Efficient protein crystallization

Lawrence J. DeLucas,a,* Terry L. Bray,a Lisa Nagy,a Debbie McCombs,a

Nikolai Chernov,b David Hamrick,c Larry Cosenza,c Alexander Belgovskiy,d Brad Stoops,d

and Arnon Chaitd

a Center for Biophysical Sciences and Engineering, University of Alabama at Birmingham, Birmingham, AL 35294-4400, USAb Natural Sciences and Mathematics, University of Alabama at Birmingham, Birmingham, AL 35294-4400, USA

c Diversified Scientific, Inc., Birmingham, AL, USAd ANALIZA, Inc., Bay Village, OH, USA

Received 5 February 2003

Abstract

High-throughput molecular biology and crystallography advances have placed an increasing demand on crystallization, the one

remaining bottleneck in macromolecular crystallography. This paper describes three experimental approaches, an incomplete fac-

torial crystallization screen, a high-throughput nanoliter crystallization system, and the use of a neural net to predict crystallization

conditions via a small sample (�0.1%) of screening results. The use of these technologies has the potential to reduce time and samplerequirements. Initial experimental results indicate that the incomplete factorial design detects initial crystallization conditions not

previously discovered using commercial screens. This may be due to the ability of the incomplete factorial screen to sample a

broader portion of ‘‘crystallization space,’’ using a multidimensional set of components, concentrations, and physical conditions.

The incomplete factorial screen is complemented by a neural network program used to model crystallization. This capability is used

to help predict new crystallization conditions. An automated, nanoliter crystallization system, with a throughput of up to 400

conditions/h in 40-nl droplets (total volume), accommodates microbatch or traditional ‘‘sitting-drop’’ vapor diffusion experiments.

The goal of this research is to develop a fully-automated high-throughput crystallization system that integrates incomplete factorial

screen and neural net capabilities.

� 2003 Elsevier Science (USA). All rights reserved.

Keywords: High-throughput nanocrystallization; Incomplete factorial screen; Neural-net

1. Introduction

The promise of high-throughput structural genomics

(HTX) as an enabling technology for rapid drug dis-

covery is becoming a reality. Advances in experimentaland computational technologies have made it possible

for pharmaceutical and drug discovery companies to

apply HTX to the multitude of new targets available

from high-throughput genomics efforts. Successful HTX

application will require flexible systems that allow for

efficient development and usage of experimental data for

iterative optimization.

A number of techniques have been employed for de-

termining protein structures, including X-ray crystal-

lography, nuclear magnetic resonance spectroscopy, and

mass spectrometry. Of these, X-ray crystallography re-

mains the only method routinely used to determinestructures of large biomolecules (i.e., MW in excess of

20 000Da). The elucidation of complete genome se-

quences for a large number of vertebrate and inverte-

brate species (Roses, 2002) has accelerated international

efforts to develop high-throughput methods/technologies

that enable rapid three-dimensional protein structure

determination (Kuhn et al., 2002; Lamzin and Perrakis,

2000). The National Institutes of Health (NIH) estab-lished a structural genomics program with the goal of

‘‘encouraging research on the development of method-

ology and technology underpinning the emerging field of

Journal of Structural Biology 142 (2003) 188–206

www.elsevier.com/locate/yjsbi

Journal of

StructuralBiology

* Corresponding author. Fax: +205-934-2659.

E-mail address: delucas@cbse.uab.edu (L.J. DeLucas).

1047-8477/03/$ - see front matter � 2003 Elsevier Science (USA). All rights reserved.

doi:10.1016/S1047-8477(03)00050-9

structural genomics, whose goal is the understanding ofprotein structural families, structural folds, and the re-

lation of structure and function.’’ One such program

(P50-GM62407), the ‘‘Southeastern Collaboratory for

Structural Genomics’’ (SECSG), involves researchers

from a consortium of universities, including the Uni-

versity of Georgia (UGA; P.I. B.C. Wang), Georgia

State University, the University of Alabama at Bir-

mingham (UAB), the University of Alabama in Hunts-ville, and Duke University. The complete genomes from

Caenorhabditis elegans and Pyrococcus furiosis, plus se-

lected genes from the human genome, were chosen for

this collaborative program. At UAB, the Center for

Biophysical Sciences and Engineering (CBSE) has been

involved in fundamental studies of macromolecular

crystal growth since 1985. The CBSE previously devel-

oped an automated crystallization system that providesreal-time control of pre- and postnucleation vapor

equilibration kinetics, in an effort to enhance crystal size

and diffraction resolution (Bray et al., 1997, 1998; Col-

lingsworth et al., 2000). More recently, the CBSE and

two collaborating companies, ANALIZA, Inc., and Di-

versified Scientific, Inc., have developed novel technol-

ogies that have the potential to increase the efficiency of

protein crystal screening and optimization. Screening forpotential crystallization conditions often requires large

quantities (25–100mg) of purified protein. Unfortu-

nately, even with the recent advances in cloning and

protein expression, many proteins can be produced in

only submilligram or low-milligram quantities without

enormous expenditures of resources to produce larger

quantities. The recent efforts in genomics, and subse-

quently proteomics, have produced thousands of newproteins for study in structural biology and drug design

projects. The number of new proteins available will

continue to increase significantly in the next several years

as additional investigators become involved in this im-

portant research. As a result, there is a significant need

for more efficient and effective methods for determining

protein structures. Since X-ray crystallography will likely

remain the primary technique for determining structuresof large proteins for the foreseeable future, the limita-

tions associated with this method must be overcome.

There are two primary approaches to solving the limi-

tations of X-ray crystallography: (1) develop more ef-

fective methods for large-scale protein production and

(2) reduce the scale and number of individual screening

experiments so that less protein is needed to perform

screening experiments.A holistic HTX approach is presented, combining

with a thorough understanding of the physical phe-

nomena underlying important problem areas and novel,

automated technology platforms that are modular, effi-

cient, and flexible.

