops on a humidity-controlled hit-and-return chip (Baxter et al., Reference Baxter, Aguila, Alonso-Mori, Barnes, Bonagura, Brehmer, Brunger, Calero, Caradoc-Davies, Chatterjee, Degrado, Fraser, Ibrahim, Kern, Kobilka, Kruse, Larsson, Lemke, Lyubimov, Manglik, Mcphillips, Norgren, Pang, Soltis, Song, Thomaston, Tsai, Weis, Woldeyes, Yachandra, Yano, Zouni and Cohen2016; Mehrabi et al., Reference Mehrabi, Schulz, Dsouza, Müller-Werkmeister, Tellkamp, Miller and Pai2019b; Mehrabi et al., Reference Mehrabi, Müller-Werkmeister, Leimkohl, Schikora, Ninkovic, Krivokuca, Andriček, Epp, Sherrell, Owen, Pearson, Tellkamp, Schulz and Miller2020), sandwiched between mylar, kapton, or cyclic-olefin-copolymer foils (Schubert et al., Reference Schubert, Kapis, Gicquel, Bourenkov, Schneider, Heymann, Betzel and Perbandt2016; Broecker et al., Reference Broecker, Morizumi, Ou, Klingel, Kuo, Kissick, Ishchenko, Lee, Xu, Makarov, Cherezov, Ogata and Ernst2018; Doak et al., Reference Doak, Nass Kovacs, Gorel, Foucar, Barends, Grünbein, Hilpert, Kloos, Roome, Shoeman, Stricker, Tono, You, Ueda, Sherrell, Owen and Schlichting2018; Feiler et al., Reference Feiler, Wallacher and Weiss2019; Wierman et al., Reference Wierman, Paré-Labrosse, Sarracini, Besaw, Cook, Oghbaey, Daoud, Mehrabi, Kriksunov, Kuo, Schuller, Smith, Ernst, Szebenyi, Gruner, Miller and Finke2019), on micropatterned silicon chips (Mueller et al., Reference Mueller, Marx, Epp, Zhong, Kuo, Balo, Soman, Schotte, Lemke, Owen, Pai, Pearson, Olson, Anfinrud, Ernst and Dwayne Miller2015; Roedig et al., Reference Roedig, Duman, Sanchez-Weatherby, Vartiainen, Burkhardt, Warmer, David, Wagner and Meents2016; Meents et al., Reference Meents, Wiedorn, Srajer, Henning, Sarrou, Bergtholdt, Barthelmess, Reinke, Dierksmeyer, Tolstikova, Schaible, Messerschmidt, Ogata, Kissick, Taft, Manstein, Lieske, Oberthuer, Fischetti and Chapman2017; Owen et al., Reference Owen, Axford, Sherrell, Kuo, Ernst, Schulz, Miller and Mueller-Werkmeister2017; Ebrahim et al., Reference Ebrahim, Appleby, Axford, Beale, Moreno-Chicano, Sherrell, Strange, Hough and Owen2019a) or transported into the beam by a conveyor-belt like tape drive (Roessler et al., Reference Roessler, Kuczewski, Stearns, Ellson, Olechno, Orville, Allaire, Soares and Héroux2013; Beyerlein et al., Reference Beyerlein, Dierksmeyer, Mariani, Kuhn, Sarrou, Ottaviano, Awel, Knoska, Fuglerud, Jönsson, Stern, Wiedorn, Yefanov, Adriano, Bean, Burkhardt, Fischer, Heymann, Horke, Jungnickel, Kovaleva, Lorbeer, Metz, Meyer, Morgan, Pande, Panneerselvam, Seuring, Tolstikova, Lieske, Aplin, Roessle, White, Chapman, Meents and Oberthuer2017). Another option is to directly grow crystals on an in situ micropatterned silicon chip. This minimizes crystal handling, allows the manipulation of sensitive crystals, and enables ligand soaking for serial crystallography (Lieske et al., Reference Lieske, Cerv, Kreida, Komadina, Fischer, Barthelmess, Fischer, Pakendorf, Yefanov, Mariani, Seine, Ross, Crosas, Lorbeer, Burkhardt, Lane, Guenther, Bergtholdt, Schoen, Törnroth-Horsefield, Chapman and Meents2019). Predetermining crystal positions on the chip accelerates crystal alignment and improves hit rates under controlled humidity conditions (Oghbaey et al., Reference Oghbaey, Sarracini, Ginn, Pare-Labrosse, Kuo, Marx, Epp, Sherrell, Eger, Zhong, Loch, Mariani, Alonso-Mori, Nelson, Lemke, Owen, Pearson, Stuart, Ernst, Mueller-Werkmeister and Miller2016). For more detailed accounts on creative sample delivery systems refer to articles such as Martiel et al. (Reference Martiel, Müller-Werkmeister and Cohen2019), Grünbein and Nass Kovacs (Reference Grünbein and Nass Kovacs2019), Zhao et al. (Reference Zhao, Zhang, Yan, Sun, Wang, He and Yin2019), and references therein.

