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>MgCl2</td>
			<td>Sigma-Aldrich</td>
			<td>13452-1KG</td>
			<td></td>
		</tr>
		<tr>
			<td>MicroMeshes 700/25</td>
			<td>MiTeGen</td>
			<td>SKU:&#160;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>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Programs</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>ISPyB</td>
			<td>ESRF</td>
			<td>Solange Delageni&#232;re, Patrice Brenchereau, Ludovic Launer, Alun W. Ashton, Ricardo Leal, St&#233;phanie Veyrier, Jos&#233; Gabadinho, Elspeth J. Gordon, Samuel D. Jones, Karl Erik Levik, Se&#225;n M. McSweeney, St&#233;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&#8211;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? Acta Crystallographica Section D Biological Crystallography. 69 (7), 1204&#8211;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 ccCluster program. Journal of Applied Crystallography. 50 (6), 1844&#8211;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. et al. MeshAndCollect: an automated multi-crystal data-collection workflow for synchrotron macromolecular crystallography beamlines. Acta Crystallographica Section D Biological Crystallography. 71 (11), 2328&#8211;2343, doi: 10.1107/S1399004715017927 (2015).</td>
			<td>local development</td>
		</tr>
		<tr>
			<td>MXCuBE2</td>
			<td>ESRF</td>
			<td>Gabadinho, J. et al. MxCuBE: a synchrotron beamline control environment customized for macromolecular crystallography experiments. Journal of Synchrotron Radiation. 17 (5), 700&#8211;707, doi: 10.1107/S0909049510020005 (2010). De Santis, D., Leonard, G. Notiziario Neutroni e Luce di Sincrotrone,Consiglio Nazionale delle Ricerche. (19), 24&#8211;226 (2014).</td>
			<td>local development</td>
		</tr>
		<tr>
			<td>XDS</td>
			<td>Max-Planck-Institut f&#252;r Medizinische Forschung</td>
			<td>Kabsch, W. XDS. Acta Crystallographica Section D Biological Crystallography. 66 (2), 125&#8211;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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5.6K 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

Using Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating these studies, reincorporation of detergent-solubilized membrane proteins into liposomes is a stochastic process where protein topology is impossible to enforce. This paper offers an alternative method to these challenging techniques that utilizes a liposome-based scaffold. Protein solubility is enhanced by deletion of the transmembrane domain, and these amino acids are replaced with a tethering moiety, such as a His-tag. This tether interacts with an anchoring group (Ni2+ coordinated by nitrilotriacetic acid (NTA(Ni2+)) for His-tagged proteins), which enforces a uniform protein topology at the surface of the liposome. An example is presented wherein the interaction between Dynamin-related protein 1 (Drp1) with an integral membrane protein, Mitochondrial Fission Factor (Mff), was investigated using this scaffold liposome method. In this work, we have demonstrated the ability of Mff to efficiently recruit soluble Drp1 to the surface of liposomes, which stimulated its GTPase activity. Moreover, Drp1 was able to tubulate the Mff-decorated lipid template in the presence of specific lipids. This example demonstrates the effectiveness of scaffold liposomes using structural and functional assays and highlights the role of Mff in regulating Drp1 activity.

Using Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

This paper describes a method for assessing the interactions and assemblies of integral membrane proteins in vitro with various partner factors in a lipid-proximal environment.

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating ...

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. Hydrogen-Deuterium Exchange (HDX) measured at rapid time scales is uniquely suited to detect structures and hydrogen bonding networks that are briefly populated, allowing for the characterization of transient conformers in native ensembles. Coupling of HDX to mass spectrometry offers several key advantages, including high sensitivity, low sample consumption and no restriction on protein size. This technique has advanced greatly in the last several decades, including the ability to monitor HDX labeling times on the millisecond time scale. In addition, by incorporating the HDX workflow onto a microfluidic platform housing an acidic protease microreactor, we are able to localize dynamic properties at the peptide level. In this study, Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) coupled to HDX was used to provide a detailed picture of residual structure in the tau protein, as well as the conformational shifts induced upon hyperphosphorylation.