A critical component of X-ray crystallography is ob-

taining well-ordered crystals of the target protein. This

effort traditionally requires screening thousands of so-lutions with varying chemical compositions. In an effort

to minimize the total amount of protein required to

discover suitable crystallization conditions, the CBSE,

ANALIZA, Inc., and Diversified Scientific, Inc., have

explored three different complementary approaches: an

incomplete factorial screen with response surface opti-

mization, a high-throughput nanoliter crystallization

robot, and a neural net software program capable ofusing initial screening results to predict future conditions

that are likely to yield crystals. The incomplete factorial

screen with response surface optimization allows a small

number of experiments to be performed that, sampling

the extremely large experiment space in a statistically

robust manner. This approach allows for efficient de-

termination of solution conditions suitable for crystal-

lizing proteins by performing experiments that take intoaccount the independent and interdependent influences

of each experimental parameter. Hits obtained in the

initial screening process are used to design an optimiza-

tion screen to further improve the initial crystallization

results. To enable a comprehensive search in a large

parameter space for optimal crystallization conditions

using submilligram protein quantities, a modular line of

high-throughput crystallization and inspection work-stations has been developed. The process begins with a

statistically based screen optimization that directs the

production of specialized libraries of crystallization

conditions. The libraries and the proteins are subse-

quently combined using a novel nanoliter-range crystal-

lization screening system (NanoScreenTM). An

automated intelligent high-resolution inspection system

(CrystalScoreTM) is then deployed to periodically exam-ine and classify the optimal starting conditions for the

subsequent scale-up crystallization experiments. This

system is currently being upgraded to include a neural

net crystallization prediction program.

The high-throughput nanoliter crystallization ro-

bot significantly reduces the scale of each experi-

ment, allowing the use of as little as 0.1 lg of protein

per screening experiment condition. The use of neuralnetwork software programs facilitates prediction of

probable crystallization conditions based on results

from a small number of experiments. This can fur-

ther improve the efficiency of protein crystallization

screening experiments by learning from prior experi-

mental results and predicting new conditions that

should produce crystals. The three technologies work

in tandem to facilitate highly efficient and effectivescreening of protein crystallization conditions. The

ultimate goal is to develop an automated system that

combines all three approaches, yielding a more effi-

cient and successful method for macromolecular

crystallization. The following provides our strategy

and preliminary investigations for each of these ap-

proaches.

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 189

2. Incomplete factorial screen

The crystallization of biological molecules is a pro-

cess in which large, complex molecules in solution as-

sociate with one another to form a regular lattice in the

solid phase. This process is accomplished by changing

the physical and chemical environment such that the

formation of this lattice is more favorable thermody-

namically than remaining in the solution state. Thethermodynamics can be rationalized in terms of several

factors.

The Gibb�s free energy, DG, is given by the equation

DG ¼ DH � TDS:

The energy released by crystallization is the difference

between the changes in enthalpic and entropic energy.

The energetic cost of order (negative entropy) increases

with temperature.

The macromolecule itself has a favorable (positive)

entropy in solution due to its translational and rotationaldisorder. It is surrounded by an ordered shell (or shells) of

water molecules that have negative entropy. The charged

macromolecular surface forms hydrogen bonds with the

water with favorable (negative) enthalpy. In the crystal-

line form, water is released as a portion of those hydrogen

bonds are sacrificed in favor of stronger intramolecular

hydrogen and ionic bonds. The order of the lattice de-

creases the entropy of the molecules. The exclusion of thewater from the lattice releases thewatermolecules into the

bulk solution, however, raising their entropy. In solution,

hydrophobic patches on the surface of themacromolecule

are also surrounded by (unfavorably) ordered water

molecules, but lack the favorable hydrogen bonds. In the

crystalline form, these hydrophobic patches are often

buried by intermolecular interactions. These energetic

conditions are summarized in Table 1.The conditions chosen to crystallize a macromole-

cule exploit and control these energetic differences. The

pH, for example, determines the charge on the mole-

cule, directly influencing the energetics of its interactionwith the bulk solvent. The addition of counterions

shields surface charges and changes the chemical po-

tential of the solvent. Polymeric alcohols sequester

water away from the macromolecule and may interact

with it as well. Certain other components, such as di-

valent cations and metals, may interact directly with

the macromolecules and moderate lattice contacts.

Although some macromolecules are crystallized solelyby temperature change, in most cases the buffers that

stabilize the solution and crystalline states have differ-

ent compositions. This is often accomplished either by

evaporation or by dialysis. The rate of change can be

controlled by the physical form of the experiment, such

as vapor diffusion, dialysis, or controlled evaporation.

A phase diagram representing a vapor diffusion ex-

periment is shown in Fig. 1. The zigzag line indicatesthe transition from soluble to crystalline macromole-

cule as the macromolecule and precipitant concentra-

tions increase.

Changes in the concentration of the macromolecule

due to water evaporation increase the tendency for

nucleation and crystal growth. Crystal growth itself

Table 1

Summary of molecular energetics that affect crystallization processes

In solution Crystal

Favorable Unfavorable Favorable Unfavorable

Macromolecule

entropy

Freedom of translation

and rotation

Packing order

Macromolecule

enthalpy

Hydrogen bonds to

water, ionic bonds to

counter ions

Ionic and hydrogen

bonds to lattice

neighbors

Loss of hydrogen bonds

to water shell

Solution entropy Ordered surface

water—especially

around hydrophobic

areas

Water released from shell

into bulk solvent

Solution enthalpy Hydrogen bonds to

macromolecule

Fig. 1. Crystallization phase diagram.

