We acknowledge the many Diamond Light Source scientists and support team members who contributed to the design, construction, and operation of the VMXi beamline. We are grateful to beamline users, who have contributed ideas later to the development of the crystallization and data collection pipelines. The Crystallization Facility at Harwell is supported by Diamond Light Source Ltd, the Rosalind Franklin Institute, and the Medical Research Council.

Automated Protein Crystallization and Real-Time Data Collection

<ol>
	<li>Lynch, M. L., Snell, M. E., Potter, S. A., Snell, E. H., Bowman, S. E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=20+years+of+crystal+hits:+Progress+and+promise+in+ultrahigh-throughput+crystallization+screening.">20 years of crystal hits: Progress and promise in ultrahigh-throughput crystallization screening.</a> Acta Crystallographica Section D Structural Biology. 79 (Pt 3), 198-205 (2023).</li><li>Fischer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macromolecular+room+temperature+crystallography.">Macromolecular room temperature crystallography.</a> Quarterly Reviews of Biophysics. 54, 1 (2021).</li><li>Helliwell, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+is+the+structural+chemistry+of+the+living+organism+at+its+temperature+and+pressure.">What is the structural chemistry of the living organism at its temperature and pressure.</a> Acta Crystallographica Section D Structural Biology. 76 (Pt 2), 87-93 (2020).</li><li>Thorne, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determining+biomolecular+structures+near+room+temperature+using+x-ray+crystallography:+Concepts,+methods+and+future+optimization.">Determining biomolecular structures near room temperature using x-ray crystallography: Concepts, methods and future optimization.</a> Acta Crystallographica Section D Structural Biology. 79 (Pt 1), 78-94 (2023).</li><li>Keedy, D. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Crystal+cryocooling+distorts+conformational+heterogeneity+in+a+model+michaelis+complex+of+dhfr.">Crystal cryocooling distorts conformational heterogeneity in a model michaelis complex of dhfr.</a> Structure. 22 (6), 899-910 (2014).</li><li>Huang, C. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Probing+ligand+binding+of+endothiapepsin+by+'temperature-resolved'+macromolecular+crystallography.">Probing ligand binding of endothiapepsin by 'temperature-resolved' macromolecular crystallography.</a> Acta Crystallographica Section D Structural Biology. 78 (Pt 8), 964-974 (2022).</li><li>Sanchez-Weatherby, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Vmxi:+A+fully+automated,+fully+remote,+high-flux+in+situ+macromolecular+crystallography+beamline.">Vmxi: A fully automated, fully remote, high-flux in situ macromolecular crystallography beamline.</a> Journal of Synchrotron Radiation. 26 (Pt 1), 291-301 (2019).</li><li>Jacquamet, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+analysis+of+vapor+diffusion+crystallization+drops+with+an+x-ray+beam.">Automated analysis of vapor diffusion crystallization drops with an x-ray beam.</a> Structure. 12 (7), 1219-1225 (2004).</li><li>Mikolajek, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein-to-structure+pipeline+for+ambient-temperature+in+situ+crystallography+at+vmxi.">Protein-to-structure pipeline for ambient-temperature in situ crystallography at vmxi.</a> IUCrJ. 10, 420-429 (2023).</li><li>Douangamath, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Achieving+efficient+fragment+screening+at+xchem+facility+at+diamond+light+source.">Achieving efficient fragment screening at xchem facility at diamond light source.</a> Journal of Visualised Experiments. (171), (2021).</li><li>Delageniere, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ispyb:+An+information+management+system+for+synchrotron+macromolecular+crystallography.">Ispyb: An information management system for synchrotron macromolecular crystallography.</a> Bioinformatics. 27 (22), 3186-3192 (2011).</li><li>Fisher, S. J., Levik, K. E., Williams, M. A., Ashton, A. W., Mcauley, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synchweb:+A+modern+interface+for+ispyb.">Synchweb: A modern interface for ispyb.</a> Journal of Applied Crystallography. 48 (Pt 3), 927-932 (2015).</li><li>Jenkins, J., Cook, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mosquito&#174;:+An+accurate+nanoliter+dispensing+technology.">Mosquito&#174;: An accurate nanoliter dispensing technology.</a> JALA: Journal of the Association for Laboratory Automation. 9 (4), 257-261 (2016).</li><li>Gildea, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Xia2.Multiplex:+A+multi-crystal+data-analysis+pipeline.">Xia2.Multiplex: A multi-crystal data-analysis pipeline.</a> Acta Crystallographica Section D Structural Biology. 78 (Pt 6), 752-769 (2022).</li><li>Winter, G., Mcauley, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+data+collection+for+macromolecular+crystallography.">Automated data collection for macromolecular crystallography.</a> Methods. 55 (1), 81-93 (2011).</li><li>Jumper, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+accurate+protein+structure+prediction+with+alphafold.">Highly accurate protein structure prediction with alphafold.</a> Nature. 596 (7873), 583-589 (2021).</li><li>Winter, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dials+as+a+toolkit.">Dials as a toolkit.</a> Protein Science. 31 (1), 232-250 (2022).</li></ol>

The authors declare no conflicts of interest.

