Name: structure solution of the fluorescent protein cerulean using meshandcollect
Uploaded: 2019-03-19T13:00:00.000+00:00
Duration: 6 min 42 s
Description: Watch this Scientific Journal Video about Structure Solution of the Fluorescent Protein Cerulean Using MeshAndCollect at JoVE.com

We thank the ESRF for providing beam time through its in-house research program.

1. Expression and Purification of Cerulean
Note: This is based on the protocol published by Lelimousin et al.21
<ol>
	<li>Express His-tagged Cerulean in Escherichia coli BL21 cells grown at 37&#160;&#176;C in 4 L of auto inducible medium23 until OD600=1 and then incubate overnight at 27&#160;&#176;C.</li>
	<li>Harvest the bacterial cells at 5,000 x g and lyse the cells via sonication (40%, 5 min, 10 s pulse, 10 s pause) in 200 mL of buffer comprised of 20 mM Tris pH 8.0, 500 mM NaCl and 1x EDTA-free protease inhibitors.</li>
	<li>Load the supernatant on a His-trap Ni-NTA column and elute Cerulean with 100 mM imidazole in the same buffer conditions.</li>
	<li>Pool the bright yellow colored fractions. The protein is intrinsically colored, hence the Cerulean-containing fractions are easily distinguishable.</li>
	<li>Purify the protein (4 mL) on an S75 column in 20 mM Tris pH 8.0.</li>
	<li>Pool the bright yellow fractions and concentrate the protein solution to 15 mg/mL.</li>
</ol>
2. Crystallization
<ol>
	<li>Use the hanging drop vapor diffusion technique24 at 20&#160;&#176;C in Linbro plates. Fill the wells with 1 mL of a precipitant solution consisting of 100 mM HEPES at pH 6.75, 12% PEG8000 and 100 mM MgCl2. For the hanging drops, mix 1 &#181;L of protein concentrated to 15 mg/mL with 1 &#181;L of precipitant solution. Crystals should appear in 24 h.</li>
	<li>Harvest the crystals obtained and transfer them to 100 &#181;L of a seeding buffer comprised of 0.1 M HEPES pH 6.75, 22% PEG 8000.</li>
	<li>Grind the crystals with a 0.1 mL tissue grinder and dilute in seeding buffer (ratio 1:100).</li>
	<li>Digest an aliquot of the protein stock solution (15 mg/mL) with trypsin (0.5 mg/mL in the same buffer) for 1 h (1:10 (v/v)).</li>
	<li>Mix the digested protein solution with 10% of seed-containing buffer (v/v).</li>
	<li>Grow crystals (10*10*20 &#181;m3) in 0.1 M HEPES pH 7, 14% PEG 8000, 0.1 MgCl2 in 1-1.5 &#956;L hanging drops using the vapor diffusion method.</li>
</ol>
3. Crystal Mounting
<ol>
	<li>Use a suitable loop, e.g., a mesh loop 700 square holes of 25 &#181;m each mounted on a SPINE standard sample holder25. Transfer crystals from the crystallization drop (Step 2.6) into 1 &#181;L of cryoprotectant solution (the well precipitant solution mixed with glycerol (20% v/v final)).</li>
	<li>Mount the protein crystal slurry onto a mesh loop by moving the loop under the crystals and lifting them out of the drop. Ideally the crystals should be in the size range of 5 &#181;m &#8211; 30 &#181;m in maximum dimension with no overlap between crystals mounted in the loop.</li>
	<li>Wick off excess liquid by touching the mount quickly with filter paper. Sediment the crystals so that they sit in the plane of the loop surrounded by as little bulk liquid as possible.</li>
	<li>Plunge the mount into a unipuck full of liquid nitrogen. Store the puck at 100 K in a suitable storage container until beam time is available.</li>
</ol>
4. Offline preparation of the synchrotron experiment
Note: Request synchrotron beam time as early as possible and follow the online guidelines for available access types and on how to submit an application for a given synchrotron. The ESRF guidelines can be found at http://www.esrf.eu/UsersAndScience/UserGuide/Applying. If a member of an ESRF Block Allocation Group (BAG), an application for each specific project is not required. In this case experimenters should approach their BAG Responsible concerning the scheduling of beam time.
<ol>
	<li>After the proposal is accepted and an invitation for the experiment is received, have all participants complete safety training. Fill in the &#8220;A-form&#8221; (via the ESRF user portal, http://www.esrf.eu/UsersAndScience/UserGuide/Preparing/new-a-form) with the required safety information on the samples. Contact the local contact person to discuss the experiment. Once your A-form is submitted and validated it will give you the experiment number and password.</li>
	<li>Connect to extended ISPyB26 (http://www.esrf.fr/) and choose MX.</li>
	<li>Log in with the experiment number and the password from the A-form.</li>
	<li>Select Shipment | Add New and fill in the requested information.</li>
	<li>Select Add Parcel and fill in the relevant data. Select Add Container, choose a unipuck and fill in the information required, including the positions of the sample holders in the puck.</li>
</ol>
5. Loading of the Sample onto a Beamline
<ol>
	<li>In the experimental hutch, load the puck into the sample changer (SC) dewar and note its position.</li>
	<li>Interlock the experimental cabin and enter the control hutch.</li>
	<li>Log in to the ISPyB (https://esrf.fr/). Select Prepare Experiment, find the shipment, select Next and indicate the beamline and the puck position in the SC.</li>
	<li>Log in into the beamline control software, here MXCuBE227,28 with the experimental number and password provided on the A-form.
	<ol>
		<li>Press Sync to synchronize the beamline control software with the ISPyB database.</li>
	</ol>
	</li>
	<li>Use the beamline control software, to mount the sample holder onto the goniometer. In MXCuBE2, right click a position in the sample changer area and select Mount Sample.</li>
	<li>Taking advantage of the MK3 mini-kappa goniometer29 installed at most of the ESRF MX beamlines, use MXCuBE2&#8217;s &#8220;visual realignment&#8221; workflow30 to align the plane of the sample holder with the rotation axis of the goniometer.
	<ol>
		<li>Select the Centre button, then 3-click center on the middle of the edge of the tip of the loop. Save the centered position by selecting Save.</li>
		<li>Click again on the Centre button, then 3-click center the middle of the start of the stem of the loop. Save the second position as well by clicking on Save.</li>