190 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

removes the macromolecule from the solution phase,decreasing its concentration.

A preliminary crystallization screen searches broadly

though ‘‘crystallization space,’’ a multidimensional set

of possible components, concentrations, and physical

conditions. The goal is good outcomes (i.e., leads to

diffraction-quality crystals). The experiment number

and type for the initial screen are chosen to reflect a

reasonable balance between thoroughness and cost anda reasonable expectation of where the desired results

may be found. For example, excessive trials will return

many repeated results from similar experiments, wast-

ing material, time, and manpower. An insufficient

number of experiments may miss a region that would

have ultimately led to crystals. It is also important to

avoid time- and sample-consuming general trials in

areas of chemical space that would rarely, if ever,produce crystals. These areas might be accessed later

by widening the search if initial trials fail to produce

leads.

There are many strategies available to search for

crystallization conditions. Commercial screens use

sparse matrix methods, in which the experiments are

clustered around conditions that have already given

crystals in the past. The advantage of this approach isthat when a protein is crystallized under one set of

conditions, it will often exhibit hits under other con-

ditions as well. The disadvantage is that some areas

of crystallization space are neglected. Random screens

(such as Crystool) sample these areas, but in both

cases it is difficult to glean information from the

collected results because the screens are not balanced.

With the assistance of Professor Charles Carter(University of North Carolina at Chapel Hill), CBSE

scientists have been developing a set of conditions to

construct an efficient screen. A general model is used

that incorporates factors whose levels are mathemati-

cally balanced. Each possible level of a factor is sam-

pled an equal number of times. In our 360-experiment

screen, each of the six anionic precipitants is sampled

60 times. Binary combinations are also balanced.Third-order and higher combinations are distributed as

randomly as possible. A statistical computer program,

INFAC (C.W. Carter, private communication), is used

to construct the balanced matrices that encode the ex-

periments. A spreadsheet program translates the ma-

trices into chemical recipes for our solution-handling

robots to construct. This balanced design facilitates the

determination of which factor levels are most suitablefor crystallization. For example, comparison of the

average score of all the experiments that contain chlo-

ride as an anionic precipitant to the overall average

(and the other anions) allows one to determine whether

chloride is the best choice for the anion. Some binary

combinations will show obvious synergy, such as the

combination of pH and anion choice. Anions show

varying effectiveness as precipitating agents as the netcharge on the protein changes. This is related to the

Hofmeister effect (Hofmeister, 1888). The temperature

can have a significant effect on the solubility of the

macromolecule, also affecting the solution properties

for other components. Several buffers have a tempera-

ture dependence on their pKa, a characteristic that may

be exploited. The pH affects the net charge on the

molecule and the charge state of the surface aminoacids. Above the protein isoelectric point (pI), the net

charge on the protein is negative and more of the basic

groups are neutral, rather than protonated. At the pI,

the net charge is zero and the protein is less soluble.

Below the pI, the net charge on the protein is positive

and more of the acidic groups are neutral.

The phase diagram (Fig. 1) reflects a general solu-

bility (Ksp) model, in which the nucleation point dependson both the concentrations of macromolecule and pre-

cipitant. At higher concentrations of macromolecule,

less precipitant is required for nucleation. At lower

concentrations of macromolecule, the reverse is true.

Conditions with higher concentrations of protein have

more protein available to form crystals, but may cause

overly rapid crystal growth rates, leading to flaws and

twinning. In a screen, the precipitant concentration isvaried relative to the macromolecule concentration. This

avoids areas of crystallization space that would never

give crystals.

Both organic precipitants, such as polymeric alco-

hols (polyethylene glycols), and ionic salts are useful

as precipitants. Many ionic salts are used as precip-

itants and have particular charges, polarizabilities, si-

zes, and solution activities. Many have significantbuffering capabilities as well. Using a combination of

organic and inorganic precipitants can balance their

various properties. Glycerol can stabilize the macro-

molecule in solution by specific interaction with the

surface and it can shift the nucleation point of the

macromolecule independent of other factors. Divalent

cations stabilize specific interactions between macro-

molecule monomers, often increasing the order of acrystal. Many proteins bind specific divalent metals,

and often the metals have an anomalous signal that

can be used for phasing. Additives such as detergents

and arginine can affect the macromolecule conforma-

tion, strengthen specific contacts, or reduce nonspecific

contacts.

2.1. Methods

Once a screen design has been devised and con-

structed, the experiments are conducted and the out-

comes are scored in a qualitative fashion. We use a

scale that includes scores for various crystalline, quasi-

crystalline, and noncrystalline results. From the out-

comes of the screen, a central starting point is defined

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 191

for a fine search of the physical and chemical param-eters. This optimization step is important because at the

optimum, small unavoidable statistical variations in

the experimental conditions have negligible effects on

the outcome. The experiment is more robust and re-

producible at the optimum than at the extremes. In the

case in which a trial has given crystalline material, it

may serve as the center point for a multidimensional

search. A starting point may also be inferred by astatistical treatment (least-squares and/or nonlinear

methods) of the variation of noncrystalline results. In

our optimization model, four or five numerical vari-

ables (protein concentration, pH, total amount of pre-

cipitant, relative amounts of organic and ionic

precipitant, and if used, concentration of glycerol or

polyol) are varied simultaneously to explore the vicinity

of the hit for improved results. The particular param-eters are defined to minimize their synergistic effects.