We have described the full procedure from the arrival of a protein sample in the CF to the downloading of the final data by the user for further applications. Critical steps are the production of a high-quality protein sample and appropriate crystal screens, either using commercial sparse matrix screens or optimization screens based on established conditions. This process may take place in the CF, or users may carry out the crystallization procedures in the home laboratories and bring suitable crystallization plates to the beamline. The identification of suitable data collection parameters may be important for certain samples, particularly where radiation damage is a concern. In most cases, automated data processing is fully sufficient to answer the scientific question although users retain the capability to reprocess using the beamline tools, for example, where the space group is ambiguous or only the initial part of the collected data is used to minimize radiation damage effects.
If suitable crystals are not produced from initial crystallization trials, then changes in protein concentration, purity, or crystallization screens may be explored, as may the use of crystal seeding. If crystals do not diffract to a useful resolution at the beamline, then grid scans may be used with an unattenuated beam to assess the inherent diffraction limit and unit cell of the crystals to guide optimization efforts. Crystals that are too small for data collection within plates (e.g., &#60;10 &#181;m) may instead be suitable for serial crystallography or nano focus experiments (e.g., at Diamond beamline VMXm). Solving structures using VMXi data is generally straightforward by molecular replacement, particularly since the advent of Alphafold16 to give effective search models. If this is not successful, crystals may be harvested and cryocooled from plates to enable conventional single-wavelength anomalous diffraction, multiwavelength anomalous diffraction, or long wavelength phasing experiments.
The advantages of this method include the ability to obtain rapid, high-quality datasets and feedback directly from crystallization plates without the need to disturb crystals from the environments in which they have grown. The so-called &#39;room temperature Renaissance&#39; in structural biology places a premium on structures obtained under non-cryogenic conditions to enable more physiological relevance and protein dynamics to be explored2. Usually, a slightly lower resolution is achieved than for an optimized cryocooled crystal but only when suitable cryo conditions have been established and if the crystals are robust to mechanical handling and opening of the crystallization drop3. A forthcoming application for which this pipeline is very well suited is a large-scale screening of protein-ligand complexes or fragment campaigns at room temperature in drug discovery. Ligands or fragments can either be cocrystallized or added by pipette or acoustic drop ejection prior to room-temperature data collection. Another application is to rapidly measure data from many hundreds or thousands of crystals in a highly efficient manner and then to use the DIALS17 multiplex14 software to extract isomorphous clusters that may represent different biological entities or to establish statistically significant differences between populations of crystals that have been treated in a different way or exposed to different ligands or signal.