Modeling heterogeneity

Resulting high-resolution RTX data have inspired the development of a set of new tools that assist refinement, modeling, and analysis of alternative conformations. Originally, in the context of collecting anomalous data, Diederichs et al. introduced a simple partial correction for intensity decay of reflections following radiation damage via a redundancy-based ‘zero dose extrapolation’ that leads to enhanced electron density maps (Diederichs et al., Reference Diederichs, McSweeney and Ravelli2003). On the refinement end, available tools include qFit and Phenix. The qFit algorithm uses an iterative procedure to represent discrete heterogeneity by fitting multi-conformers that are commonly ignored during manual model building into electron density maps (Fig. 4d). Since its initial implementation, qFit has expanded from side-chain rotamers (van den Bedem et al., Reference Van Den Bedem, Dhanik, Latombe and Deacon2009) to backbone heterogeneity (Keedy et al., Reference Keedy, Fraser and Van Den Bedem2015a) and to ligands (van Zundert et al., Reference Van Zundert, Hudson, De Oliveira, Keedy, Fonseca, Heliou, Suresh, Borrelli, Day, Fraser and Van Den Bedem2018). The new release of qFit 3 expands the work into single-particle cryoEM density maps (Riley et al., Reference Riley, Wankowicz, De Oliveira, Van Zundert, Hogan, Fraser, Keedy and Van Den Bedem2020). Phenix offers occupancy-refinement and ensemble-refinement strategies based on time averaging of simple molecular dynamics simulations to model X-ray data better than single structures (Burnley et al., Reference Burnley, Afonine, Adams and Gros2012). None of these automated methods is the panacea. They support rather than replace careful inspection of the electron density maps by an experienced crystallographer. Inspiration for modeling and analysis can also come from other helpful tools such as Ringer and CONTACT. Ringer is the quantitative, automated alternative to qualitative, manual inspection of electron density maps within Coot (Emsley et al., Reference Emsley, Lohkamp, Scott and Cowtan2010). Ringer samples electron density maps around dihedral angles of each protein residue (Lang et al., Reference Lang, Ng, Fraser, Corn, Echols, Sales, Holton and Alber2010). Statistical evidence supports modeling of alternative conformations below the traditional 1 sigma cutoff, even down to about 0.3 sigma (Fig. 4c). The result is more reflective of the polymorphic nature of proteins than a single structure (Fraser et al., Reference Fraser, Van Den Bedem, Samelson, Lang, Holton, Echols and Alber2011). For those interested in allostery, the CONTACT algorithm identifies networks of correlated motions of conformationally heterogeneous residues (van den Bedem et al., Reference Van Den Bedem, Bhabha, Yang, Wright and Fraser2013) (Fig. 4e–f). Sterically mutually exclusive placement of neighboring residues can form a functional network that links remote effector sites to the active site. The observation that such functional networks have been identified independently via co-evolving amino acids may present a framework to combine a phylogenetic with a dynamic view of protein evolvability (van den Bedem and Fraser, Reference Van Den Bedem and Fraser2015).