For example, the concentration of precipitant necessary

to crystallize a protein at 10mg/ml would be insufficient

to crystallize that same protein at 5mg/ml and would

precipitate the protein at 20mg/ml. For this reason, as

the concentration of the protein is varied in the opti-

mization, the relative rather than the absolute concen-

tration of precipitant is varied.A grid search at three levels in five dimensions

would entail 243 experiments. To reduce that number

to a manageable level without sacrificing too much

data, we have used the Design of Experiments–Re-

sponse Surface method (Carter, 1997). Many industrial

processes are optimized using this method. We use

previously published matrices (Carter, 1977) to define

the variation about the center point. These matrices arearrays of numbers from )1 to +1, with repetitions at

the center point. The desired spread of variation for

each dimension is multiplied by the matrix value and

added to the center point for that variable. The out-

comes of the experiments are scored using a size

measurement, such as the ratio of width to length. The

results of the experiments are then fed into the statis-

tical analysis package JMP (SAS Institute, Cary, NC,USA), which has built-in methods to construct and

analyze a model from response surface data. The

program fits the data to a multidimensional quadratic

model and either predicts an optimum within the

bounds of the experiments or suggests a direction in

which to locate a new center point. Full statistical

calculations are presented to allow the researcher to

assess the goodness of fit of the model. An iterativeapproach is then used to refine about the optimum,

leading to higher quality crystals.

A matrix to encode the variables for the screen was

calculated using the program INFAC. The screen size

was chosen to be 360 experiments with 10 variables at

six or three levels each. The most balanced matrix was

chosen from the 10 000 numerical seeds tested. Screen

variables were chosen according to previously publishedwork (Carter and Carter, 1979).

The matrix was translated into a set of recipes using a

spreadsheet program. The stock components were

mixed using the RecipeMaker robot, a custom-config-

ured Hamilton ML 4000 liquid-handling robot capable

of mixing up to 48 different stocks and water. Recipes

were prepared in 2-ml block plates and reformatted into

standard 384-well plates. For comparison, the proteinswere also subjected to a group of commercial screens

consisting of Hampton Crystal Screens I and II,

MembFac, Natrix, and Emerald Wizards I and II (290

experiments). Comparison of crystallization success

rates for incomplete and sparse matrix screens suggests

that the incomplete factorial method may be more ef-

fective. A set of five proteins (two from C. elegans and

three from P. furiosis), 2G1.1, 9c9, UGA 214, UGA 220,and UGA 222, was subjected to crystallization screening

using both incomplete factorial and sparse matrix

models. For this limited sample size, the incomplete

factorial method of screening identified a greater num-

ber of crystallization conditions for each protein. Each

of the five protein samples was crystallized using the

incomplete factorial method, whereas only two proteins,

2G1.1 and UGA 214, crystallized using the sparse ma-trix set. Interestingly, the incomplete factorial method

identified conditions that appear disparate compared to

the results from the sparse matrix screens.

2.2. Screening results

Table 2 summarizes the screening results for five

proteins (two from C. elegans and three from P. furiosis)from the SECSG program for the incomplete factorial

screen vs commercial screens.

2.3. Optimization

Preliminary optimization efforts have focused on two

proteins, the VEE capsid protein and Group B Strep-

tococcus hyaluronate lyase.In the search for diffraction quality crystals of the

VEE capsid protein, one hit in the screen, 12% PEG

8000, 10% glycerol and 150 mM Ammonium Citrate,

Table 2

Comparison of results obtained using incomplete factorial screen

versus available commercial screens

Protein Incomplete factorial

screen hits

Commercial

screen hits

2G1.1 3 5

9C9 1 0

UGA 214 6 1

UGA 220 7 0

UGA 222 2 0

192 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

pH 7.2, gave very small crystals that appeared to betetragonal. The normal form, which is orthorhombic,

tended to grow as needles or prisms. A response surface

optimization experiment was constructed such that the

pH, protein concentration, glycerol concentration, and

precipitant concentrations were varied simultaneously.

Thirty optimization experiments were performed using

the response surface methodology to search for im-

proved conditions for VEE capsid crystallization. Theresults can be inferred from the width and length mea-

surements of the crystals obtained. Most conditions

gave small to large blocky tetragonal crystals, while

other conditions produced needles or a mixture of the

two. Representative photos are shown in Fig. 2.

The scores were analyzed by both width (smallest

dimension) and aspect ratio in the statistical program

JMP. Fig. 3 is a plot of the predicted against actualwidths.

2.3.1. Summary of fit

The summary of fit for Fig. 3 is as follows: RSquare,

0.821104; RSquare Adj, 0.694825; Root mean square

error, 0.088871; Mean of response, 0.1521; Observations

(or sum Wts), 30.

When the model was predicted based on the widthscore alone, the surface represented not a maximum, but

a single dimension minimum (Fig. 4). This indicates that

the area probed by the experiments is on the boundary

between two maxima. The results confirm this, as two

forms are present separately and mixed under other

conditions. In order to maximize the tendency toward

the blocky crystal form, a model was constructed based

on aspect ratio (Figs. 5 and 6). Since the majority of the

trials gave the blocky form, the statistical model is not asrobust. However, the predictions indicate that the form

is sensitive to both the protein concentration and the

precipitant ratios. In particular, the blocky form is

preferred at higher concentrations of ammonium citrate,

and the needles form at higher concentrations of PEG

8000.

2.3.2. Summary of fit

The summary of fit for Fig. 5 is as follows: RSquare,

0.73941; RSquare Adj, 0.664956; Root mean square

error, 0.235667; Mean of response, 0.75978; Observa-

tions (or sum Wts), 28.

A set of experiments was prepared at the predicted

maximum. All trials gave large single blocky crystals.

2.3.3. Crystallization of full-length Group B Streptococ-

cus hyaluronate lyase (GBShl)

Preliminary crystals of GBShl were obtained using

vapor diffusion methods. The reservoir precipitant was

Fig. 2. Crystals from the VEE capsid response surface experiment.

Fig. 3. Actual vs. predicted width of crystals obtained using incomplete

factorial screen.

Fig. 4. Prediction profiles based on crystal width.

