research papers
Journal ofApplied
Crystallography
ISSN 0021-8898

Received 2 March 2010
Accepted 9 July 2010

Nanovolume optimization of protein crystal growthusing the microcapillary protein crystallization
systemCory J. Gerdts,a,b,c Glenn L. Stahl,a Alberto Napuli,d,e Bart Staker,a,d Jan
Abendroth,a,d Thomas E. Edwards,a,d Peter Myler,d,f Wesley Van Voorhis,e Peter
Nollertb and Lance J. Stewarta,b,c,d*a

Emerald BioStructures Inc., 7869 NE Day Road West, Bainbridge Island, WA 98110, USA,
Emerald BioSystems Inc., 7869 NE Day Road West, Bainbridge Island, WA 98110, USA,c
Accelerated Technologies Center for Gene to 3D Structure, USA, dSeattle Structural Genomics
Center for Infectious Disease, USA, eUniversity of Washington, Seattle, WA 98195, USA, andfSeattle BioMed, 307 Westlake Avenue North, Suite 500, Seattle, WA 98109, USA. Correspondence
e-mail: lstewart@embios.comb

The Microcapillary Protein Crystallization System (MPCS) is a microfluidic,
plug-based crystallization technology that generates X-ray diffraction-readyprotein crystals in nanolitre volumes. In this study, 28 out of 29 (93%) proteins
crystallized by traditional vapor diffusion experiments were successfully
crystallized by chemical gradient optimization experiments using the MPCS
technology. In total, 90 out of 120 (75%) protein/precipitant combinations
leading to initial crystal hits from vapor diffusion experiments were successfully
crystallized using MPCS technology. Many of the resulting crystals produced
high-quality X-ray diffraction data, and six novel protein structures that were
derived from crystals harvested from MPCS CrystalCards are reported.

1. Introduction
New technologies to improve protein crystallization successrates are the focus of continuous research and technology
development (Fox et al., 2008; Ng, Clark et al., 2008; Ng,
Stevens & Kuhn, 2008; Li et al., 2009, 2010; Hansen et al., 2002;
Cherezov et al., 2008, 2009; Dhouib et al., 2009; Sauter et al.,2007; Hansen & Quake, 2003). Protein crystals are often so
difficult to produce that crystallographers are willing to try a
new crystallization technology, even if it might provide only asmall chance at crystallization success. However, a new technology will only be widely accepted if it is able to demonstrate
clear value to crystallographers. In this field, value to crystallographers is measured by the ease with which diffraction
quality crystals and crystal structures can be produced from alimited amount of protein supply.
The Accelerated Technologies Center for Gene to 3D
Structures (ATCG3D) has developed the Microcapillary
Protein Crystallization System (MPCS), which is a plug-based,
microfluidic protein crystallization technology capable ofquickly and easily setting up hundreds of batch-under-oil-style
crystallization experiments (Gerdts et al., 2008). The MPCS is
unique because it is capable of generating hundreds ofnanovolume (10–20 nl) experiments, each containing a slightly
different chemical composition (Gerdts et al., 2006; Zheng etal., 2003, 2005). The result is on-chip formulation of finely
controlled concentration gradients over a series of drops(plugs) that are effective at optimizing protein crystals.

1078

doi:10.1107/S0021889810027378

Further, the peel-apart CrystalCards used as a part of the
MPCS allow simple crystal extraction for diffraction studies.
Combining these benefits yields a technology that is able tocarefully optimize crystal hits, generating protein crystals thatare ready for subsequent diffraction experiments. In thisreport we have examined the ability of the MPCS technology
to perform crystal optimizations of 29 different soluble
proteins provided by the Seattle Structural Genomics Center
for Infectious Disease (SSGCID).

2. Research study workflow
SSGCID is one of two centers funded by the National Instituteof Allergy and Infectious Diseases (NIAID) and is a consortium of four Pacific Northwest institutions (Seattle BioMed,
Emerald, University of Washington and Battelle). SSGCID’s
primary mission is to determine 75–100 new protein structuresannually for targets from NIAID category A–C agents, as wellas emerging and re-emerging infectious disease organisms fora period of five years. In this study, SSGCID proteins wereused to test the ability of the MPCS technology to rapidly
optimize protein crystallization conditions using crystallization hits from traditional sitting-drop vapor-diffusion
crystallization trials. A chart describing the workflow of thestudy is shown in Fig. 1. Purified SSGCID proteins werescreened – using sitting-drop vapor diffusion – against a seriesof common crystallization screens (Wizard I, Wizard II,
Wizard III, JCSG+ and Precipitant Synergy from Emerald
J. Appl. Cryst. (2010). 43, 1078–1083