X-ray crystallography remains a key tool for understanding protein structure and function, providing high-resolution structures of proteins or their complexes with, for example, substrates or drug candidates. In many cases, however, obtaining crystals with desirable properties - highly diffracting, crystal form amenable to soaking and without crystal pathologies such as twinning - remains a considerable bottleneck1. As suitable chemical conditions to produce protein crystals cannot, in general, be predicted, crystallization screening exploring thousands of potential chemical mixtures is standard, often aided by automation/robotics in setting screens and crystal hotels for monitoring, often remotely, the crystallization drop images that are recorded.
When crystals appear, typically they must be harvested from the crystallization environment using a nylon or Kapton loop and then, transferred to a droplet containing a cryoprotection agent (the search for which is an additional variable) before plunge-freezing into liquid nitrogen. These additional steps between crystallization and X-ray data collection can involve dehydration of the crystallization drop when its sealed environment is broken, mechanical stresses on the crystal when it is handled, and damage from the cryoprotection agents to the crystal lattice (typically resulting in increased mosaic spread) amongst other factors2. In addition, crystal harvesting is time- and labor-intensive and can lead to inhomogeneity between samples, especially when skin forms on drops during the harvesting process. The VMXi beamline gives access to useable data from crystals that are stuck to the plate, which would otherwise be discarded for data collection.
The vast majority of X-ray crystal structures are determined at 100K using the above approach, enabling straightforward crystal transport and handling and increasing crystal lifetime in the X-ray beam by orders of magnitude. There is increasing interest, however, in determining structures under non-cryogenic conditions, that is, much closer to the physiological conditions relevant to protein function2,3,4. This enables a much better appreciation of the dynamic structure of proteins, avoids amino acid conformations or loops being frozen out in functionally non-relevant states5, and enables ligand binding to be explored under conditions much closer to those in the protein&#39;s natural environment within the cell and organism6.
An alternative approach, implemented at the Versatile Macromolecular Crystallography in situ (VMXi) beamline at the Diamond Light Source synchrotron, UK, is to measure the diffraction data directly from crystals within the environment in which they have grown (i.e., within the crystallization plate), under ambient conditions and without disturbance7,8. This enables very rapid feedback from crystallization screens and optimizations to guide a user to an optimal crystal form for their requirements. It also enables high-quality room-temperature structures to be produced in an automated manner9.
This protocol assumes that a user has a highly pure protein sample ready for crystallization. We describe the user experience accessing the Crystallization Facility at Harwell to produce protein crystals and then use beamline VMXi for data collection (Figure 1).
The Crystallization Facility at Harwell
The Crystallization Facility at Harwell (CF) is located in the Research Complex at Harwell (RCaH) adjacent to the Diamond Light Source. The facility offers users a high-throughput automated laboratory for macromolecular crystallization, using robotics for crystallization screening, crystal optimization, crystal imaging, and characterization. Through close integration with the highly automated VMXi beamline, the pace of determining room temperature structures has greatly accelerated and enables the characterization of novel protein structures, protein-ligand and DNA-ligand complexes, as well as automated fragment screening (Figure 1), all under non-cryogenic conditions.
The CF pipeline is a suite of instrumentation encompassing nanoliter crystallization robots9 for the crystallization of soluble and membrane proteins, liquid handling robots to prepare commercial crystallization screens and complex custom optimization screens, and four imaging instruments (one at 4 &#176;C and three at 20 &#176;C for the imaging of crystallization plates (see the Table of Materials). One imager is capable of imaging lipid cubic phase (LCP) glass plates and one imager is equipped with multi-fluorescence optics (both at 20 &#176;C).
The facility is now used widely by a broad spectrum of academic and industrial users, including the Membrane Protein Laboratory (MPL;https://www.diamond.ac.uk/Instruments/Mx/MPL.html), the XChem fragment screening facility 10, MX beamlines, the XFEL-hub, as well as the Rosalind Franklin Institute (RFI). This well-established and optimized pipeline has enabled crystallization experiments to be performed across a wide spectrum of structural biology projects. This paper describes the pipeline for crystals intended for data collection at VMXi, although crystals may also be harvested and cryo-cooled or directed to the XChem pipeline.
User access is allocated through the Diamond MX proposal system (https://www.diamond.ac.uk/Instruments/Mx/Synchrotron-Access.html) and industrial users are supported through the Diamond Industry Liaison group. All users can come to the site with their sample(s) or plates, which can be transported by hand. It is not recommended to send plates by courier as our experience suggests that drops can move away from the location in which they were dispensed, or drops may be damaged by the crystallization reservoir. Alternatively, by arrangement, users can send their protein samples to the CF, where members of staff set up crystallization experiments on their behalf. The experiments can be monitored remotely by the user by either logging on to Rock Maker Web in the case of CF or via ISPyB in the case of VMXi. Access to the CF can be carried out in an iterative fashion based on the X-ray diffraction results collected at Diamond.
Beamline VMXi at Diamond Light Source
Beamline VMXi (hereafter referred to as &#34;the beamline&#34;) is a unique and recently developed instrument fully dedicated to room-temperature, highly automated X-ray crystallography with a focus on measuring data from crystals within suitable crystallization plates. The beamline offers a micro focus (10 x 10 &#181;m), pink beam (band pass of &#60;5 &#215; 10-2&#916;E/E) with a high flux of ~2 &#215; 1013 photons/s (at 16 KeV)7. This high-flux beam, coupled with a fast detector, enables very high throughput of samples and collection of data from samples upwards of 10 &#181;m in size.
Crystallization plates enter the beamline by being stored in a sample storage system and imaged based on the schedule provided by the user while registering the plates using the ISPyB11 interface SynchWeb12. Typically, users are advised to select a Fibonacci sequence of time points for imaging (0, 12, 24, 36, 60&#8230;7,320 h from the plate being entered into the system). The user is informed by email once a plate has been imaged. Both visible light and UV light imaging are available to users on demand. The images taken by the sample storage system are analyzed by a machine learning algorithm; this automatically locates and defines points of interest of objects that resemble crystals and registers the points of interest ready for the user to add to a queue for data collection. Users may also manually click on the visible light images to register points of interest or can click and drag a region to be analyzed by raster scan. These points are available to users to add to the queue alongside the automatically located points.
Once all samples have appropriate parameters for data collection, the plate enters a queue. When the plate reaches the top of the queue, it is automatically dispensed to the beamline. The crystallization plates are loaded from the crystal hotels into the beamline automatically by a robotic arm, and following image matching, crystallographic data sets of up to 60&#176; rotation are measured from each selected crystal as per user defined instructions. All drops within a plate may be used for these experiments on the beamline. Data are merged from multiple crystals to produce isomorphous, optimally merged data sets in an automated manner7,9. Once all of the queued data sets are collected, the user is sent an email with a link to follow to view the data sets in ISPyB11, as in other Diamond MX beamlines. Users are also directed to the beamline web page (https://www.diamond.ac.uk/Instruments/Mx/VMXi.html).&#160;