Conformational flexibility plays a critical role in protein function. Herein, we describe the use of time-resolved electrospray ionization mass spectrometry coupled to hydrogen-deuterium exchange for probing the rapid structural changes that drive function in ordered and disordered proteins.

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. ...

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality for: (1) The identification of related protein structure sets to user specified thresholds of similarity; (2) Their multiple alignment and structure superposition; (3) Sequence and structure conservation analysis; (4) Inter-conformer relationship mapping with principal component analysis, and (5) comparison of predicted internal dynamics via ensemble normal mode analysis. This integrated functionality provides a complete online workflow for investigating sequence-structure-dynamic relationships within protein families and superfamilies.

A protocol for the online investigation of protein sequence-structure-dynamics relationships using Bio3D-web is presented.

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality ...

Affinity Purification of Chloroplast Translocon Protein Complexes Using the TAP Tag

Chloroplast biogenesis requires the import of thousands of nucleus-encoded proteins into the plastid. The import of these proteins depends on the translocon at the outer (TOC) and inner (TIC) chloroplast membranes. The TOC and TIC complexes are multimeric and probably contain yet unknown components. One of the main goals in the field is to establish the complete inventory of TOC and TIC components. For the isolation of TOC-TIC complexes and the identification of new components, the preprotein receptor TOC159 has been modified N-terminally by the addition of the tandem affinity purification (TAP) tag resulting in TAP-TOC159. The TAP-tag is designed for two sequential affinity purification steps (hence "tandem affinity"). The TAP-tag used in these studies consists of a N-terminal IgG-binding domain derived from Staphylococcus aureus Protein A (ProtA) followed by a calmodulin-binding peptide (CBP). Between these two affinity tags, a tobacco etch virus (TEV) protease cleavage site has been included. Therefore, TEV protease can be used for gentle elution of TOC159-containing complexes after binding to IgG beads. In the protocol presented here, the second Calmodulin-affinity purification step was omitted. The purification protocol starts with the preparation and solubilization of total cellular membranes. After the detergent-treatment, the solubilized membrane proteins are incubated with IgG beads for the immunoisolation of TAP-TOC159-containing complexes. Upon binding and extensive washing, TAP-TOC159 containing complexes are cleaved and released from the IgG beads using the TEV protease whereby the S. aureus IgG-binding domain is removed. Western blotting of the isolated TOC159-containing complexes can be used to confirm the presence of known or suspected TOC and TIC proteins. More importantly, the TOC159-containing complexes have been used successfully to identify new components of the TOC and TIC complexes by mass spectrometry. The protocol that we present potentially allows the efficient isolation of any membrane-bound protein complex to be used for the identification of yet unknown components by mass spectrometry.

We here present a proven and tested protocol for the purification of chloroplast protein import complexes (TOC-TIC complex) using the TAP-tag. The one-step affinity-isolation protocol can potentially be applied to any protein and be used to identify new interaction partners by mass spectrometry.

Chloroplast biogenesis requires the import of thousands of nucleus-encoded proteins into the plastid. The import of these proteins depends on the ...

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Protein-protein interactions involving proteins with multiple globular domains present technical challenges for determining how such complexes form and how the domains are oriented/positioned. Here, a protocol with the potential for elucidating which specific domains mediate interactions in multicomponent system through ab initio modeling is described. A method for calculating solution structures of macromolecules and their assemblies is provided that involves integrating data from small angle X-ray scattering (SAXS), chromatography, and atomic resolution structures together in a hybrid approach. A specific example is that of the complex of full-length nidogen-1, which assembles extracellular matrix proteins and forms an extended, curved nanostructure. One of its globular domains attaches to laminin &#947;-1, which structures the basement membrane. This provides a basis for determining accurate structures of flexible multidomain protein complexes and is enabled by synchrotron sources coupled with automation robotics and size exclusion chromatography systems. This combination allows rapid analysis in which multiple oligomeric states are separated just prior to SAXS data collection. The analysis yields information on the radius of gyration, particle dimension, molecular shape and interdomain pairing. The protocol for generating 3D models of complexes by fitting high-resolution structures of the component proteins is also given.