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BioSystems, and Crystal Screen HT and Index HT from
Hampton Research). Proteins that did not yield initial crystals
were retired – consistent with the workflow of the highthroughput SSGCID structure determination pipeline. If theinitial screens yielded single crystals ready for analysis by
X-ray diffraction, they were first tested for diffraction quality
before undergoing optimization using the MPCS (this wasdone to avoid optimizing crystals that were not in need of any
improvement). Proteins that led to initial microcrystals or
single crystals that did not produce high-quality X-ray
diffraction underwent optimization using the MPCS. Thus, theproteins examined in this study were those that generated
initial crystal hits but were otherwise randomly selected. In
total, 29 proteins underwent MPCS optimizations.
MPCS optimization experiments generated highly granulargradients containing up to 400 individual crystallization
experiments in 20 nl drops called plugs. Approximately 2 ml ofleftover protein and 2 ml of the precipitant solution (used in
the initial screen) were combined inside the microfluidic
circuitry of the MPCS CrystalCard (Fig. 2a). Each plug wasformed at a slightly different concentration than the plugs
before and after. This was accomplished by dynamicallycontrolling the flow rates of the solutions used to form theplugs. Computer control of flow rates generated a wide varietyof potential gradients. For simplicity, only two types ofgradients were used in this study. The two types are shown
schematically in Figs. 2(b) and 2(c). The goal of the two MPCS
optimization types was to carefully interrogate a narrow
region of crystallization phase space surrounding the initialhit. Type 1 optimizations maintained protein concentration in
all of the plugs while varying the precipitant concentration.
Type 2 optimizations varied the protein and precipitantconcentration against one another in order to interrogate theeffect of varied ratios of protein and precipitant. Completed
optimization experiments were incubated in the CrystalCard
at 100% humidity to allow for crystal growth. Crystals in plugs
stored in CrystalCards at 100% humidity have been shown to
be stable for more than six months. Additionally – although
not pursued in this study – plugs can be intentionally dehydrated while in the CrystalCard to initiate crystal growth bycontrolling the humidity of the storage container. After crystals grew, they were harvested for analysis by X-ray diffraction
by peeling back the thin plastic bonding layer (Fig. 2d) andharvesting the protein crystal directly from the microcapillary(Fig. 2e).

3. Materials and methods

Figure 1
A flow chart describing the sequence of events undertaken in this study.
Purified protein was received and initial screening via sitting-drop vapordiffusion experiments was set up. If initial crystals were single andharvestable, they were analyzed via X-ray diffraction. If the initial protein
crystals produced high-quality X-ray diffraction data, the structure wassolved without MPCS optimization. However, if the initial X-ray
diffraction data were poor, or if the initial crystals were small or not
harvestable, the crystals were optimized using the MPCS.
J. Appl. Cryst. (2010). 43, 1078–1083

Plastic CrystalCards were manufactured from cyclic olefin
copolymer. Each CrystalCard has two separate microcapillaries with approximately 10 ml of useful volume. One
optimization experiment may be performed in each microcapillary. Plug formation in the CrystalCard requires a lowsurface-energy (hydrophobic) surface. This ensures that thecarrier fluid (FC-40) preferentially wets the walls of the
microcapillary. To prepare the microcapillary surface for plugformation, Cytonix PFC 502AFA solution is used to coat theinside of the microcapillary. To apply the coating, the CrystalCard is filled from the outlet with Cytonix 502AFA solution
and incubated under ambient conditions for 0.5–1 h. The
502AFA solution is removed from the CrystalCard via
vacuum, followed by curing at 333–343 K for 1 h.
The CrystalCard has four inlet ports for introducing liquids,
one each for the carrier fluid (1), protein (2), precipitant (3)and buffer (4). The buffer, protein and precipitant inlet
channels merge at the 3 + 1 mixer, where the aqueous solutions are combined and segmented into individual plugs by theinert and immiscible carrier fluid. Syringes and Teflon tubingsare back-filled with the carrier fluid, and the desired amountsof the aqueous solutions are aspirated into the ends of the
Teflon tubings. Connection to the CrystalCard is achieved via
the Teflon tubing and a polypropylene connector that forms anairtight seal to the port in the CrystalCard. The component
liquids of the experiment are placed in the Teflon tubing of thesyringe pumping system and delivered to the CrystalCard in amanner described previously using the MicroPlugger pumpcontrol software (Gerdts et al., 2008). The positioning of thefluid lines on the CrystalCard is noted in Fig. 2.
The flow rates of the aqueous solutions can be varied suchthat a smooth gradient over a series of plugs is generated. In

Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth1079

research papers
Table 1
Flow rate scheme (ml min1) used for the Type 1 and Type 2 MPCS
gradients in this study.
ProteinPrecipitant

BufferCarrier fluid

Type 1

Starting flow rate
Ending flow rate

2
2

2
0–1

0
1–2

5
5

Type 2Starting flow rate
Ending flow rate

2
0

0
2

0.2
0.2

5
5

this study, we primarily used two types of gradients (Table 1
and Fig. 3). In gradient Type 1, the protein is delivered at aconstant rate (2 ml min1) while a linear gradient is made fromprecipitant and buffer solutions (with the sum of the precipitant and buffer flow rates remaining constant at 2 ml min1).
For most Type 1 gradients, the flow rate of the precipitant was
programmed to start at 2 ml min1 and slowly decrease as theflow rate of the buffer slowly increased at the same rate.

Therefore in all Type 1 experiments, the concentration of theprotein remains constant as the concentration of the precipitant varies (Table 1). In gradient Type 2, a dynamic gradient
between the protein and precipitant is generated. In Type 2
gradients, the flow rate of the protein is slowly decreased as
the flow rate of the precipitant is slowly increased and thebuffer flow rate is held constant (Table 1). In general, a Type 1
gradient was generated for a protein/precipitant combination
first and if, no crystals were seen, a Type 2 gradient was often
generated as a follow-up.
Precipitants used for this study were available commerciallyfrom either Emerald Biosystems (Wizard I, Wizard II, Wizard
III, JCSG+ and Precipitant Synergy) or Hampton Research(Crystal Screen HT and Index HT). Customized versions ofWizard I and Wizard II (also commercially available from
Emerald BioSystems on request) were also used. The customized screen consisted of Wizard I and Wizard II with primary
precipitant concentration increased by 50–100%.
Crystals were extracted directlyfrom the CrystalCards for subsequent
analysis by X-ray diffraction (Fig. 2d).
The 100 mm-thick plastic bonding layer
was pealed off in order to expose thedesired crystals to be harvested. Typically, ca 1 ml of a previously preparedcryo-protectant solution was pipetted
directly onto the desired crystal. The
crystal was then pulled out of the
microcapillary using a traditional nylon
cryo-loop (ca 0.2 mm diameter from
Hampton Research) and stored inliquid nitrogen for transport to an
X-ray source for analysis. On rareoccasions, a crystal was found toremain on the 100 mm bonding layer. In
this scenario, the crystal was still
covered with the cryo-protectant solution and harvested from atop theplastic layer.

4. Results

Figure 2(a) Ca 2 ml of leftover protein solution and ca 2 ml of precipitant solution from the initial experiment(left) were used to generate an optimization experiment in the MPCS CrystalCard (right). In the
CrystalCard, aqueous solutions (protein, precipitant and buffer) were combined and spontaneously
segmented into individual drops (plugs) by the inert, immiscible carrier fluid. The resulting plugs
filled the microcapillary and were incubated as individual crystallization experiments. Scale bar =400 mm. (b), (c) Generic protein crystallization phase diagrams indicating how crystallization phasespace is interrogated in MPCS optimizations. In Type 1 MPCS optimizations (b) protein
concentration is held constant while a gradient of precipitant concentration is generated over aseries of plugs. In Type 2 MPCS optimizations (c), protein concentration begins high and slowly
decreases as precipitant concentration begins low and slowly increases to generate a dynamic protein
versus precipitant gradient over a series of plugs. (d) A picture of an MPCS CrystalCard being peeledapart in order to expose the crystals. Scale bar = 1 inch ’ 2.54 cm. (e) A picture of a protein crystal
being harvested from a CrystalCard using a 0.2 mm cryo-loop. Scale bar = 200 mm.

1080

Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth

MPCS gradients were shown to optimize vapor diffusion crystal hits with ahigh rate of success. Of the 29 proteinsthat underwent MPCS optimizations,
28 (93%) were crystallized using the
MPCS. Many of the proteins used inthis study produced crystals in morethan one precipitant solution during
the initial screening. In total, 120different protein/precipitant combinations produced crystals in the vapor
diffusion experiments. Of the 120
combinations, 90 (75%) produced
crystals during the MPCS optimization
experiments – a high success rate given
J. Appl. Cryst. (2010). 43, 1078–1083