<table fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Formulator</td>
			<td>Formulatrix</td>
			<td>on request</td>
			<td>Liquid handling robot</td>
		</tr>
		<tr>
			<td>Formulatrix imager</td>
			<td>Formulatrix</td>
			<td>on request</td>
			<td>Crystallisation plate imager</td>
		</tr>
		<tr>
			<td>Greiner CrystalQuick X&#160;</td>
			<td>Greiner</td>
			<td>Z617644</td>
			<td>Crystallisation plate</td>
		</tr>
		<tr>
			<td>Gryphon&#160;</td>
			<td>Art Robbins Instruments</td>
			<td>620-1000-10&#160;</td>
			<td>Crystalisation robot</td>
		</tr>
		<tr>
			<td>MiTeGen Insitu-1</td>
			<td>Mitegen</td>
			<td>InSitu-01CL-40</td>
			<td>Crystallisation plate</td>
		</tr>
		<tr>
			<td>Mosquito LCP&#160;</td>
			<td>(SPT Labtech)</td>
			<td>on request</td>
			<td>Crystallisation robot</td>
		</tr>
		<tr>
			<td>Rock Imager &#38; Maker</td>
			<td>Formualtrix</td>
			<td>on request</td>
			<td>
			Software for Imager 
 [1] https://formulatrix.com/protein-crystallization-systems/rock-maker-crystallization-software/
			</td>
		</tr>
		<tr>
			<td>Scorpion</td>
			<td>Art Robbins Instruments</td>
			<td>640-1000-10&#160;</td>
			<td>
			Liquid handling robot 
			https://www.artrobbins.com/scorpion
			</td>
		</tr>
	</tbody>
</table>

crystallization and in situ room temperature data collection using the crystallization facility at harwell and beamline vmxi, diamond light source

Protocols for robotic protein crystallization using the Crystallization Facility at Harwell and in situ room temperature data collection from crystallization plates at Diamond Light Source beamline VMXi are described. This approach enables high-quality room-temperature crystal structures to be determined from multiple crystals in a straightforward manner and provides very rapid feedback on the results of crystallization trials as well as enabling serial crystallography. The value of room temperature structures in understanding protein structure, ligand binding, and dynamics is becoming increasingly recognized in the structural biology community. This pipeline is accessible to users from all over the world with several available modes of access. Crystallization experiments that are set up can be imaged and viewed remotely with crystals identified automatically using a machine learning tool. Data are measured in a queue-based system with up to 60&#176; rotation datasets from user-selected crystals in a plate. Data from all the crystals within a particular well or sample group are automatically merged using xia2.multiplex with the outputs straightforwardly accessed via a web browser interface.