		<li>Select one of the saved centered positions by clicking on it.</li>
		<li>Under Advanced, add the workflow Visual Reorientation to the MxCuBE2 data collection queue.</li>
		<li>Launch the workflow by clicking on Collect Queue.</li>
		<li>After the workflow aligns the plane of the sample holder with the rotation axis of the goniometer, center the sample holder again, this time somewhere in the middle of the mesh.</li>
	</ol>
	</li>
	<li>Orient the sample holder so that the face of the mesh is perpendicular to the X-ray beam direction by rotating the omega axis using MXCuBE2.</li>
	<li>In MXCuBE2, select the beam size required for the scanning of the sample holder (only for beamlines with variable beam size).
	<ol>
		<li>Click on the aperture drop down menu in the beamline control software and select a value, e.g., 10 &#181;m.</li>
	</ol>
	</li>
	<li>Define a mesh for the mesh scan.
	<ol>
		<li>Click on the mesh tool icon in MXCuBE2. The mesh tool window will appear.</li>
		<li>In the sample view of MXCuBE2, draw the mesh by left clicking and dragging the mouse over the area containing crystals on the sample holder.</li>
		<li>To save the mesh click on the Plus button in the mesh tool window (mesh becomes green).</li>
	</ol>
	</li>
</ol>
6. Prepare and Execute the MeshAndCollect Workflow
<ol>
	<li>In the Resolution field of MXCuBE2, enter the resolution (dmin) at which diffraction images should be collected, e.g., here 1.8 &#197;.</li>
	<li>Select MeshAndCollect in the Advanced data collection tab, add it to the queue and click Collect the Queue.</li>
	<li>In the parameter window which appears, use the beamline dependent default parameters. In the experiment described here defaults parameters are 0.037 s exposure time per mesh scan point, 100% transmission (leading in this case to 4 x 1011 ph/s), 1&#176; oscillation per mesh scan line.</li>
	<li>Click Continue. The mesh scan runs and the diffraction images collected at each grid point are analyzed and ranked according to diffraction strength with the software DOZOR1. This process runs in the background.</li>
	<li>After the DOZOR analysis a heat map is generated and the order for subsequent partial data collections is assigned automatically based on diffraction strength (see Figure 1). 
	Note: The results of this step can also be inspected in ISPyB. For the collection of partial data sets a new tab with settings pops up in the beamline control software, select suitable values for rotation range (i.e., 0.1&#176;), number of images (i.e., 100), exposure time, resolution, transmission, inverse beam etc. Ideally the dose for each wedge to be collected should be below the Garman limit (30 MGy). The approximate exposure time per image is 0.037 s to 0.1 s in the described experimental conditions.</li>
	<li>Click Continue to launch the partial data collections.</li>
</ol>
7. Data Processing
Note: The partial data sets are integrated with a suitable program (XDS10). For this a Python script will be used that recognizes each individual data set, integrates it and makes sure that indexing between the different partial data sets is consistent.
<ol>
	<li>Open the folder containing the images: /data/visitor/mxXXXX/beamline_name/date/RAW_DATA/Cerulean.</li>
	<li>Make a safety copy of the process subfolder that can be found in the folder where the partial data sets are collected.
	<ol>
		<li>On the Linux terminal, use the command cp &#8211;r process process_backup.</li>
	</ol>
	</li>
	<li>Navigate into the process folder and launch the processing script.
	<ol>
		<li>On the Linux terminal, type the command cd process and hit enter.</li>
		<li>Type procMultiCrystalData and hit enter. 
		Note: The script will ask for a space group and cell parameters (this information is optional), enter those according to the instructions. After a last user confirmation, the script will run automatically.</li>
	</ol>
	</li>
</ol>
8. Merging of Data Sets
Note: After all partial data sets are integrated the best combination of them are merged to produce the final data set for use in structure determination and refinement. Different aims of this merging process can be to obtain full completeness (highly recommended), high multiplicity or the best data statistics (high &#60;I/&#963;(I)&#62;, low R-factors, etc.). The latter can sometimes be at the expense of completeness and/or multiplicity so this option should be chosen with care.
<ol>
	<li>Merge the partial data sets using the program ccCluster14. It uses Hierarchical Cluster Analysis (HCA) to determine possible combinations of isomorphous partial data sets ().
	<ol>
		<li>Type ccCluster in the Unix terminal to open its graphical user interface (GUI). 
		Note: In the ccCluster GUI a dendrogram is drawn. This gives a suggestion as to which partial data sets might be best merged based on isomorphism between them.</li>
		<li>Click on a node that corresponds to a value of about 0.4 on the vertical axis. Generally, higher values will include more partial data sets but lead to worse merging statistics as partial data sets will be less isomorphous.</li>
		<li>Click on MERGE DATA. The selected cluster will be processed in the background and the estimated merging statistics will appear in a new tab in the GUI. This step can be repeated for different combinations of data sets. For a good combination of partial data sets the completeness should be close to 100%, the &#60;I/&#963;(I)&#62; values high (10 or higher in the lowest resolution shell) and the R-meas31 values low (around 5% in the low resolution shell).</li>
	</ol>
	</li>
	<li>For each combination selected use the generated input script to merge the partial data sets chosen into a single mtz file (i.e., pointless32).</li>
	<li>Definitively scale and merge the intensity data in this file using a scaling program (i.e., aimless32) and, as with a file originating from a single crystal data collection, use the output for subsequent phasing and structure solution33.</li>
</ol>

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The authors have nothing to disclose