Fig. 5. Predicted vs. actual aspect ratio.

Fig. 6. Prediction profiles based on crystal aspect ratio.

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 193

chosen based on the crystallization conditions reportedfor the 92-kDa fragment of S. agalactiae hyaluronate

lyase and included polyethylene glycol monomethyl

ether 5000 (PEG-MME 5000), potassium thiocyanate,

and 100mM Hepes buffer, pH 7. The protein solution

contained 20mM Hepes, pH 7, and 10mM calcium

chloride. In a typical experiment, 2 ll of protein so-

lution was mixed with 2 ll of the reservoir solution.

The initial crystals were poorly formed, as shown inFig. 7.

As with the previous example, response surface

methods were used to refine the crystallization condi-

tions. The protein concentration, reservoir pH, thiocy-

anate, and PEG-MME 5000 concentrations were varied

simultaneously.

A variety of outcomes, ranging from clear drops to

precipitate to poor crystals and single crystals, was ob-tained. A model of the interactions of these factors was

constructed using the statistics package JMP (SAS In-

stitute). The statistics are rough approximations because

many of the drops that might have produced crystals

failed to nucleate (Fig. 8).

2.3.4. Summary of fit

The summary of fit for Fig. 8 is as follows: RSquare,0.684151; RSquare Adj, 0.514078; Root mean square

error, 2.359997; Mean of response, 3.190476; Observa-

tions (or sum Wts), 21.

The new predicted center point (Fig. 9) had a

protein concentration of 24mg/ml, and the reservoir

buffer was pH 7.6, 19% PEG-MME 5000, and 56mM

KSCN. Larger single crystals were obtained by seed-

ing. Crystals were crushed in the mother liquor, and1 ll of this solution was added to the 1-ml reservoirs

in the vapor diffusion setup. The reservoir was mixed

as normal with the protein solution. The seeded

crystallization conditions were refined using response

surface methods to give consistent large single crystals,

0:2� 0:3� 0:5mm, suitable for diffraction experi-

ments. A tray of 24 replicates produced crystals that

diffracted to 2�AA (Fig. 10).

2.3.5. Problems with optimization

One of the problems inherent with this or any op-

timization experiment is the batch-to-batch variability

of protein samples. Although the protein concentra-

tion and buffer composition can be adjusted to the

desired conditions, variations in handling the sample,

particularly during the concentration step, may give

rise to differing aggregation and oxidation states.

Another problem is the stochastic nature of crystal

nucleation (this can be controlled to some extent via

laser light-scattering measurements). Nucleation oftenfails to occur for some experiments, even though exact

replicates yield large single crystals. Additionally, there

are inherent errors involved with scale-up of experi-

ments from nanoliter to microliter volumes. The

equilibration kinetics for microbatch or vapor diffu-

sion is affected by the protein drop volume and sur-

face area. Although these differences can be modeled,

this added variable complicates scale-up and finaloptimization. Despite these difficulties, initial incom-

plete factorial screen and optimization results are

encouraging.Fig. 7. Initial crystals of GBShl.

Fig. 8. Actual vs. predicted score.

Fig. 9. Prediction profiles for GBShl crystal score.

Fig. 10. Representative optimized crystals of GBShl.

194 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

3. High-throughput nanoliter protein crystallization

CBSE scientists recognized several years ago that the

demand for a significant quantity of protein for crystal

screening would inhibit the ability of researchers to de-

termine structures for a significant percentage of the

targeted proteins. In 1997, the CBSE began developing

concepts for reducing the scale of protein crystallization

experiments. New technologies that would support au-tomated, reduced-scale crystallization screening were

also developed. Indeed, several companies, academic

laboratories, and government laboratories are now ac-

tively pursuing reduced-scale crystallization, both with

internally developed technologies and with commer-

cially available systems (Syrrx, Inc., 10410 Science

Center Drive, San Diego, CA 92121, USA; DeTitta

et al., 2001; Luft et al., 2001; Mueller et al., 2001; San-tarsiero et al., 2002; Segelke et al., 2002). This approach

has proven to be an enabling methodology for high-

throughput crystallization screening and structure de-

termination.

3.1. Fundamental vs practical considerations

Macromolecular crystallization is characterized byextremely low and highly anisotropic molecular at-

tachment kinetics. Large molecules require a longer

time (compared with small molecules) to assemble into

a highly ordered crystalline lattice. Critical variables

include the solvent structure, the presence of various

crystallization agents that modify solvent structure,

and the rate at which protein molecules are trans-

ported to the crystalline surface. In large-scale crystalgrowth it is possible to handle highly viscous solutions

and mix and prepare screening solutions containing

minor ingredients in very low proportions. Reduced-

scale crystallization places constraints on these three

requirements.

Our approach to reduced-scale crystallization ex-

plicitly separates preparation of screening solutions

from the screening experiments. Libraries of crystalli-zation solutions are prepared according to previously

described statistical design methodologies, using con-

ventional liquid handlers in large volumes (ca. 1ml).

Rather viscous bulk ingredients can be dispensed at

proper proportions and completely mixed in advance of

the actual screening experiment. Different screening li-

braries specific for each class of proteins are prepared

and stored in sealed 96-deep-well plates for further re-formatting and subsequent use. The actual experiment is

reduced to aspiration/dispension of the screening and

protein solutions for the specific crystallization tech-

nique chosen.