The crystallization facility and VMXi beamline have been used for a wide variety of project types and use cases. Here are a small number of examples to illustrate what users may wish to pursue.
Case study 1: Standard data collection
The beamline enables rapid determination of room-temperature crystal structures from a small number of crystals within a crystallization plate. The minimum number of crystals is dependent on the space group and crystal orientations but is frequently 1-4 although improved data quality may be achieved by merging data from several tens of crystals. A recent example is one of the beamline standards, thaumatin. Multiple crystals, shown in Figure 8A, were marked up for data collection manually as described in protocol section 2.3. These crystals were added to the queue as described in protocol section 2.4 and experimental parameters were selected from the dropdown list. Once experimental parameters had been applied, the plate was queued for data collection. Datasets were collected, automatically scaled, and merged using the xia2.multiplex pipeline as described in protocol section 3. An example output from SynchWeb is shown in Figure 8A middle. Five merged datasets gave rise to a dataset of 1.66 &#197; resolution. For standard data collection of approximately five crystals in a well, datasets were collected within 2.5 min.
Case study 2: Ligand Binding&#160;&#8211;&#160;Fragment experiment using Mac1 protein
Producing structures of protein-ligand complexes at room temperature may be straightforwardly achieved using the beamline. Ligands may be added to drops on crystallization plates (either manually or by acoustic drop injection) and data measured after a suitable incubation time. In the example described here, a series of fragments were dispensed into wells containing crystals of the SARS-CoV-2 first macrodomain of the protein nsp3 (Mac-1) in a crystallization plate. Two of the wells containing the same fragment were assigned as a group as described in protocol step 2.5. Multiple crystals (42) were marked for data collection as described in protocol steps 2.3 and 2.4, and datasets were collected using standard parameters (60&#176; rotation, 0.1&#176; step, 0.00178 s exposure, 5% transmission, 16 KeV - per crystal) (Figure 8B). Datasets from the two wells were automatically processed using the xia2.dials pipeline and subsequently, the xia2.multiplex pipeline was initiated to automatically merge 22 of these data sets. DIMPLE was then run on the output of these pipelines and yielded maps that clearly showed evidence of the bound fragment. The fragment model was built into the unoccupied density and further refined (Figure 8B right). Room-temperature ligand-bound structures can easily be determined using this series of steps to provide invaluable information and feedback to the structure-based drug design process. For this data collection of 42 crystals across a number of wells, datasets were collected within 10 min.
Case study 3: Structure solution with a low symmetry space group and preferred orientations A stack of multiple crystals with plate-like morphology was produced from crystallization experiments with a c-type gas-binding cytochrome (Figure 8C). By selecting several positions around the edge of the stack where only a single crystal was in the X-ray beam, it was possible to obtain a good quality dataset to 1.75 &#197; resolution by merging wedges from four crystals, despite a monoclinic (C2) space group. This allowed rapid progress of the project without the need to further optimize the crystallization conditions. This result has been described previously9. For this data collection of four crystals in a well, datasets were collected within 2 min.
Case study 4: Obtaining information and room-temperature structure from microcrystals in a plate using serial crystallography
Often when microcrystals appear in a drop or when users are seeking to optimize micro-crystallization protocols in batch as a precursor to serial crystallography experiments at synchrotron or XFEL sources, it is very helpful to gain rapid feedback on the diffraction properties and unit cell dimensions of different trials using minimal material. In this use case, microcrystals of lysozyme growing in batch were pipetted into a crystallization plate (200 nL volume per drop) and data collected from eight drops using a grid scan with a 10 &#181;m step size (Figure 9). The resulting 25,906 still images were processed using serial crystallography software resulting in a dataset, where 9,891 diffraction patterns were indexed and merged producing a dataset to 2.0 &#197; resolution that refined well against the published room temperature structure (Rwork = 19.6%, Rfree = 23.6% using PDB 8A9D) (Table 1). This allowed detailed analysis of unit cell distribution and a microcrystal room temperature structure determination that could feed into complex serial crystallography experiments including time-resolved studies. The total volume of microcrystal suspension required was 1.6 &#181;L. For this data collection of microcrystals across eight wells using grid scans, datasets were collected within 40 min.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65964/65964fig1.jpg" /> 
Figure 1: Schematic of the protein-to-structure pipeline integrating crystallization screening, optimization at the crystallization facility, room-temperature automated data collection and processing without sample harvesting at VMXi, XChem fragment screening, and data collection at other MX beamlines. Users can start the pipeline by supplying a sample or by bringing plates to the VMXi beamline. Abbreviation: Versatile Macromolecular Crystallography in situ. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65964/65964fig2.jpg" /> 
Figure 2: The Mosquito SPT Labtech interface for setting up crystallization plates. (A) (1) The MiTeGen In Situ-1&#160;Setup view. Choose the MiTeGen 2 drop standard plate by going to (2) the 96-well plate type and selecting (3) the MiTeGen plate 2 drop plate. To change the definition parameters for drop 1 and drop 2, which is required for VMXi, click on the (4) edit icon. This opens a new window (B) where (5) the X and Y offsets need to be changed as shown. Select (B) the sub well 2 and (C) sub well 3 and change the values accordingly. (D) The CrystalQuickX&#8203; Setup view. Choose the CrystalQuickX&#160;2 drop standard plate by going to the 96-well plate type and selecting the MiTeGen plate 2 drop plate. To change the definition parameters for drop 1 and drop 2, which is required for VMXi, click the edit icon the same as above. This opens a new window where (E,F) the X and Y offsets need to be changed as shown. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65964/65964fig3.jpg" /> 
Figure 3: The SynchWeb interface showing how to create a VMXi shipment, register a plate, and check contact details. Screenshots of the various stages of uploading information into the SynchWeb interface are shown from (A) the dropdown menu, (B,C) registering a new shipment, (D) registering a new container, (E) inputting the plate information, (F) check contact details, and (G) a list of registered containers within a proposal. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65964/65964fig4.jpg" /> 
Figure 4: Selecting and preparing samples for data collection using SynchWeb. A series of screenshots showing the various stages of preparing samples for data collection using the SynchWeb interface are displayed. (A) Points and regions of interest are selected from the drop overview. In the lower section of this panel, there is a chronological series of photographs of one drop. (B) An example of the CHiMP output for one plate highlighting results for the &#39;crystal&#39; category. (C) Adding samples to the queue from the list of selected points and regions and (D) applying parameters for data collection to the queued samples from the dropdown list of beamline-created experiment settings. Note the difference between samples with no experimental parameters (in red) versus those that have correctly applied parameters (top and bottom). At the bottom of this panel is the Queue Container button, which queues the plate to be collected on the beamline. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65964/65964fig5.jpg" /> 
Figure 5: Sample group creation in SynchWeb. A series of screenshots showing the various stages of creating sample groups. (A) The plate(s) containing samples are selected from the relevant shipment and (B) the drops within the plate are selected. These may be individual drops or may be selected by row and/or column. (C) A list of sample groups that have already been created. (D) The outputs of the last three multiplex processing jobs are listed and can be selected to show statistics from the processing pipeline. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65964/65964fig6.jpg" /> 
Figure 6: Data processing and data reduction. (A) Screenshot of a processed dataset in ISPyB11. The button to access reprocessing features is highlighted. Sample ID and experimental parameters are shown in the top left and the diffraction image viewer in the middle. Clicking on this image will open an interactive window to examine different images. The crystal image viewer is shown on the right and clicking on this image will also open an interactive window to compare beamline and Formulatrix storage images. Per-image analysis plot is shown on the far right and clicking on this image will open a magnified version of this output. Clicking on the Auto Processing tab will make the autoprocessing visible and make the comparison between the results of the different pipelines easy. Click on the tabs to switch between the different processing pipelines and view the detailed output from the selected pipeline. The Logs &#38; Files button for data download is highlighted. Clicking on the Downstream Processing tab will expand and provide results for any data sets run through post-data reduction pipelines where appropriate. (B) Screenshot from the Sample Group Management screen. The user-defined group name is on the top and the visual description of the included wells can be seen below. A green well indicates that all crystals measured from that drop will be included in the group. A summary of different multiplex jobs performed on that group can be seen and underneath is the detailed output from multiplex. The Data Sets button to examine the included experiments is highlighted. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65964/65964fig7.jpg" /> 
Figure 7: Data reprocessing windows. (A) Individual and (B) multi-crystal datasets. Two individual datasets are displayed where regions of data have been selected. With the Process individually check box ticked, the selected diffraction images will be individually processed by pressing the Integrate button. Clicking the Multi-crystal button will open a display of the individual datasets. To reprocess diffraction images from multiple datasets, regions of images are selected as displayed, and reprocessing is initiated by clicking the Integrate button as highlighted. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65964/65964fig8.jpg" /> 
Figure 8: Representative results from the VMXi pipeline. (A) Marked-up crystals for protein thaumatin within a crystallization drop (left panel), data processing results (center panel), and electron density (right panel). (B) Collection on multiple crystals to determine binding of the fragment to SARS-CoV-2 Macro domain. Datasets were collected on multiple crystals in the presence of a fragment from the EU-OPENSCREEN fragment screen using standard experimental settings. Examples of these data collections are shown in this excerpt from SynchWeb. The fragment was built into the corresponding density and further refined as is displayed in the furthest on the right. (C) Marked-up monoclinic crystals in a stack from a challenging crystallization hit used for data collection. Green crosses and red numbers indicate where data were measured using a 10 &#181;m beam and 60&#176; rotation. Four of the resulting wedges were merged to produce a dataset at 1.75 &#197; resolution. Electron density around the Heme group is displayed on the right. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/65964/65964fig9.jpg" /> 
Figure 9: Serial crystallography in the crystallization plate. (A) Optical image of the crystallization drop, with a white box representing the region of interest. (B) Definition of grid scan points. (C) Heat map indicating diffraction. (D) Electron density map arising from a serial crystallography data set from greater than 9,000 still diffraction patterns. <a href="https://www.jove.com/files/ftp_upload/65964/65964fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Resolution (&#197;)</td>
			<td>Completeness (%)&#160;</td>
			<td>Multiplicity&#160;</td>
			<td>I/&#963;(I)&#160;</td>
			<td>Rsplit&#160;</td>
			<td>CC1/2&#160;</td>
			<td>Unique Observations&#160;</td>
		</tr>
		<tr>
			<td>Overall&#160;</td>
			<td>100&#160;</td>
			<td>95.5&#160;</td>
			<td>20.8&#160;</td>
			<td>0.063&#160;</td>
			<td>0.998&#160;</td>
			<td>8422&#160;</td>
		</tr>
		<tr>
			<td>Low (55.55 - 5.43)&#160;</td>
			<td>100&#160;</td>
			<td>147.1&#160;</td>
			<td>81.7&#160;</td>
			<td>0.028&#160;</td>
			<td>0.999&#160;</td>
			<td>488&#160;</td>
		</tr>
		<tr>
			<td>High (2.03 -2.00)&#160;</td>
			<td>100&#160;</td>
			<td>75.3&#160;</td>
			<td>1.2&#160;</td>
			<td>1.092&#160;</td>
			<td>0.410&#160;</td>
			<td>411&#160;</td>
		</tr>
	</tbody>
</table>
Table 1: Data statistics for the VMXi RT serial dataset. Abbreviations: I = mean intensity of the scaled observations; Rsplit = a measure of discrepancy of the measured intensities; CC 1/2 = correlation coefficient between two random halves of the dataset.