The success of an MX experiment usually depends on the existence of relatively large, well diffracting crystals. For projects where optimization from small crystal showers to larger crystals fails, MeshAndCollect provides a possibility to obtain a complete diffraction dataset for structure solution via the combination of isomorphous partial data sets collected from a series of small crystals. The method is compatible with synchrotron beamlines for MX, ideally with a high photon flux and a small beam diameter, equipped with a state of the art diffractometer device and a fast-readout detector. On such an end station, the data collection part of such an experiment will take about 20 minutes, depending on the number of partial data sets to be collected and the number of crystal-containing sample holders to be analyzed.
The most important prerequisite for the success of a MeshAndCollect experiment is the existence of a sufficient number (at least 50, 100 ideally) of diffracting positions on the sample holder. From experience, the minimum size of the crystals to be analyzed should be about 5 &#181;m in the smallest dimension. The method is compatible with any kind of standard cryo-cooling compatible sample holders with the best results being achieved using mesh mounts that are rigid and straight.
At the ESRF, MeshAndCollect is implemented in a user-friendly manner in a Passerelle (http://isencia.be/passerelle-edm-en) workflow30 available from the MXCuBE2 beamline control software. A major advantage of MeshAndCollect compared to other SX methods is that the data collected can be processed by standard programs and automated pipelines used for single crystal MX.
As our example shows, MeshAndCollect is very easy to apply and leads to a series of partial diffraction data sets, usually collected from small crystals, which can be merged to produce a complete data set for use in structure solution. Moreover, MeshAndCollect has the potential to open up the sampling space of protein crystallography as it provides a way to collect usable data from crystallization trials where the last optimization step, the production of large crystals, is unsuccessful.
In the light of the current developments towards brighter X-ray sources (e.g., Extremely Brilliant Source (EBS) project/ESRF35) it is foreseeable that due to increased radiation damage, the type of multi-crystal data collection facilitated by MeshAndCollect will become the standard method of data collection, rather than an exception &#8211; as is currently the case - at synchrotron-based MX beamlines.

Macromolecular X-ray crystallography (MX) is, by far, the most used method for gaining atomic resolution insight into the three-dimensional structures of biological macromolecules. However, a major bottle necks is the requirement for relatively large, well diffracting crystals.
Often, and particularly when crystallizing membrane proteins, only very small crystals of a few microns in the largest dimension can be obtained. Radiation damage effects limit the resolution of a complete diffraction data set that can be collected from a single micro crystal2, and very often, it is necessary to improve the signal to noise ratio and hence data set resolution, by merging several partial diffraction data sets from different, but isomorphic crystals. The increases in flux density of X-ray beams at synchrotron sources and elsewhere (e.g. X-ray free-electron lasers (X-FELs)), have meant that useful partial diffraction data sets can be collected from even very small crystals of biological macromolecules. This, in turn, has led to the development of new techniques for the collection and merging of partial diffraction data sets collected from many different crystals in order to produce a complete data set for structure solution. Such techniques are commonly referred to as serial crystallography (SX)3,4,5,6,7,8. A prototypical example of SX is the use of injector devices to introduce a narrow stream of a crystal slurry into the X-ray beam3,4,5. A diffraction pattern is recorded every time a crystal is exposed to X-rays leading to the collection, from many thousands of individual crystals, of &#39;still&#39; diffraction images, information which is then merged to produce a complete data set. However, a considerable disadvantage of this type of serial data collection is that the processing of still images can be problematic. The data quality is considerably improved if crystals can be rotated and/or several diffraction images are collected from the same crystal during serial crystallography experiments6.
MeshAndCollect1 was developed with the aim of combining SX with &#39;standard&#39; MX rotation data collection and allows, in an automatic fashion, experimenters to collect partial diffraction data sets from numerous crystals of the same macromolecular target mounted on the same or different sample holders. A complete diffraction data set is then obtained by merging the most isomorphous of the partial data sets collected. MeshAndCollect is compatible with any state-of-the-art synchrotron X-ray beamline for MX (ideally an insertion device facility with a relatively small (20 &#181;m or less) beam size at the sample position). In addition to the compilation of complete data sets from a series of small, well-diffracting crystals, the method is also very suitable for the initial experimental assessment of the diffraction quality of micro-crystals and for the processing of opaque samples, e.g., in meso grown microcrystals of membrane proteins9.
At the start of a MeshAndCollect experiment, the positions, in two dimensions, of each of the many crystal contained in a single sample holder are determined using a low dose X-ray scan. The diffraction images collected during this scan are automatically analyzed by the program DOZOR1, which sorts the positions of the crystals on the sample holder according to their respective diffraction strength. Positions for the collection of partial data sets are assigned automatically based on a diffraction strength cut-off and, in the last step, small wedges of diffraction data, typically &#177;5&#176; of rotation, are collected from each chosen position. Experience has shown that this rotation range provides a sufficient amount of reflections per crystal for partial data set scaling purposes, while at the same time, reducing possible crystal centering issues and the chance of exposing multiple crystals in a particularly crowded support1. The individual diffraction data wedges (partial data sets) are then processed either manually or using automated data processing pipelines10,11,12,13. For downstream structure determination it is then necessary to find the best combination of partial data sets to be merged14,15,16 after which the resulting complete data set can be treated in the same way as one originating from a single crystal experiment.
As an example of MeshAndCollect in practice, we present here the solution of the crystal structure of the Cyan Fluorescent Protein (CFP) Cerulean, using a diffraction data set constructed from the combination of partial data sets collected from a series of microcrystals mounted on the same sample support. Cerulean has been engineered from the Green Fluorescent Protein (GFP) from the jellyfish Aequorea victoria17, whose fluorescent chromophore is autocatalytically formed from the cyclisation of three consecutive amino acid residues. Cerulean is obtained from GFP by mutating the first and second residues of the chromophore, a serine and a tyrosine, to threonine (S65T) and tryptophan (Y66W) respectively and adapting the chromophore environment with further mutations (Y145A, N146I, H148D, M153T and V163A) to produce a significant, yet suboptimal fluorescence level of QY = 0.4918,19,20. The suboptimal fluorescent properties of Cerulean have been proposed to be linked to complex protein dynamics involving the imperfect stabilization of one of the eleven &#946;-strands of the protein21 and to the accommodation of two different chromophore isomers depending on the pH and irradiation conditions22. We chose to work with Cerulean as a model protein illustrating the use of the MeshAndCollect protocol due to the relatively ease of tuning crystal size depending on the crystallization. The structure of Cerulean is very similar to that of its parent protein GFP, as it is constituted of a &#946;-barrel formed of eleven &#946;-strands surrounding an &#945;-helix, which bears the chromophore.