There are three fundamental approaches to deliver-

ing submicroliter volumes. The simplest approach is the

spotting method, whereby a pin with an extremely fine

point is dipped into a solution and removed. A smallamount of solution clings to the tip of the pin and can

be transferred to a microarray or slide by touching the

pin to the target surface. This method is easy to im-

plement but it is difficult to control the accuracy and

precision of the delivered volume. A second approach

involves using chambers of defined dimensions to

control the volumes delivered. A common application

of this method is in microfluidic devices, which utilizefabrication techniques from the computer chip industry

to create channels and chambers in silicon that can

contain nanoliter volumes. However, controlling fluid

movement in this format has proved difficult primarily

due to limitations of valve fabrication. Only recently

has effective valve fabrication technology been achieved

in soft polymeric material devices (Fluidigm Corp.,

7100 Shoreline Court, South San Francisco, CA 94080,USA). A third method for delivering small volumes

involves dispensing technologies. At the outset, the

CBSE and ANALIZA determined that the use of active

dispensing technologies was most feasible for success-

fully performing high-throughput nanoliter crystalliza-

tion. Fundamental technologies capable of dispensing

nanoliter volumes, and adaptable for our application,

were available in the ink-jet printing industry. Otherapproaches were judged to have limitations not easily

overcome in a reasonable time frame. Although the

active dispensing approach was judged to be the most

feasible, there remained several hurdles. One of the

most challenging problems encountered was the deliv-

ery of nanoliter volumes of solutions with widely

varying physicochemical properties. This problem is

solved by the choice of active dispensing method andby tailoring the dispense rate to the solution viscosity.

These two approaches resulted in a system capable of

dispensing solutions exhibiting a wide range of physi-

cochemical properties with high precision and accuracy.

Other practical problems included dispensing multiple

nanoliter volumes simultaneously with similar precision

and accuracy, eliminating cross-contamination between

solutions, and controlling unintended water loss duringexperiment preparation. Dispensing of multiple-nano-

liter solutions was accomplished using a hybrid mi-

crofluidic valve (Innovadyne Technologies, Inc., 2835

Duke Court, P.O. Box 7329, Santa Rosa, CA 95407-

7329, USA) that controlled the fluid flow at each dis-

pensing tip. Cross-contamination was eliminated by use

of custom wash stations that rinsed the dispensing tips

thoroughly before sets of new solution conditions wereaspirated. Water loss during experiment preparation

was virtually eliminated via an automated oil (micro-

batch) dispense system or by using a humidity chamber

(vapor diffusion) to retard evaporation.

Dispensing of viscous screening solutions in very

small volumes (e.g., low nanoliter quantities) is a

vexing problem that rapidly reduces the choice of

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 195

dispensing technologies. Techniques relying on surfacewave excitation and instability, such as piezo capillar-

ies, are inherently limited since they typically rely on

small variations in surface tension and viscosity to

achieve quantitative accuracy. Screening solutions vary

in all of their physicochemical parameters thus re-

quiring a robust dispensing technique to achieve re-

producible and quantitative screens. Fast solenoid

(a.k.a. drop-on-demand) techniques were chosen be-cause they rely on fluid inertia for dispensing and

typically result in accurate results across a large oper-

ating range.

Low-volume screening experiments can be executed

in multiple ways. Our initial work concentrated on mi-

crobatch (under-oil) crystallization (Chayen, 1997), in

which the screening and protein droplet is quickly cov-

ered with a mixture of water-impermeable and water-permeable oils, offering the desired kinetic profile (e.g., 1

or 2 weeks for complete drying). Crystallization under

oil is particularly suitable for low-volume screening in

which crystal recovery is not required. Also, rapid

evaporation of prepared droplets is a serious issue when

using small volumes and typical techniques such as base-

plate cooling and humidity control are not optimal since

every droplet (screen) has different colligative properties.For example, base-plate cooling below the dew point of

water can result in droplets continuing to dry while

others, on the same multiwell plate, actually experience

hydration. Rapid sealing of individual wells requires

sophisticated automation, a level of complexity that was

omitted from initial prototypes. Other crystallization

techniques such as sitting drops are easily accommo-

dated, and new miniaturized crystallization plates fa-cilitate such experiments. It should be noted that a thin

layer of silicon oil can be used to slow the rapid initial

evaporation, thereby allowing an entire plate to be

prepared before covering the individual experiment

chambers.

As discussed previously, a preferred method for

conducting reduced-scale crystallization screening is

through the use of technologies, similar to those em-ployed in ink-jet printing devices, which allow small

volumes to be dispensed with high accuracy and pre-

cision. The CBSE partnered with ANALIZA, Inc. (a

Cleveland-based biotechnology company) to develop

NanoScreen, an automated crystallization system that

can dispense 20 nl of solution with �10% accuracy.

This system can prepare 3000 crystallization experi-

ments with less than 350 lg of protein. A wide rangeof solution viscosities can be accommodated while

maintaining accurate dispensing. Twenty-nanoliter

droplets consisting of up to 15% PEG 8000 can be

rapidly dispensed with less than 10% error. Higher

PEG concentrations are achieved by dispensing four-

fold diluted PEG solutions (making the solutions more

water-like with similar chemical contents) or by de-

creasing the dispense rate. The microbatch method canbe adjusted so that water is continually drawn from

the diluted crystallization droplet until the desired

percentage of PEG is obtained. The dilution effect can

be accounted for in our statistical experiment design

strategy.

The NanoScreen system (Fig. 11) is comprised of the

several key components. A microfluidic dispensing head

is used to handle the protein and crystallization (recipe)solutions. The first-generation system has 12 tips for

fluid handling, with 2 tips dedicated to dispensing pro-

tein solutions and the remaining 10 used to deliver the

recipe solutions. Motorized stages allow for accurate x-,

y-, and z-axis movement of the head to the various lo-

cations on the deck of the NanoScreen system. The

system can perform both microbatch and traditional

vapor diffusion (sitting drop) experiments. For micro-batch experiments, an oil-dispensing subsystem is used

to cover the experiment solutions after they are de-

ployed. Wash stations for protein and recipe prevent

contamination between solutions and a quality control

capability is used to calibrate drops and to monitor the

dispense mechanism. Custom software controls all as-

pects of the NanoScreen operation, including solution

aspiration, solution dispensing, tip washing, oil dis-pensing, stage movement, quality control operations,

and drop-dispensing calibration. The system accom-

modates experiments in conventional 384-well or 1536-

well plates, as well as the new Corning 192-experiment

vapor diffusion plate.