Watch this Scientific Journal Video about Advancing Protein Structure Analysis for Drug Development at JoVE.com

Author Spotlight: Advancing Protein Structure Analysis for Drug Development 

We present a protocol for the crystallization of proteins using the crystallization facility in the Research Complex at Harwell and subsequent in situ X-ray crystallographic data collection from crystals within the plates at Diamond&#39;s Versatile Macromolecular Crystallography in situ (VMXi) beamline. We describe sample requirements, crystallization protocols, and data collection guidelines.

Crystallization and In Situ Room Temperature Data Collection Using the Crystallization Facility at Harwell and Beamline VMXi, Diamond Light Source

Protocols for robotic protein crystallization using the Crystallization Facility at Harwell and in situ room temperature data collection from ...

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Research

JoVE Journal

Biochemistry

3.3K Views.  Harwell Science and Innovation Campus. We present a protocol for the crystallization of proteins using the crystallization facility in the Research Complex at Harwell and subsequent in situ X-ray crystallographic data collection from crystals within the plates at Diamond's Versatile Macromolecular Crystallography in situ (VMXi) beamline. We describe sample requirements, crystallization protocols, and data collection guidelines.

Video: Author Spotlight: Advancing Protein Structure Analysis for Drug Development 

Thermostabilization, Expression, Purification, and Crystallization of the Human Serotonin Transporter Bound to S-citalopram

The serotonin transporter is a sodium and chloride-coupled transporter that &#34;pumps&#34; extracellular serotonin into cells. S-citalopram is a drug used to treat depression and anxiety by binding to the serotonin transporter with high-affinity, blocking serotonin reuptake. Here we report an efficient procedure and a set of tools to stabilize, express, purify, and crystallize serotonin transporter-antibody complexes bound to S-citalopram and other antidepressants. Mutations which stabilize the serotonin transporter were identified using an S-citalopram binding assay. Serotonin transporter expressed in baculovirus-transduced HEK293S GnTI- cells, was reconstituted into proteoliposomes and used to raise high-affinity antibodies. We have developed a strategy to discover antibodies that are useful for structural studies. A straightforward approach for the expression of antibody fragments in Sf9 cells has also been established. Transporter-antibody complexes purified using this procedure are well-behaved and readily crystallize, producing complexes with S-citalopram that diffract X-rays to 3-4 &#197; resolution. The strategies developed here can be utilized to determine the structure of other challenging membrane proteins.