<table><tbody><tr><td>Beamline</td><td>ESRF ID 23-1</td><td></td><td></td></tr><tr><td>Concentrators: Amicon Ultra-4 Ultracel -30K</td><td>Merck Millipore</td><td>UFC803024</td><td></td></tr><tr><td>Crystallization plates XDXm with sealant</td><td>Hampton Research</td><td>HR3-306</td><td></td></tr><tr><td>EDTA- free protease inhibitors</td><td>Roche</td><td>4,693,159,001</td><td></td></tr><tr><td>Escherichia coli BL21 (DE3)</td><td>Life Technologies Thermo Fisher Scientific</td><td>C600003</td><td></td></tr><tr><td>glycerol</td><td>VWR Chemicals Prolabo</td><td>14388.29T</td><td></td></tr><tr><td>HEPES</td><td>Euromedex</td><td>10-110-C</td><td></td></tr><tr><td>His-trap HP</td><td>GE healthcare</td><td>17-5247-01</td><td></td></tr><tr><td>imidazole</td><td>Sigma-Aldrich</td><td>56750-500G</td><td></td></tr><tr><td>MgCl&lt;sub&gt;2&lt;/sub&gt;</td><td>Sigma-Aldrich</td><td>13452-1KG</td><td></td></tr><tr><td>MicroMeshes 700/25</td><td>MiTeGen</td><td>SKU:&amp;nbsp;M3-L18SP-25L</td><td></td></tr><tr><td>NaCl</td><td>Fisher Chemical</td><td>S/3160/60</td><td></td></tr><tr><td>PEG8000</td><td>Sigma-Aldrich</td><td>P5413-500G</td><td></td></tr><tr><td>Sonicator vibra cell 75/15</td><td>SONICS</td><td></td><td></td></tr><tr><td>Superdex 75 10/300 -GL</td><td>GE healthcare</td><td>17-5174-01</td><td></td></tr><tr><td>Tris base</td><td>Euromedex</td><td>26-128-3094-B</td><td></td></tr><tr><td>Trypsin</td><td>Sigma-Aldrich</td><td>T9201-1G</td><td></td></tr><tr><td>Unipuck</td><td>Molecular Dimensions</td><td>MD7-601</td><td></td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Programs&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>ISPyB</td><td>ESRF</td><td>Solange Delageni&amp;egrave;re, Patrice Brenchereau, Ludovic Launer, Alun W. Ashton, Ricardo Leal, St&amp;eacute;phanie Veyrier, Jos&amp;eacute; Gabadinho, Elspeth J. Gordon, Samuel D. Jones, Karl Erik Levik, Se&amp;aacute;n M. McSweeney, St&amp;eacute;phanie Monaco, Max Nanao, Darren Spruce, Olof Svensson, Martin A. Walsh, Gordon A. Leonard; ISPyB: an information management system for synchrotron macromolecular crystallography, Bioinformatics, Volume 27, Issue 22, 15 November 2011, Pages 3186&amp;ndash;3192, https://doi.org/10.1093/bioinformatics/btr535</td><td>local development</td></tr><tr><td>aimless</td><td>MRC Laboratory of Molecular Biology</td><td>Evans, P.R., Murshudov, G.N. How good are my data and what is the resolution? &lt;em&gt;Acta Crystallographica Section D Biological Crystallography&lt;/em&gt;. &lt;strong&gt;69&lt;/strong&gt; (7), 1204&amp;ndash;1214, doi: 10.1107/S0907444913000061 (2013).</td><td></td></tr><tr><td>ccCluster</td><td>ESRF</td><td>Santoni, G., Zander, U., Mueller-Dieckmann, C., Leonard, G., Popov, A. Hierarchical clustering for multiple-crystal macromolecular crystallography experiments: the &lt;em&gt;ccCluster&lt;/em&gt; program. &lt;em&gt;Journal of Applied Crystallography&lt;/em&gt;. &lt;strong&gt;50&lt;/strong&gt; (6), 1844&amp;ndash;1851, doi: 10.1107/S1600576717015229 (2017).</td><td>local development</td></tr><tr><td>DOZOR</td><td>ESRF</td><td>Bourenkov and Popov, unpublished</td><td>local development</td></tr><tr><td>MeshAndCollect workflow</td><td>ESRF</td><td>Zander, U. &lt;em&gt;et al.&lt;/em&gt; &lt;em&gt;MeshAndCollect&lt;/em&gt;: an automated multi-crystal data-collection workflow for synchrotron macromolecular crystallography beamlines. &lt;em&gt;Acta Crystallographica Section D Biological Crystallography&lt;/em&gt;. &lt;strong&gt;71&lt;/strong&gt; (11), 2328&amp;ndash;2343, doi: 10.1107/S1399004715017927 (2015).</td><td>local development</td></tr><tr><td>MXCuBE2</td><td>ESRF</td><td>Gabadinho, J. &lt;em&gt;et al.&lt;/em&gt; &lt;em&gt;MxCuBE&lt;/em&gt;: a synchrotron beamline control environment customized for macromolecular crystallography experiments. &lt;em&gt;Journal of Synchrotron Radiation&lt;/em&gt;. &lt;strong&gt;17&lt;/strong&gt; (5), 700&amp;ndash;707, doi: 10.1107/S0909049510020005 (2010). De Santis, D., Leonard, G. Notiziario Neutroni e Luce di Sincrotrone,Consiglio Nazionale delle Ricerche. (19), 24&amp;ndash;226 (2014).</td><td>local development</td></tr><tr><td>XDS</td><td>Max-Planck-Institut f&amp;uuml;r Medizinische Forschung</td><td>Kabsch, W. &lt;em&gt;XDS&lt;/em&gt;. &lt;em&gt;Acta Crystallographica Section D Biological Crystallography&lt;/em&gt;. &lt;strong&gt;66&lt;/strong&gt; (2), 125&amp;ndash;132, doi: 10.1107/S0907444909047337 (2010)</td><td></td></tr></tbody></table>