research papers
that many of the initial crystals were not single crystals but tiny
microcrystals or precipitation that looked crystalline (seeexamples in Fig. 1). In addition, MPCS experiments underwent a significant (50–100-fold) decrease in experimental
volume and were translated from sitting-drop vapor-diffusionstyle crystallization to batch-under-oil-style crystallization.
Further, ten precipitant solutions that did not generate crystals
in vapor diffusion experiments were used to generate crystals
in MPCS optimization experiments. These particular precipitant solutions were tested using the MPCS (despite notyielding crystals from vapor diffusion experiments) because
they possessed similarities in chemical composition to otherprecipitant solutions that did yield crystals in vapor diffusion
experiments. In total, 17 precipitant solutions were tested inthis manner and ten yielded crystals (59%). This indicates thatusing the MPCS to perform optimizations of every precipitantin the standard crystallization screens may generate many
unique crystal hits that are being missed with a single vapordiffusion experiment – consistent with previously reported

data comparing vapor-diffusion-style crystallization withbatch-under-oil-style crystallization (Baldock et al., 1996;
D’Arcy et al., 2003). More than 90% of the precipitant solutions that were tested using vapor-diffusion experiments went
untested in the MPCS, indicating a strong potential for
discovering new crystal hits using the MPCS. A wide variety ofsolutions were represented in the precipitants used in thisstudy, including various salt solutions, low- (4.5) to high-pH(10.5) solutions, high-viscosity polyethylene glycol solutions,and organics such as 2-propanol and 2-methyl-2,4-pentanediol.

5. Discussion

The goal of the MPCS optimizations was to salvage protein
structures from initially screened proteins by (i) generating
single crystals when sitting-drop experiments did not and/or(ii) improving the diffraction quality of initial single crystals
generated in the sitting-drop experiments. Of the 29 protein
targets involved in the study, six novel
protein structures have been determined using crystals from MPCS optimizations and deposited in the PDB
for a successful salvage rate of 21%(Figs. 3a–3f; for crystal optimization
data, see Table 2). This salvage ratecompares favorably to published datafrom reductive methylation (Kim et al.,2008) and limited proteolysis (Dong etal., 2007). In two cases, high-quality
diffraction was also generated fromcrystals grown from subsequent sittingdrop vapor-diffusion experiments (inone case, X-ray diffraction from the
MPCS crystal was of slightly higher
resolution and in one case diffraction
from the MPCS crystal was of slightly
lower resolution). The development of
the MPCS by ATCG3D has continued
throughout this study, leading to thecommercialization of the MCPS Plug
Maker (Fig. 3g). As shown in this study,
the MPCS technology has been a
successful method of optimizing
protein crystals in order to yield highquality X-ray diffraction results. Future
Figure 3
directions for this technology are
Pictures of crystals in plugs generated from MPCS optimizations that led to high-quality data sets
emerging and include incorporation of(2.5 Å or better) and/or novel structures. Corresponding ribbon structures are included below thelipidic cubic phase into plugs for
pictures of the plugs (for data collection and refinement statistics, see Table 2). All scale bars =200 mm. (a) Enoyl-CoA hydratase from Mycobacterium tuberculosis (1.8 Å; PDB code 3h81); (b)membrane protein crystallization (Li et
aldehyde dehydrogenase from Bartonella henselae (2.1 Å; PDB code 3i44; deposited structure foral., 2009) and high-throughput initial
PDB code 3i44 came from a sitting-drop optimization at 2.0 Å resolution; the 2.1 Å-resolution data
screening of protein samples with theset was generated from a crystal optimized using the MPCS); (c) methionine-R-sulfoxide reductase
hybrid method (Li et al., 2006) (sparsefrom Burkholderia pseudomallei (1.7 Å; PDB code 3cxk); (d) methylisocitrate lyase from Brucellamelitensis (2.9 Å; PDB code 3eoo); (e) dihydrofolate reductase/thymidylate synthase from Babesia
matrix + gradient screening) madebovis (2.5 Å; PDB code 3i3r); ( f ) tRNA guanine-n1-methyltransferase from Bartonella henselae
possible in an automated fashion by(2.5 Å; PDB code 3ief); (g) A picture of the commercial version of the MPCS Plug Maker. Left: The
the availability of the MPCS Plug
touch screen user interface and live image of the CrystalCard. Right: Instrument stage that holds theCrystalCard and crystallization samples.
Maker.
J. Appl. Cryst. (2010). 43, 1078–1083

Cory J. Gerdts et al.  Nanovolume optimization of protein crystal growth1081

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Table 2Crystal optimization data.