Thermostabilization, Expression, Purification, and Crystallization of the Human Serotonin Transporter Bound to S-citalopram

This manuscript describes how to screen for thermostabilizing mutations, purify the human serotonin transporter, generate high affinity antibodies, and crystallize the serotonin transporter-antibody complex bound to the antidepressant drug S-citalopram. This protocol can be adapted to the study of other challenging membrane transporters, receptors, and channels.

The serotonin transporter is a sodium and chloride-coupled transporter that &#34;pumps&#34; extracellular serotonin into cells. S-citalopram is a drug ...

Automated Protocols for Macromolecular Crystallization at the MRC Laboratory of Molecular Biology

When high quality crystals are obtained that diffract X-rays, the crystal structure may be solved at near atomic resolution. The conditions to crystallize proteins, DNAs, RNAs, and their complexes can however not be predicted. Employing a broad variety of conditions is a way to increase the yield of quality diffraction crystals. Two fully automated systems have been developed at the MRC Laboratory of Molecular Biology (Cambridge, England, MRC-LMB) that facilitate crystallization screening against 1,920 initial conditions by vapor diffusion in nanoliter droplets. Semi-automated protocols have also been developed to optimize conditions by changing the concentrations of reagents, the pH, or by introducing additives that potentially enhance properties of the resulting crystals. All the corresponding protocols will be described in detail and briefly discussed. Taken together, they enable convenient and highly efficient macromolecular crystallization in a multi-user facility, while giving the users control over key parameters of their experiments.

Automated systems and protocols for the routine preparation of a large number of screens and nanoliter crystallization droplets for vapor diffusion experiments are described and discussed.

When high quality crystals are obtained that diffract X-rays, the crystal structure may be solved at near atomic resolution. The conditions to crystallize ...

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092

Development of new antimicrobials and vaccines for Streptococcus pneumoniae (pneumococcus) are necessary to halt the rapid rise in multiple resistant strains. Carbohydrate substrate binding proteins (SBPs) represent viable targets for the development of protein-based vaccines and new antimicrobials because of their extracellular localization and the centrality of carbohydrate import for pneumococcal metabolism, respectively. Described here is a rationalized integrated protocol to carry out a comprehensive characterization of SP0092, which can be extended to other carbohydrate SBPs from the pneumococcus and other bacteria. This procedure can aid the structure-based design of inhibitors for this class of proteins. Presented in the first part of this manuscript are protocols for biochemical analysis by thermal shift assay, multi angle light scattering (MALS), and size exclusion chromatography (SEC), which optimize the stability and homogeneity of the sample directed to crystallization trials and so enhance the probability of success. The second part of this procedure describes the characterization of the SBP crystals using a tunable wavelength anomalous diffraction synchrotron beamline, and data collection protocols for measuring data that can be used to resolve the crystallized protein structure.

A streamlined protocol for performing an extensive biochemical and structural characterization of a carbohydrate substrate binding protein from Streptococcus pneumoniae is presented.

Development of new antimicrobials and vaccines for Streptococcus pneumoniae (pneumococcus) are necessary to halt the rapid rise in multiple resistant ...

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to precisely maneuver the nucleation and crystal growth towards desired sizes of protein crystals. The controlled crystallization is based on sample investigation with in situ Dynamic Light Scattering (DLS) while all visual changes in the droplet are monitored online with the help of a microscope coupled to a CCD camera, thus enabling a full investigation of the protein droplet during all stages of crystallization. The use of in situ DLS measurements throughout the entire experiment allows a precise identification of the highly supersaturated protein solution transitioning to a new phase &#8211; the formation of crystal nuclei. By identifying the protein nucleation stage, the crystallization can be optimized from large protein crystals to the production of protein microcrystals. The experimental protocol shows an interactive crystallization approach based on precise automated steps such as precipitant addition, water evaporation for inducing high supersaturation, and sample dilution for slowing induced homogeneous nucleation or reversing phase transitions.

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Here we present a protocol for controlled production of protein microcrystals. The process uses an automated device allowing controlled manipulation of several crystallization parameters. The protein crystallization is carried-out by controlled and automated addition of crystallization solutions while monitoring and investigating the radius distribution of particles in the crystallization droplet.

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to ...

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

High-brilliance X-ray beams coupled with automation have&#160;led to the use of synchrotron-based macromolecular X-ray crystallography (MX) beamlines for even the most challenging projects in structural biology. However, most facilities still require the presence of a scientist on site to perform the experiments. A new generation of automated beamlines dedicated to the fully automatic characterization of, and data collection from, crystals of biological macromolecules has recently been developed. These beamlines represent a new tool for structural biologists to screen the results of initial crystallization trials and/or the collection of large numbers of diffraction data sets, without users having to control the beamline themselves. Here we show how to set up an experiment for automatic screening and data collection, how an experiment is performed at the beamline, how the resulting data sets are processed, and how, when possible, the crystal structure of the biological macromolecule is solved.