structure solution of the fluorescent protein cerulean using meshandcollect

X-ray crystallography is the major technique used to obtain high resolution information concerning the 3-dimensional structures of biological macromolecules. Until recently, a major requirement has been the availability of relatively large, well diffracting crystals, which are often challenging to obtain. However, the advent of serial crystallography and a renaissance in multi-crystal data collection methods has meant that the availability of large crystals need no longer be a limiting factor. Here, we illustrate the use of the automated MeshAndCollect protocol, which first identifies the positions of many small crystals mounted on the same sample holder and then directs the collection from the crystals of a series of partial diffraction data sets for subsequent merging and use in structure determination. MeshAndCollect can be applied to any type of micro-crystals, even if weakly diffracting. As an example, we present here the use of the technique to solve the crystal structure of the Cyan Fluorescent Protein (CFP) Cerulean.

MeshAndCollect, as implemented in MXCuBE2 (see Figure 1A), was used for the collection of partial diffraction data sets from small crystals of Cerulean located on the same sample holder in which visual identification of crystals was difficult. To screen the sample holder, we drew a grid over the center of the meshloop (see Figure 1B) and based on the DOZOR score heat map (see Figures 1C, 1D) 85 partial diffraction data sets were automatically collected. These were individually integrated then merged (see above) to produce a data set with 99.8% completeness at dmin = 1.7&#197; (see Table 1). Half-set correlation (CC1/2)34 in the highest resolution shell was 60% ( = 4.7). As expected, the crystal structure of Cerulean could be straightforwardly solved by molecular replacement33 using the data set generated. After refinement, we obtained an Rwork of 22.8% and an Rfree of 25.4%. Superposition with the previously determined structure (PDB entry 2WSO21) shows a global rmsd on C&#945; positions of 0.1 &#197;. 
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Statistics of the merged data set</td>
			<td></td>
		</tr>
		<tr>
			<td>Clustering Threshold</td>
			<td>0.35</td>
		</tr>
		<tr>
			<td>Number of partial datasets</td>
			<td>25</td>
		</tr>
		<tr>
			<td>Space Group</td>
			<td>P212121</td>
		</tr>
		<tr>
			<td>Unit Cell (a, b, c)</td>
			<td>50.98, 62.76, 69.50</td>
		</tr>
		<tr>
			<td>Resolution Range</td>
			<td>46.58-1.70 (1.73-1.70)</td>
		</tr>
		<tr>
			<td>Rmerge (all I+ and I-)</td>
			<td>0.133 (0.743)</td>
		</tr>
		<tr>
			<td>Rmeas (all I+ &#38; I-)</td>
			<td>0.142 (0.813)</td>
		</tr>
		<tr>
			<td>Rpim (all I+ &#38; I-)</td>
			<td>0.047 (0.318)</td>
		</tr>
		<tr>
			<td>Observations total/unique</td>
			<td>220693/25129</td>
		</tr>
		<tr>
			<td>Mean((I)/sd(I))</td>
			<td>13.8 (4.7)</td>
		</tr>
		<tr>
			<td>Mn(I) half-set correlation CC(1/2)</td>
			<td>0.994 (0.602)</td>
		</tr>
		<tr>
			<td>Completeness</td>
			<td>99.8 (99.5)</td>
		</tr>
		<tr>
			<td>Multiplicity</td>
			<td>8.8 (6.5)</td>
		</tr>
		<tr>
			<td>Final Rcryst</td>
			<td>22.8</td>
		</tr>
		<tr>
			<td>Final Rfree</td>
			<td>25.4</td>
		</tr>
	</tbody>
</table>
Table 1: Statistics of the merged data set indicating the high quality of the data collected.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58594/58594fig1.jpg" /> 
Figure 1: Using MeshAndCollect to collect a series of partial data sets from a series of small crystals contained in the same sample holder. A) User-interface of MXCuBE2. The green oval over the on-axis viewer field indicates the grid tool. B) With it a grid is drawn onto the image of sample holder in the life image field. C) Heat map of the DOZOR scores. D) Example of a diffraction image. E) Dendrogram after hierarchical cluster analysis. Data sets in red were used for merging. F) Overall structure of Cerulean. <a href="https://www.jove.com/files/ftp_upload/58594/58594fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Structure Solution of the Fluorescent Protein Cerulean Using MeshAndCollect at JoVE.com

Structure Solution of the Fluorescent Protein Cerulean Using MeshAndCollect

We present the use of the MeshAndCollect protocol to obtain a complete diffraction data set, for use in subsequent structure determination, composed of partial diffraction data sets collected from many small crystals of the fluorescent protein Cerulean.

X-ray crystallography is the major technique used to obtain high resolution information concerning the 3-dimensional structures of biological ...

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Research

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Biochemistry

5.7K Views.  Structural Biology Group. We present the use of the MeshAndCollect protocol to obtain a complete diffraction data set, for use in subsequent structure determination, composed of partial diffraction data sets collected from many small crystals of the fluorescent protein Cerulean.

Video: Structure Solution of the Fluorescent Protein Cerulean Using MeshAndCollect

Expression of Fluorescent Proteins in Branchiostoma lanceolatum by mRNA Injection into Unfertilized Oocytes

We report here a robust and efficient protocol for the expression of fluorescent proteins after mRNA injection into unfertilized oocytes of the cephalochordate amphioxus, Branchiostoma lanceolatum. We use constructs for membrane and nuclear targeted mCherry and eGFP that have been modified to accommodate amphioxus codon usage and Kozak consensus sequences. We describe the type of injection needles to be used, the immobilization protocol for the unfertilized oocytes, and the overall injection set-up. This technique generates fluorescently labeled embryos, in which the dynamics of cell behaviors during early development can be analyzed using the latest in vivo imaging strategies. The development of a microinjection technique in this amphioxus species will allow live imaging analyses of cell behaviors in the embryo as well as gene-specific manipulations, including gene overexpression and knockdown. Altogether, this protocol will further consolidate the basal chordate amphioxus as an animal model for addressing questions related to the mechanisms of embryonic development and, more importantly, to their evolution.