Fig. 4.

AFTER – Working with room temperature data; exemplified for two proteins, cytochrome C peroxidase gateless mutant (CCP; panels a–c) and dihydrofolate reductase (DHFR; panels d–f). (a) Crystallographic data of CCP (grey surface) bound to benzimidazole were collected from the same crystal at RT (4xv5, red) and cryogenic temperature (4nve, cyan). Green difference electron density shows that only at RT does His96 occupy an alternative ‘open’ conformation, revealing a transient secondary site that is occupied by benzimidazole, whereas water occupies the ‘closed’ site at cryo. (b) Overview of CCP with the ligand cavity in yellow and distal cryptic site is highlighted by a red dashed ellipsoid. (c) Ringer plot of CCP bound to another ligand, 2-amino-5-methylthiazole, shows an alternate conformation of Glu199 near the cavity site at RT (red line) [below the common threshold of 1 sigma (Fraser et al., Reference Fraser, Van Den Bedem, Samelson, Lang, Holton, Echols and Alber2011)] that is hidden at cryogenic temperature (blue line). (d) Multi-conformer model of DHFR built by qFit with alternate conformations B in magenta, C in cyan, and the NADP⁺ cofactor (NAP) and folate (FOL) in yellow sticks. (e) Nine CONTACT networks are mapped onto the DHFR structure (in the same orientation as in d). (f) The largest network identified by CONTACT contains 15 residues (sequence numbers given in nodes) and the NAP ligand. Edges connect nodes of clashing residues that are relieved by alternative conformations.

Integrated approaches

RTX studies have the most impact when they are used in combination with complementary methods to illuminate an unappreciated aspect of biological function. RTX studies of proline isomerase cyclophilin A (CypA) revealed hidden conformational states that are important for catalysis (Fraser et al., Reference Fraser, Clarkson, Degnan, Erion, Kern and Alber2009). To populate this conformation, a mutation was designed that inverted the conformational states observed at equilibrium and correlated with catalytic rates of the enzyme. In combination with nuclear magnetic resonance (NMR) analysis, this demonstrated the strength of RTX in accessing functional protein dynamics. Correlation between fluctuations detected by NMR in solution and RT electron density maps were also discovered for the oncogenic-signaling switch protein Ras (Scheidig et al., Reference Scheidig, Burmester and Goody1999; Fraser et al., Reference Fraser, Van Den Bedem, Samelson, Lang, Holton, Echols and Alber2011). Revealing the structural origin for this allosteric network provided insights into a catalytically competent conformation of Ras that aids mechanistic understanding of Ras inhibition (Lu et al., Reference Lu, Jang, Gu, Zhang and Nussinov2016). Another powerful combination of methods at RT is joint X-ray/neutron refinement. Intricacies of H-bonding were revealed for several different proteins. A cGMP-dependent protein kinase joint X-ray/neutron refinement of RT data suggests that the conformation of an arginine interacting with cGMP is more dynamic than previously appreciated and it is ‘frozen-out’ in the low-temperature structure (Huang et al., Reference Huang, Gerlits, Blakeley, Sankaran, Kovalevsky and Kim2014). The same group also discovered an unprecedented low-barrier H-bond in concanavalin A, a legume lectin with anticancer potential (Gerlits et al., Reference Gerlits, Coates, Woods and Kovalevsky2017b). Another study found that interactions between an HIV protease triple-mutant with the inhibitor amprenavir are not as significantly altered as proposed on the basis of a cryogenic structure (Gerlits et al., Reference Gerlits, Keen, Blakeley, Louis, Weber and Kovalevsky2017a). Most recently, RT neutron and X-ray data were collected on the main protease of SARS-CoV-2 (Kneller et al., Reference Kneller, Phillips, Kovalevsky and Coates2020a) for which RT X-ray studies have revealed novel backbone conformations in the P5 binding pocket (Kneller et al., Reference Kneller, Phillips, O'neill, Jedrzejczak, Stols, Langan, Joachimiak, Coates and Kovalevsky2020b).