Here, we describe how to use the automated screening and data collection options available at some synchrotron beamlines. Scientists send cryocooled samples to the synchrotron, and the diffraction properties are screened, the data sets are collected and processed and, where possible, a structure solution is carried out&#8212;all without human intervention.

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Analysis of SEC-SAXS data via EFA deconvolution and Scatter

BioSAXS is a popular technique used in molecular and structural biology to determine the solution structure, particle size and shape, surface-to-volume ratio and conformational changes of macromolecules and macromolecular complexes. A high quality SAXS dataset for structural modeling must be from monodisperse, homogeneous samples and this is often only reached by a combination of inline chromatography and immediate SAXS measurement. Most commonly, size-exclusion chromatography is used to separate samples and exclude contaminants and aggregations from the particle of interest allowing SAXS measurements to be made from a well-resolved chromatographic peak of a single protein species. Still, in some cases, even inline purification is not a guarantee of monodisperse samples, either because multiple components are too close to each other in size or changes in shape induced through binding alter perceived elution time. In these cases, it may be possible to deconvolute the SAXS data of a mixture to obtain the idealized SAXS curves of individual components. Here, we show how this is achieved and the practical analysis of SEC-SAXS data is performed on ideal and difficult samples. Specifically, we show the SEC-SAXS analysis of the vaccinia E9 DNA polymerase exonuclease minus mutant.

SEC-BioSAXS measurements of biological macromolecules are a standard approach for determining solution structure of macromolecules and their complexes. Here, we analyze SEC-BioSAXS data from two types of commonly encountered SEC traces&#8212;chromatograms with fully resolved and partially resolved peaks. We demonstrate the analysis and deconvolution using scatter and BioXTAS RAW.

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The preparation of well diffracting crystals and their handling before their X-ray analysis are two critical steps of biocrystallographic studies. We describe a versatile microfluidic chip that enables the production of crystals by the efficient method of counter-diffusion. The convection-free environment provided by the microfluidic channels is ideal for crystal growth and useful to diffuse a substrate into the active site of the crystalline enzyme. Here we applied this approach to the CCA-adding enzyme of the psychrophilic bacterium Planococcus halocryophilus in the presented example. After crystallization and substrate diffusion/soaking, the crystal structure of the enzyme:substrate complex was determined at room temperature by serial crystallography and the analysis of multiple crystals directly inside the chip. The whole procedure preserves the genuine diffraction properties of the samples because it requires no crystal handling.

A versatile microfluidic device is described that enables the crystallization of an enzyme using the counter-diffusion method, the introduction of a substrate in the crystals by soaking, and the 3D structure determination of the enzyme:substrate complex by a serial analysis of crystals inside the chip at room temperature.

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A Sample Preparation Pipeline for Microcrystals at the VMXm Beamline

The mounting of microcrystals (&#60;10 &#181;m) for single crystal cryo-crystallography presents a non-trivial challenge. Improvements in data quality have been seen for microcrystals with the development of beamline optics, beam stability and variable beam size focusing from submicron to microns, such as at the VMXm beamline at Diamond Light Source1. Further improvements in data quality will be gained through improvements in sample environment and sample preparation. Microcrystals inherently generate weaker diffraction, therefore improving the signal-to-noise is key to collecting quality X-ray diffraction data and will predominantly come from reductions in background noise. Major sources of X-ray background noise in a diffraction experiment are from their interaction with the air path before and after the sample, excess crystallization solution surrounding the sample, the presence of crystalline ice and scatter from any other beamline instrumentation or X-ray windows. The VMXm beamline comprises instrumentation and a sample preparation protocol to reduce all these sources of noise.
Firstly, an in-vacuum sample environment at VMXm removes the air path between X-ray source and sample. Next, sample preparation protocols for macromolecular crystallography at VMXm utilize a number of processes and tools adapted from cryoTEM. These include copper grids with holey carbon support films, automated blotting and plunge cooling robotics making use of liquid ethane. These tools enable the preparation of hundreds of microcrystals on a single cryoTEM grid with minimal surrounding liquid on a low-noise support. They also minimize the formation of crystalline ice from any remaining liquid surrounding the crystals. 
We present the process for preparing and assessing the quality of soluble protein microcrystals using visible light and scanning electron microscopy before mounting the samples on the VMXm beamline for X-ray diffraction experiments. We will also provide examples of good quality samples as well as those which require further optimization and strategies to do so.

The signal-to-noise ratio of data is one of the most important considerations in performing X-ray diffraction measurements from microcrystals. The VMXm beamline provides a low-noise environment and microbeam for such experiments. Here, we describe sample preparation methods for mounting and cooling microcrystals for VMXm and other microfocus macromolecular crystallography beamlines.

The mounting of microcrystals (&#60;10 &#181;m) for single crystal cryo-crystallography presents a non-trivial challenge. Improvements in data quality ...