Expression of Fluorescent Proteins in Branchiostoma lanceolatum by mRNA Injection into Unfertilized Oocytes

We report here the robust and efficient expression of fluorescent proteins after mRNA injection into unfertilized oocytes of Branchiostoma lanceolatum. The development of the microinjection technique in this basal chordate will pave the way for far-reaching technical innovations in this emerging model system, including in vivo imaging and gene-specific manipulations.

We report here a robust and efficient protocol for the expression of fluorescent proteins after mRNA injection into unfertilized oocytes of the ...

Developmental Biology

Imaging Subcellular Structures in the Living Zebrafish Embryo

In vivo imaging provides unprecedented access to the dynamic behavior of cellular and subcellular structures in their natural context. Performing such imaging experiments in higher vertebrates such as mammals generally requires surgical access to the system under study. The optical accessibility of embryonic and larval zebrafish allows such invasive procedures to be circumvented and permits imaging in the intact organism. Indeed the zebrafish is now a well-established model to visualize dynamic cellular behaviors using in vivo microscopy in a wide range of developmental contexts from proliferation to migration and differentiation. A more recent development is the increasing use of zebrafish to study subcellular events including mitochondrial trafficking and centrosome dynamics. The relative ease with which these subcellular structures can be genetically labeled by fluorescent proteins and the use of light microscopy techniques to image them is transforming the zebrafish into an in vivo model of cell biology. Here we describe methods to generate genetic constructs that fluorescently label organelles, highlighting mitochondria and centrosomes as specific examples. We use the bipartite Gal4-UAS system in multiple configurations to restrict expression to specific cell-types and provide protocols to generate transiently expressing and stable transgenic fish. Finally, we provide guidelines for choosing light microscopy methods that are most suitable for imaging subcellular dynamics.

Imaging the dynamic behavior of organelles and other subcellular structures in vivo can shed light on their function in physiological and disease conditions. Here, we present methods for genetically tagging two organelles, centrosomes and mitochondria, and imaging their dynamics in living zebrafish embryos using wide-field and confocal microscopy.

In vivo imaging provides unprecedented access to the dynamic behavior of cellular and subcellular structures in their natural context. Performing such ...

Luminescence Resonance Energy Transfer to Study Conformational Changes in Membrane Proteins Expressed in Mammalian Cells

Luminescence Resonance Energy Transfer, or LRET, is a powerful technique used to measure distances between two sites in proteins within the distance range of 10-100 &#197;. By measuring the distances under various ligated conditions, conformational changes of the protein can be easily assessed. With LRET, a lanthanide, most often chelated terbium, is used as the donor fluorophore, affording advantages such as a longer donor-only emission lifetime, the flexibility to use multiple acceptor fluorophores, and the opportunity to detect sensitized acceptor emission as an easy way to measure energy transfer without the risk of also detecting donor-only signal. Here, we describe a method to use LRET on membrane proteins expressed and assayed on the surface of intact mammalian cells. We introduce a protease cleavage site between the LRET fluorophore pair. After obtaining the original LRET signal, cleavage at that site removes the specific LRET signal from the protein of interest allowing us to quantitatively subtract the background signal that remains after cleavage. This method allows for more physiologically relevant measurements to be made without the need for purification of protein.

We describe here an improved Luminescence Resonance Energy Transfer (LRET) method where we introduce a protease cleavage site between the donor and acceptor fluorophore sites. This modification allows us to obtain specific LRET signals arising from membrane proteins of interest, allowing for the study of membrane proteins without protein purification.

Luminescence Resonance Energy Transfer, or LRET, is a powerful technique used to measure distances between two sites in proteins within the distance range ...

Bioengineering

Photoconversion of Purified Fluorescent Proteins and Dual-probe Optical Highlighting in Live Cells

Photoconvertible fluorescent proteins (pc-FPs) are a class of fluorescent proteins with "optical highlighter" capability, meaning that the color of fluorescence can be changed by exposure to light of a specific wavelength. Optical highlighting allows noninvasive marking of a subpopulation of fluorescent molecules, and is therefore ideal for tracking single cells or organelles.
Critical parameters for efficient photoconversion are the intensity and the exposure time of the photoconversion light. If the intensity is too low, photoconversion will be slow or not occur at all. On the other hand, too much intensity or too long exposure can photobleach the protein and thereby reduce the efficiency of photoconversion.
This protocol describes a general approach how to set up a confocal laser scanning microscope for pc-FP photoconversion applications. First, we describe a procedure for preparing purified protein droplet samples. This sample format is very convenient for studying the photophysical behavior of fluorescent proteins under the microscope. Second, we will use the protein droplet sample to show how to configure the microscope for photoconversion. And finally, we will show how to perform optical highlighting in live cells, including dual-probe optical highlighting with mOrange2 and Dronpa.

This protocol describes a general approach to perform photoconversion of fluorescent proteins on a confocal laser scanning microscope. We describe procedures for the photoconversion of puried protein samples, as well as for dual-probe optical highlighting in live cells with mOrange2 and Dronpa.

Photoconvertible fluorescent proteins (pc-FPs) are a class of fluorescent proteins with "optical highlighter" capability, meaning that the color of ...