The comparison of structural ensembles of CypA collected at synchrotron and XFEL sources confirms the agreement between both methods and motivates experiments that resolve the temperature- and time-coordinate of protein functional dynamics (Keedy et al., Reference Keedy, Kenner, Warkentin, Woldeyes, Hopkins, Thompson, Brewster, Van Benschoten, Baxter, Uervirojnangkoorn, Mcphillips, Song, Alonso-Mori, Holton, Weis, Brunger, Soltis, Lemke, Gonzalez, Sauter, Cohen, Van Den Bedem, Thorne and Fraser2015b). As this review focuses on synchrotron RTX, I refer to topical reviews that highlight the value of XFEL RT studies to investigate the important target class of membrane-associated proteins, such as Neutze et al. (Reference Neutze, Brändén and Schertler2015), Martin-Garcia et al. (Reference Martin-Garcia, Conrad, Coe, Roy-Chowdhury and Fromme2016), Mishin et al. (Reference Mishin, Gusach, Luginina, Marin, Borshchevskiy and Cherezov2019), Wickstrand et al. (Reference Wickstrand, Nogly, Nango, Iwata, Standfuss and Neutze2019).

Variable temperature crystallography

Variable temperature crystallography uses discrete temperature steps to sample the protein conformational landscape (Keedy, Reference Keedy2019). Given the experimental obstacles, there are only a few examples of ‘temperature titrations’ since the pioneering efforts mentioned above (Frauenfelder et al., Reference Frauenfelder, Petsko and Tsernoglou1979; Tilton et al., Reference Tilton, Dewan and Petsko1992). A further two decades later, variable temperature crystallography was used to study the complex evolution of conformational heterogeneity for CypA across a range of eight temperatures. The authors found that instead of a single concerted movement across the protein, different parts of the protein have different transition temperatures (Keedy et al., Reference Keedy, Kenner, Warkentin, Woldeyes, Hopkins, Thompson, Brewster, Van Benschoten, Baxter, Uervirojnangkoorn, Mcphillips, Song, Alonso-Mori, Holton, Weis, Brunger, Soltis, Lemke, Gonzalez, Sauter, Cohen, Van Den Bedem, Thorne and Fraser2015b). Temperature changes have also been used to analyze flavodoxin oxidation states (Watt et al., Reference Watt, Tulinsky, Swenson and Watenpaugh1991), and the in crystallo thermodynamics of conformational states of a redox quinone co-factor in bacterial copper amine oxidase during the catalytic reaction (Murakawa et al., Reference Murakawa, Baba, Kawano, Hayashi, Yano, Kumasaka, Yamamoto, Tanizawa and Okajima2019). Advances in collecting high-temperature datasets at 293–363 K promise future temperature explorations of the conformational landscape beyond proteinase K, thaumatin, and lysozyme (Doukov et al., Reference Doukov, Herschlag and Yabukarski2020).

Ligands

Conformational heterogeneity is present not only in protein structures but also in ligands. Ligands adopt a wide range of conformations upon binding to proteins. This is especially true for small-molecule fragments that bind with low affinity and high promiscuity. However, finding evidence for multiple binding modes within an averaged electron density map can be difficult. To address this shortcoming, qFit-ligand was developed to find often isoenergetic, unmodeled conformations of drug-like molecules within crystal structures (van Zundert et al., Reference Van Zundert, Hudson, De Oliveira, Keedy, Fonseca, Heliou, Suresh, Borrelli, Day, Fraser and Van Den Bedem2018). For PDB structures of cancer target BRD4, the method revealed that as much as 29% of the protein crystal structures showed evidence of unmodeled protein–ligand interactions that would have been informative to guide compound design (Raich et al., Reference Raich, Meier, Günther, Christ, Noé and Olsson2020). Generally, one rarely finds ligand sites 100% occupied, as they are typically modeled. Especially at RT, ligand binding is thermodynamically less favorable (Fischer et al., Reference Fischer, Shoichet and Fraser2015). Hence ligand complexes should be modeled as ensembles of bound and unbound states (Pearce et al., Reference Pearce, Krojer and Von Delft2017).