Protein

OrganismInternal
proteincode

Precipitants
Precipitants yielding
tested using crystals
from MPCS
MPCS

Potential
crystal hits
from initial
screening

Nucleoside diphosphate kinase
Giardia lamblia
Gila 438
Adenylate kinase
Giardia lamblia
Gila 297
Deoxynucleoside kinase
Giardia lamblia
Gila 1017
Arsenical pump-driving ATPase
Giardia lamblia
Gila 988
Peptide methionine sulfoxide
Giardia lamblia
Gila 536
reductase msrB
Rab GDI
Giardia lamblia
Gila 634
Uracil phosphoribosyltransferase Giardia lamblia
Gila 1401
Enoyl-CoA hydratase
Mycobacterium tuberculosis Mytu 386
Enoyl-CoA hydratase
Mycobacterium tuberculosis Mytu 358
Aldehyde dehydrogenase
Bartonella henselae
Bahe 886
Dihydrofolate reductase/
Babesia bovis
Babo 1191
thymidylate synthase
Thymidylate synthase 1/2 TS-1
Encephalitozoon cuniculi
Encu 1191
Bifunctional dihydrofolate
Toxoplasma gondii
Togo 1191
reductase-thymidylate synthase
Thymidylate synthase
Burkholderia pseudomallei Bups 1181
UDP-N-acetylmuramate-l-alanine Burkholderia pseudomallei Bups 137
ligaseRNA polymerase, chain,
Brucella melitensis
Brab 66
bacterial and organelletRNA (guanine-n1)-methyltrans- Bartonella henselae
Bahe 1015
feraseEndonuclease/exonuclease/phos- Giardia lamblia
Gila 1102
phataseAcetylglutamate kinase
Bartonella henselae
Bahe 993
Ribokinase
Giardia lamblia
Gila 1141
Probable thiosulfate sulfurtrans- Mycobacterium tuberculosis Mytu 1241
feraseGlycine cleavage system protein H Mycobacterium tuberculosis Mytu 1046
Aldose reductase
Giardia lamblia
Gila 1452
Methionine-R-sulfoxide reductase Burkholderia pseudomallei Bups 33
Ribose-phosphate pyrophospho- Burkholderia pseudomallei Bups 35
kinase-Aminolevulinic acid dehydratase Burkholderia pseudomallei Bups 75
Recombinase A
Burkholderia pseudomallei Bups 69
Glutaryl-CoA dehydrogenase
Burkholderia pseudomallei Bups 27
Methylisocitrate lyase
Burkholderia pseudomallei Bups 14

Best resolution fromvapor diffusion crystals

Best resolution from
MPCS
Crystalcrystals (Å) structure?

23
10
4
6
1

23
10
4
6
1

16
8
3
3
1

–
–
–
–
–

6
8
4
–
–

No
No
No
No
No

2
2
3
76
79
18

2
2
3
8
7
10

2
0
1
6
7
6

–
–
3.5
1.8
2.0
–

–
–
6
2.05
2.0
2.5

No
No
No
Yes
Yes
Yes

2
3

2
3

2
2

3.1
–

None
3.8

No
No

3
5

3
5

1
4

–
6–7

–
6.8

No
No

3

3

1

–

none

No

8

8

8

3

2.4

Yes

2

2

1

None

None

No

8
2
2

4
2
1

4
2
1

5
3.5
2.1

4.5
2.9
2.6

No
No
Yes†

1
2
2
3

1
1
2
3

1
1
2
3

1.75
2.7
–
2.3

–
3.6
1.7
–

Yes†
No
Yes
Yes†

1
1
1
1

1
1
1
1

1
1
1
1

–
–
2.2
–

–
–
–
2.9

No
No
Yes†
Yes

† The structure was solved after the completion of this study through subsequent salvage efforts.

This work was supported by the PSI-2 Specialized Center
Grant U54 GM074961, co-sponsored by NIGMS–NCRR for
the Accelerated Technologies Center for Gene to 3D Structure. All protein targets were provided by the NIAIDsupported Seattle Structural Genomics Center for Infectious
Disease (contract No. HHSN266200700057C). We thank Craig
Ogata at the Advanced Photon Source (APS) for assistanceand for helpful discussions. Part of this work is based upon
research conducted at the Northeastern Collaborative Access
Team beamlines of the APS, supported by award RR-15301
from the National Center for Research Resources at the
National Institute of Health. Use of the APS is supported by
the US Department of Energy, Office of Basic Energy
Sciences, under contract No. W-31-109-ENG-38. Some results
shown in this report are derived from work performed at
Argonne National Laboratory, Structural Biology Center, at
the APS. Argonne is operated by the University of Chicago
Argonne, LLC, for the US Department of Energy, Office of

1082

Biological and Environmental Research, under contract No.
DE-AC02-06CH11357.

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