Biology

Engineering 'Golden' Fluorescence by Selective Pressure Incorporation of Non-canonical Amino Acids and Protein Analysis by Mass Spectrometry and Fluorescence

Fluorescent proteins are fundamental tools for the life sciences, in particular for fluorescence microscopy of living cells. While wild-type and engineered variants of the green fluorescent protein from Aequorea victoria (avGFP) as well as homologs from other species already cover large parts of the optical spectrum, a spectral gap remains in the near-infrared region, for which avGFP-based fluorophores are not available. Red-shifted fluorescent protein (FP) variants would substantially expand the toolkit for spectral unmixing of multiple molecular species, but the naturally occurring red-shifted FPs derived from corals or sea anemones have lower fluorescence quantum yield and inferior photo-stability compared to the avGFP variants. Further manipulation and possible expansion of the chromophore&#39;s conjugated system towards the far-red spectral region is also limited by the repertoire of 20 canonical amino acids prescribed by the genetic code. To overcome these limitations, synthetic biology can achieve further spectral red-shifting via insertion of non-canonical amino acids into the chromophore triad. We describe the application of SPI to engineer avGFP variants with novel spectral properties. Protein expression is performed in a tryptophan-auxotrophic E. coli strain and by supplementing growth media with suitable indole precursors. Inside the cells, these precursors are converted to the corresponding tryptophan analogs and incorporated into proteins by the ribosomal machinery in response to UGG codons. The replacement of Trp-66 in the enhanced &#34;cyan&#34; variant of avGFP (ECFP) by an electron-donating 4-aminotryptophan results in GdFP featuring a 108 nm Stokes shift and a strongly red-shifted emission maximum (574 nm), while being thermodynamically more stable than its predecessor ECFP. Residue-specific incorporation of the non-canonical amino acid is analyzed by mass spectrometry. The spectroscopic properties of GdFP are characterized by time-resolved fluorescence spectroscopy as one of the valuable applications of genetically encoded FPs in life sciences.

Synthetic biology enables the engineering of proteins with unprecedented properties using the co-translational insertion of non-canonical amino acids. Here, we presented how a spectrally red-shifted variant of a GFP-type fluorophore with novel fluorescence spectroscopic properties, termed &#34;gold&#34; fluorescent protein (GdFP), is produced in E. coli via selective pressure incorporation (SPI).

Fluorescent proteins are fundamental tools for the life sciences, in particular for fluorescence microscopy of living cells. While wild-type and ...

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Two electrode voltage clamp electrophysiology (TEVC) is a powerful tool to investigate the mechanism of ion transport1 for a wide variety of membrane proteins including ion channels2, ion pumps3, and transporters4. Recent developments have combined site-specific fluorophore labeling alongside TEVC to 
concurrently examine the conformational dynamics at specific residues and function of these proteins on the surface of single cells.
We will describe a method to study the conformational dynamics of membrane proteins by simultaneously monitoring fluorescence and current changes using voltage-clamp fluorometry. This approach can be used to examine the molecular motion of membrane proteins site-specifically following cysteine replacement and site-directed fluorophore labeling5,6. Furthermore, this method provides an approach to determine distance constraints between specific residues7,8. 
This is achieved by selectively attaching donor and acceptor fluorophores to two mutated cysteine residues of interest.
In brief, these experiments are performed following functional expression of the desired protein on the surface of Xenopus leavis oocytes. The large surface area of these oocytes enables facile functional measurements and a robust fluorescence signal5. It is also possible to readily change the extracellular conditions such as pH, ligand or cations/anions, which can provide further information on the mechanism of membrane proteins4. Finally, recent developments 
have also enabled the manipulation of select internal ions following co-expression with a second protein9.
Our protocol is described in multiple parts. First, cysteine scanning mutagenesis proceeded by fluorophore labeling is completed at residues located at the interface of the transmembrane and extracellular domains. Subsequent experiments are designed to identify residues which demonstrate large changes in fluorescence intensity (&lt;5%)3 upon a conformational change of the protein. Second, these changes in fluorescence intensity are compared to the kinetic parameters of 
the membrane protein in order to correlate the conformational dynamics to the function of the protein10. This enables a rigorous biophysical analysis of the molecular motion of the target protein. Lastly, two residues of the holoenzyme can be labeled with a donor and acceptor fluorophore in order to determine distance constraints using donor photodestruction methods. It is also possible to monitor the relative movement of protein subunits following labeling with a donor and acceptor fluorophore.

Examining the Conformational Dynamics of Membrane Proteins in situ with Site-directed Fluorescence Labeling

We will describe a method which measures the kinetics of ion transport of membrane proteins alongside site-specific analysis of conformational changes using fluorescence on single cells. This technique is adaptable to ion channels, transporters and ion pumps and can be utilized to determine distance constraints between protein subunits.

Two electrode voltage clamp electrophysiology (TEVC) is a powerful tool to investigate the mechanism of ion transport1 for a wide variety of membrane ...

Residue-Specific Exchange of Proline by Proline Analogs in Fluorescent Proteins: How "Molecular Surgery" of the Backbone Affects Folding and Stability

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often detrimental to protein folding and, in particular, chromophore maturation in green fluorescent proteins and related variants. A reasonable alternative is to manipulate the translation of the protein so that all Pro residues are replaced residue-specifically by analogs, a method known as selective pressure incorporation (SPI). The built-in chemical modifications can be used as a kind of &#34;molecular surgery&#34; to finely dissect measurable changes or even rationally manipulate different protein properties. Here, the study demonstrates the usefulness of the SPI method to study the role of prolines in the organization of the typical &#946;-barrel structure of spectral variants of the green fluorescent protein (GFP) with 10-15 prolines in their sequence: enhanced green fluorescent protein (EGFP), NowGFP, and KillerOrange. Pro residues are present in connecting sections between individual &#946;-strands and constitute the closing lids of the barrel scaffold, thus being responsible for insulation of the chromophore from water, i.e., fluorescence properties. Selective pressure incorporation experiments with (4R)-fluoroproline (R-Flp), (4S)-fluoroproline (S-Flp), 4,4-difluoroproline (Dfp), and 3,4-dehydroproline (Dhp) were performed using a proline-auxotrophic E. coli strain as expression host. We found that fluorescent proteins with S-Flp and Dhp are active (i.e., fluorescent), while the other two analogs (Dfp and R-Flp) produced dysfunctional, misfolded proteins. Inspection of UV-Vis absorption and fluorescence emission profiles showed few characteristic alterations in the proteins containing Pro analogs. Examination of the folding kinetic profiles in EGFP variants showed an accelerated refolding process in the presence of S-Flp, while the process was similar to wild-type in the protein containing Dhp. This study showcases the capacity of the SPI method to produce subtle modifications of protein residues at an atomic level (&#34;molecular surgery&#34;), which can be adopted for the study of other proteins of interest. It illustrates the outcomes of proline replacements with close chemical analogs on the folding and spectroscopic properties in the class of &#946;-barrel fluorescent proteins.