Computational ligand discovery struggles from weighting energy penalties of relevant conformational states when looking for ligands in large databases. In a study of model protein cytochrome C peroxidase, the RT structure of the apo state visualized three conformational states of the protein that could be used in docking (Fischer et al., Reference Fischer, Coleman, Fraser and Shoichet2014). Their relative occupancies were used to calculate Boltzmann-weighted energy penalties in docking. Using this approach, the authors identified ligands that stabilized specific conformational states that would not have been identified at cryo. Furthermore, comparisons of pairwise cryo–RT datasets of protein-ligand complexes provided insights into systematic structural differences (Fischer et al., Reference Fischer, Shoichet and Fraser2015). For instance, side chains near the canonical ligand-binding site were remodeled in response to temperature (Fig. 4c). If only the cryo dataset was considered, as is common in computational drug discovery, these changes may have been mistaken for genuine responses to ligand binding, rather than temperature artifacts. Strikingly, the remodeling of a distal residue exposed a remote cryptic site that was hidden in the cryogenic structure collected on the same crystal (Fig. 4a).

Allostery and mutations

Allostery is often mediated by correlated motions across the protein (Mehrabi et al., Reference Mehrabi, Schulz, Dsouza, Müller-Werkmeister, Tellkamp, Miller and Pai2019b). The CONTACT algorithm capitalizes on the exchange between conformational substates that can be revealed in RTX electron density maps (van den Bedem et al., Reference Van Den Bedem, Bhabha, Yang, Wright and Fraser2013). In a study of E. coli dihydrofolate reductase, CONTACT recapitulated the effect of an allosteric mutation seen by NMR chemical-shift perturbations. This demonstrated that altering optimized contact networks of coordinated motions can impair catalytic function. In an endeavor to discover new allosteric sites for the diabetes target protein tyrosine phosphatase 1B, the active-site WPD loop opening was revealed at elevated temperatures (Keedy et al., Reference Keedy, Hill, Biel, Kang, Rettenmaier, Brandão-Neto, Pearce, Von Delft, Wells and Fraser2018). However, loop occupancies did not increase linearly with temperature. A new sample delivery system for serial RTX helped track long-range interactions and higher-energy conformations that appear to be critical for substrate access to the active site of trans-acyltransferase (Mathews et al., Reference Mathews, Allison, Robbins, Lyubimov, Uervirojnangkoorn, Brunger, Khosla, Demirci, Mcphillips, Hollenbeck, Soltis and Cohen2017).

Like temperature and ligands, mutations represent another perturbation of protein structure. Such unintentional mutations may arise to confer resistance to anti-cancer, anti-bacterial, anti-viral, or anti-fungal treatment. Understanding the effect of mutation on protein structure is also key to intentionally inform protein engineering in the pursuit of guiding the design of new protein function. Laboratory evolution of protein function takes many rounds of optimization. During the initial directed evolution, using the increase in flexibility as a guide to escape local energy minima emerged as the most promising strategy to increase the binding affinity of redesigned ubiquitin variants to the deubiquitinase USP7 (Biel et al., Reference Biel, Thompson, Cunningham, Corn and Fraser2017). Other work on laboratory evolution of a designed Kemp eliminase used RTX to recapitulate shifts in the conformational ensemble towards catalytically-productive sub-states (Broom et al., Reference Broom, Rakotoharisoa, Thompson, Zarifi, Nguyen, Mukhametzhanov, Liu, Fraser and Chica2020). This suggests that RTX guided ensemble modeling can help inform directed evolution efforts. In a recent study, a combination of thermodynamic cycles and RTX was used to track perturbations in water networks. While delicate shifts in water positions are often ignored, introducing subtle changes in the protein and ligands demonstrated that water networks contribute substantially to the binding of congeneric sialic acid ligands to virulence protein SiaP (Darby et al., Reference Darby, Hopkins, Shimizu, Roberts, Brannigan, Turkenburg, Thomas, Hubbard and Fischer2019).