The NanoScreen system operates by aspirating 20 llof protein solution into two protein tips and 20 ll of 10different recipe solutions into the 10 recipe tips. Beforethe first experiments are prepared, drops are dispensed

from each tip onto the quality control area to ensure

that the tips are dispensing properly. The dispensing

head then moves to the first set of experiment wells and

dispenses the protein solution. The 10 recipe tips are

then automatically positioned over the protein solutions

in the experiment wells and recipe solutions are simul-

Fig. 11. NanoScreen crystallization system.

196 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

taneously dispensed. For microbatch experiments, an oilmixture is immediately dispensed over the experiment

solutions to prevent unintended water loss. For vapor

diffusion experiments, the experiments are prepared

within a constant-humidity chamber to minimize unin-

tended evaporation, but coverage with permeable oil

also offers an option to arrest the initial rapid evapo-

ration while not interfering with the slow approach to

supersaturation. With each set of 10 experiments pre-pared, the recipe tips are washed, followed by aspiration

of the next set of 10 different recipe solutions for sub-

sequent experiments. This process is repeated until all

experiments on a tray have been prepared. The experi-

ment tray is then manually sealed with Crystal Clear

Sealing Tape (MANCO HP260) and placed in a con-

stant-temperature incubator. The NanoScreen system

has a throughput of 400 experiments/h. While approxi-mately 40 ll of protein solution is needed as a total

working volume, very little of this initial solution is

actually consumed. Most of the aspirated protein solu-

tion is returned, after preparation of all experiments is

completed, where it can be recovered and used for future

experiments. It should be noted that the first-generation

NanoScreen system is a prototype, with improvementsalready under development to increase throughput by a

factor of 10.

3.2. Experimental results

Our first experiments in reduced volume crystalliza-

tion involved manually dispensing nanoliter volumes of

commercially available proteins (thaumatin and lyso-zyme) and crystallizing solutions into a custom micro-

array using a micro syringe. Fig. 12 shows one of the

early custom microarrays used and Fig. 13 represents

selected images of thaumatin crystals grown in the mi-

croarray.

The initial total volume (protein + crystallizing solu-

tion) was approximately 50 nl. After verifying that

protein crystals could be grown and visualized innanoliter droplets, scientists at ANALIZA, Inc., devel-

oped and tested a number of technologies to support the

construction of the NanoScreen system.

Initial testing of the prototype system involved the

crystallization of eight commercial proteins. The pro-

teins were crystallized in 60-nl total initial volume mi-

crobatch experiments using known crystallization

conditions. Fig. 14 shows results from three of theseproteins, lysozyme, catalase, and pepsin. Additionally,

using the incomplete factorial screen, previously unre-

ported new crystallization conditions were identified for

several of the commercial proteins.

After completing the initial testing with commercial

proteins, the system was used to crystallize new proteins

obtained from internal projects and from collaborators

at other institutions. More recently, we have begunscreening crystallization conditions for proteins from

our NIH Structural Genomics project. We have

screened more than 170 proteins in nanoliter drops by

microbatch or vapor diffusion experiments, with greater

than 25% producing a crystalline phase separation.

Initial hits are currently being optimized for subsequent

scale-up to produce larger crystals required for X-ray

diffraction analysis.Microbatch crystallization has been used for the

majority of our nanoliter crystallization experiments to

date. Initial total drop volumes for microbatch experi-

ments are typically 60–80 nl. The protein and recipe

solutions are deployed as described earlier into 384-well

Fig. 12. Nanocrystallization microarray.

Fig. 13. Thaumatin crystals grown in 20-nl droplets.

Fig. 14. Nanoliter crystallization results for lysozyme, catalase, and pepsin.

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 197

plates, covered with oil, sealed with tape, and imageddaily for up to 2 weeks. Since experiments are performed

at different temperatures as part of our statistical ex-

periment design approach, the nanoliter drops lose wa-

ter through the oil and tray at different rates. Water

dissipation is slower at lower temperatures, thus an ex-

periment at 22 �C is imaged for 5–7 days, while an

experiment at 4 �C is imaged daily for up to 2 weeks. A

10-point scale based upon the Hampton Researchcrystallization scale is used to score results. Hits are

defined as a crystalline phase separation, with higher

scores assigned to results producing three-dimensional

single crystals. Fig. 15 shows microbatch nanocrystalli-

zation results for NIH Structural Genomics proteins

2G1, 4G49A, and W539A.

The NanoScreen system was recently modified by

addition of precise humidity control, thereby accom-modating vapor diffusion crystallization experiments in

the Corning 192 experiment chamber protein crystalli-

zation plate. The Corning plate is based on a 384-well

plate format, with 192 separate experiment chambers

where a protein solution can be equilibrated against a

reservoir solution. The reservoir solutions are pre-

loaded to match the chemical contents of crystallizing

solutions that are deployed into the protein solution foreach experiment. Experiments are prepared as de-

scribed earlier, with a total initial volume of 120 nl.

Larger volumes are used to allow for slight evaporation

that may occur prior to sealing the plate. This does not

increase the consumption of protein significantly rela-

tive to the working volume. The prepared tray is sealed

with clear tape and imaged through the bottom of each

crystallization well. Since the protein solutions areequilibrated against a reservoir, the drops do not lose

water to dryness and can be imaged less frequentlywithout missing hits. Thus far, crystallization experi-

ments for 18 proteins from the NIH Structural Ge-

nomics project have been performed using the vapor

diffusion method, with crystals observed for 10 of these

proteins. Fig. 16 shows sitting-drop vapor diffusion

nanocrystallization results for NIH Structural Genom-

ics proteins 2G1, 11D4A, and UGA 214.