To overcome the limitations of classical site-directed mutagenesis, proline analogs with specific modifications were incorporated into several fluorescent proteins. We show how the replacement of hydrogen by fluorine or of the single by double bonds in proline residues ("molecular surgery") affects fundamental protein properties, including their folding and interaction with light.

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often ...

Effect of Fluorescent Proteins on Fusion Partners Using Polyglutamine Toxicity Assays in Yeast

For the investigation of protein localization and trafficking using live cell imaging, researchers often rely on fusing their protein of interest to a fluorescent reporter. The constantly evolving list of genetically encoded fluorescent proteins (FPs) presents users with several alternatives when it comes to fluorescent fusion design. Each FP has specific optical and biophysical properties that can affect the biochemical, cellular, and functional properties of the resulting fluorescent fusions. For instance, several FPs tend to form nonspecific oligomers that are susceptible to impede on the function of the fusion partner. Unfortunately, only a few methods exist to test the impact of FPs on the behavior of the fluorescent reporter. Here, we describe a simple method that enables the rapid assessment of the impact of FPs using polyglutamine (polyQ) toxicity assays in the budding yeast Saccharomyces cerevisiae. PolyQ-expanded huntingtin proteins are associated with the onset of Huntington&#39;s disease (HD), where the expanded huntingtin aggregates into toxic oligomers and inclusion bodies. The aggregation and toxicity of polyQ expansions in yeast are highly dependent on the sequences flanking the polyQ region, including the presence of fluorescent tags, thus providing an ideal experimental platform to study the impact of FPs on the behavior of their fusion partner.

This article describes protocols to assess the effect of fluorescent proteins on the aggregation and toxicity of misfolded polyglutamine expansion for the rapid evaluation of a newly uncharacterized fluorescent protein in the context of fluorescent reporters.

For the investigation of protein localization and trafficking using live cell imaging, researchers often rely on fusing their protein of interest to a ...

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion

Type three secretion systems (T3SSs) enable gram-negative bacteria to inject a battery of effector proteins directly into the cytosol of eukaryotic host cells. Upon entry, the injected effector proteins cooperatively modulate eukaryotic signaling pathways and reprogram cellular functions, enabling bacterial entry and survival. Monitoring and localizing these secreted effector proteins in the context of infections provides a footprint for defining the dynamic interface of host-pathogen interactions. However, labeling and imaging bacterial proteins in host cells without disrupting their structure/function is technically challenging. 
Constructing fluorescent fusion proteins does not resolve this problem, because the fusion proteins jam the secretory apparatus and thus are not secreted. To overcome these obstacles, we recently employed a method for site-specific fluorescent labeling of bacterial secreted effectors, as well as other difficult-to-label proteins, using genetic code expansion (GCE). This paper provides a complete step-by-step protocol to label Salmonella secreted effectors using GCE site-specifically, followed by directions for imaging the subcellular localization of secreted proteins in HeLa cells using direct stochastic optical reconstruction microscopy (dSTORM) 
Recent findings suggest that the incorporation of non-canonical amino acids (ncAAs) via GCE, followed by bio-orthogonal labeling with tetrazine-containing dyes, is a viable technique for selective labeling and visualization of bacterial secreted proteins and subsequent image analysis in the host. The goal of this article is to provide a straightforward and clear protocol that can be employed by investigators interested in conducting super-resolution imaging using GCE to study various biological processes in bacteria and viruses, as well as host-pathogen interactions.

This article provides a straightforward and clear protocol to label Salmonella secreted effectors using genetic code expansion (GCE) site-specifically and image the subcellular localization of secreted proteins in HeLa cells using direct stochastic optical reconstruction microscopy (dSTORM)

Type three secretion systems (T3SSs) enable gram-negative bacteria to inject a battery of effector proteins directly into the cytosol of eukaryotic host ...

Novel Protocol for Simultaneous Fluorescence and Ultrastructure Preservation

Epon Post Embedding Correlative Light and Electron Microscopy

Correlative light and electron microscopy (CLEM) is a comprehensive microscopy that combines the localization information provided by fluorescence microscopy (FM) and the context of cellular ultrastructure acquired by electron microscopy (EM). CLEM is a trade-off between fluorescence and ultrastructure, and usually, ultrastructure compromises fluorescence. Compared with other hydrophilic embedding resins, such as glycidyl methacrylate, HM20, or K4M, Epon is superior in ultrastructure preservation and sectioning properties. Previously, we had demonstrated that mEosEM can survive osmium tetroxide fixation and Epon embedding. Using mEosEM, we achieved, for the first time, Epon post embedding CLEM, which maintains the fluorescence and the ultrastructure simultaneously. Here, we provide step-by-step details about the EM sample preparation, the FM imaging, the EM imaging, and the image alignment. We also improve the procedures for identifying the same cell imaged by FM imaging during the EM imaging and detail the registration between the FM and EM images. We believe one can easily achieve Epon post embedding correlative light and electron microscopy following this new protocol in traditional EM facilities.

Author Spotlight: Advancements in Correlative Light and Electron Microscopy with Fluorescent Protein Preservation

We present a detailed protocol for Epon post-embedding correlative light and electron microscopy using a fluorescent protein called mScarlet. This method can maintain the fluorescence and the ultrastructure simultaneously. This technique is amenable to a wide variety of biological applications.

Structure Solution of the Fluorescent Protein Cerulean Using MeshAndCollect

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