Here, we present how Small Angle X-Ray Scattering (SAXS) can be utilized to obtain information on low-resolution envelopes representing the macromolecular structures. When used in conjunction with high-resolution structural techniques such as X-Ray Crystallography and Nuclear Magnetic Resonance, SAXS can provide detailed insights into multidomain proteins and macromolecular complexes in-solution.

Protein-protein interactions involving proteins with multiple globular domains present technical challenges for determining how such complexes form and ...

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing approaches suffer from significant limitations, we have developed a new method for determining TF-DNA binding affinities with high sensitivity on a large scale. The assay relies on the established fluorescence anisotropy (FA) principle but introduces important technical improvements. First, we measure a full FA competitive titration curve in a single well by incorporating TF and a fluorescently labeled reference DNA in a porous agarose gel matrix. Unlabeled DNA oligomer is loaded on the top as a competitor and, through diffusion, forms a spatio-temporal gradient. The resulting FA gradient is then read out using a customized epifluorescence microscope setup. This improved setup greatly increases the sensitivity of FA signal detection, allowing both weak and strong binding to be reliably quantified, even for molecules of similar molecular weights. In this fashion, we can measure one titration curve per well of a multi-well plate, and through a fitting procedure, we can extract both the absolute dissociation constant (KD) and active protein concentration. By testing all single-point mutation variants of a given consensus binding sequence, we can survey the entire binding specificity landscape of a TF, typically on a single plate. The resulting position weight matrices (PWMs) outperform those derived from other methods in predicting in vivo TF occupancy. Here, we present a detailed guide for implementing HiP-FA on a conventional automated fluorescent microscope and the data analysis pipeline.

Here we present a novel method for determining binding affinities at equilibrium and in solution with high sensitivity on a large scale. This improves the quantitative analysis of transcription factor-DNA binding. The method is based on automated fluorescence anisotropy measurements in a controlled delivery system.

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing ...

Probing The Structure And Dynamics Of Nucleosomes Using Atomic Force Microscopy Imaging

Chromatin, which is a long chain of nucleosome subunits, is a dynamic system that allows for such critical processes as DNA replication and transcription to take place in eukaryotic cells. The dynamics of nucleosomes provides access to the DNA by replication and transcription machineries, and critically contributes to the molecular mechanisms underlying chromatin functions. Single-molecule studies such as atomic force microscopy (AFM) imaging have contributed significantly to our current understanding of the role of nucleosome structure and dynamics. The current protocol describes the steps enabling high-resolution AFM imaging techniques to study the structural and dynamic properties of nucleosomes. The protocol is illustrated by AFM data obtained for the centromere nucleosomes in which H3 histone is replaced with its counterpart centromere protein A (CENP-A). The protocol starts with the assembly of mono-nucleosomes using a continuous dilution method. The preparation of the mica substrate functionalized with aminopropyl silatrane (APS-mica) that is used for the nucleosome imaging is critical for the AFM visualization of nucleosomes described and the procedure to prepare the substrate is provided. Nucleosomes deposited on the APS-mica surface are first imaged using static AFM, which captures a snapshot of the nucleosome population. From analyses of these images, such parameters as the size of DNA wrapped around the nucleosomes can be measured and this process is also detailed. The time-lapse AFM imaging procedure in the liquid is described for the high-speed time-lapse AFM that can capture several frames of nucleosome dynamics per second. Finally, the analysis of nucleosome dynamics enabling the quantitative characterization of the dynamic processes is described and illustrated.

Here, we present a protocol to characterize nucleosome particles at the single-molecule level using static and time-lapse atomic force microscopy (AFM) imaging techniques. The surface functionalization method described allows for the capture of the structure and dynamics of nucleosomes in high-resolution at the nanoscale.

Chromatin, which is a long chain of nucleosome subunits, is a dynamic system that allows for such critical processes as DNA replication and transcription ...

Single Liposome Measurements for the Study of Proton-Pumping Membrane Enzymes Using Electrochemistry and Fluorescent Microscopy

Proton-pumping enzymes of electron transfer chains couple redox reactions to proton translocation across the membrane, creating a proton-motive force used for ATP production. The amphiphilic nature of membrane proteins requires particular attention to their handling, and reconstitution into the natural lipid environment is indispensable when studying membrane transport processes like proton translocation. Here, we detail a method that has been used for the investigation of the proton-pumping mechanism of membrane redox enzymes, taking cytochrome bo3 from Escherichia coli as an example. A combination of electrochemistry and fluorescence microscopy is used to control the redox state of the quinone pool and monitor pH changes in the lumen. Due to the spatial resolution of fluorescent microscopy, hundreds of liposomes can be measured simultaneously while the enzyme content can be scaled down to a single enzyme or transporter per liposome. The respective single enzyme analysis can reveal patterns in the enzyme functional dynamics that might be otherwise hidden by the behavior of the whole population. We include a description of a script for automated image analysis.

Here, we present a protocol to study the molecular mechanism of proton translocation across lipid membranes of single liposomes, using cytochrome bo3 as an example. Combining electrochemistry and fluorescence microscopy, pH changes in the lumen of single vesicles, containing single or multiple enzyme, can be detected and analyzed individually.

Proton-pumping enzymes of electron transfer chains couple redox reactions to proton translocation across the membrane, creating a proton-motive force used ...

Localization of SUMO-modified Proteins Using Fluorescent Sumo-trapping Proteins

Here we are presenting a novel method to study the sumoylation of proteins and their sub-cellular localization in mammalian cells and nematode oocytes. This method utilizes a recombinant modified SUMO-trapping protein fragment, kmUTAG, derived from the Ulp1 SUMO protease of the stress-tolerant budding yeast Kluyveromyces marxianus. We have adapted the properties of the kmUTAG for the purpose of studying sumoylation in a variety of model systems without the use of antibodies. For the study of SUMO, KmUTAG has several advantages when compared to antibody-based approaches. This stress-tolerant SUMO-trapping reagent is produced recombinantly, it recognizes native SUMO isoforms from many species, and unlike commercially available antibodies it shows reduced affinity for free, unconjugated SUMO. Representative results shown here include the localization of SUMO conjugates in mammalian tissue culture cells and nematode oocytes.

SUMO is an essential and highly conserved, small ubiquitin-like modifier protein. In this protocol we are describing the use of a stress-tolerant recombinant SUMO-trapping protein (kmUTAG) to visualize native, untagged SUMO conjugates and their localization in a variety of cell types.

Here we are presenting a novel method to study the sumoylation of proteins and their sub-cellular localization in mammalian cells and nematode oocytes. ...

Evaluation of Protein&#8211;Protein Interactions using an On-Membrane Digestion Technique

Numerous intracellular proteins physically interact in accordance with their intracellular and extracellular circumstances. Indeed, cellular functions largely depend on intracellular protein&#8211;protein interactions. Therefore, research regarding these interactions is indispensable to facilitating the understanding of physiologic processes. Co-precipitation of associated proteins, followed by mass spectrometry (MS) analysis, enables the identification of novel protein interactions. In this study, we have provided details of the novel technique of immunoprecipitation-liquid chromatography (LC)-MS/MS analysis combined with on-membrane digestion for the analysis of protein&#8211;protein interactions. This technique is suitable for crude immunoprecipitants and can improve the throughput of proteomic analyses. Tagged recombinant proteins were precipitated using specific antibodies; next, immunoprecipitants blotted onto polyvinylidene difluoride membrane pieces were subjected to reductive alkylation. Following trypsinization, the digested protein residues were analyzed using LC-MS/MS. Using this technique, we were able to identify several candidate associated proteins. Thus, this method is convenient and useful for the characterization of novel protein&#8211;protein interactions.

Here, we present a protocol for an on-membrane digestion technique for the preparation of samples for mass spectrometry. This technique facilitates the convenient analysis of protein&#8211;protein interactions.

Numerous intracellular proteins physically interact in accordance with their intracellular and extracellular circumstances. Indeed, cellular functions ...

Structure Solution of the Fluorescent Protein Cerulean Using MeshAndCollect

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