3.3. Experiment imaging

Images are acquired and stored using the Crystal-

Score imaging system developed by Diversified Scien-

tific, Inc. The CrystalScore system allows for

automated image acquisition of each experiment, ar-

chiving of sequential images, data storage, and auto-

matic determination of crystal location, size, andnumber for any experiment. Other CrystalScore capa-

bilities include filtering and sorting relational databases

for ‘‘hits’’ and growth trends; storage of 2M database

records on local workstations; remote database con-

nectivity for Oracle 9I, IBM DB2, and Microsoft SQL

servers; database report generation to HTML, Word,

and Excel; time-lapsed image acquisition; AVI Focus-

Through Movies; and tray support for Linbro 24,Corning 24 and 96, 48-well, 96-well, 192-well, 384-well,

Greiner 288-well, Cryschem 24, VDX 24, and Nunc 72-

well plates.

3.4. Future efforts

The initial approach was to investigate numerous

experimental designs for automated low-volume crys-tallization. It is advantageous to scale-up a well-char-

Fig. 15. Microbatch nanoliter crystallization results for NIH Structural Genomics proteins 2G1, 4G49A, and W539A.

Fig. 16. Sitting drop vapor diffusion nanoliter crystallization results for NIH Structural Genomics proteins 2G1, 11D4A, and UGA214.

198 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

acterized process to construct a high-speed system thatsolves only problems unique to low-volume handling.

Future efforts in efficient screening of protein crystalli-

zation will focus on several areas. The NanoScreen

system will be used to routinely prepare experiments

supporting the NIH Structural Genomics project. Ad-

ditionally, several improvements to the NanoScreen

system will be implemented. These include faster

throughput (10-fold) using NanoScreen, and by the in-corporation of additional tips and dispensing heads,

optimizing movements to improve efficiency, and im-

proving the effectiveness of our statistical screens. The

implementation of other embodiments of our original

reduced-volume screening concepts to further improve

the efficiency and effectiveness of screening protein

crystallization conditions is being pursued. These in-

clude customizing tip dispense rates to accommodatemore viscous PEG solutions and more flexible control

software that automatically calculates and prepares re-

sponse surface optimization experiments (being devel-

oped by Diversified Scientific, Inc.). To improve our

imaging throughput, we are constructing an automated

imaging platform consisting of a robotic arm, three

custom CrystalScore systems, and incubators housing

the experiment trays. This imaging platform will be in-tegrated with a bar-code reader, plate imaging sched-

uler, central server for housing image databases, and

client computers for accessing experiment databases and

evaluating crystallization results.

4. Predicting protein crystallization conditions using

neural net technology

Neural net technology developed from artificial in-

telligence research was applied to protein crystallization

screening and resulted in the ability to accurately pre-

dict/recognize conditions that favored crystallization.

The following preliminary research (developed in col-

laboration with scientist and engineers from Diversified

Scientific, Inc., and the University of Alabama at Bir-mingham) deals with an optimization technique that

may increase the success rate for producing diffraction-

quality macromolecular crystals. This technology

demonstrates the most promise for optimizing protein

crystallization if combined with a thorough sampling of

crystallization space. An initial screen based upon

sampling techniques, such as the incomplete factorial

(Carter and Carter, 1979) described previously, is usedfor the protein crystallization trials. Every crystalliza-

tion trial outcome, including failures, is used to train a

neural network. Once trained, the neural network may

recognize conditions that yield crystals. Neural net-

works are based upon a real nervous system paradigm

composed of multiple neurons communicating through

axon connections. Characteristics of neural networks

include self organization, nonlinear processing, andmassive parallelism. The neural network exhibits en-

hanced approximation, noise immunity, and classifica-

tion properties. The self-organizing and predictive

nature of the neural networks allow for accurate pre-

diction of never before seen crystallization conditions,

even in the presence of noise. Our predictive neural

networks are trained via back propagation using the

incomplete factorial screen. If properly trained, theneural network can be used to identify or recognize

important patterns of crystallization. An input pattern

comprised of the incomplete factorial screen is pre-

sented to the network. The outputs are compared to the

known scores. Additional neurons are added and in-

terconnect weights (basis functions) are adjusted to

minimize the error. This process is continued until the

average error across all the training sets is minimized.Eventually, if the correct variables and sample size are

chosen to adequately represent the crystallization na-

ture of the protein, a stable set of hidden neurons and

basis function weights evolve. This neural network can

then be used to predict non-sampled complete factorial

conditions to be used for optimization, i.e. predicting

the conditions that produce crystals from the entire

‘‘crystallization space’’ of possible experimental condi-tions based upon the results from a much smaller

number of actual experiments performed. This ap-

proach has a higher probability of producing accurate

predictions if the small test set is statistically represen-

tative of the crystallization space.

A result of the structural proteomics initiative has

been the automation and miniaturization of protein

crystal growth experiments accompanied by a tremen-dous explosion in the generation of protein crystalli-

zation data. Protein crystallization data are complex.

This complexity is due to the nature of the data in-

corporated, which includes text, images, and quanti-

tative data related to the specific solution conditions

and scoring of results. An added layer of informatics

can also be wrapped around the core information that

is anticipated to link protein amino acid sequence andbiophysical parameters to crystallization results (Juri-

sica et al., 2001; Hennessy et al., 2000). Crystallization

results are the interpretation of the outcome of indi-

vidual protein crystallization experiments into usually

one of four main categories: clear drop, precipitate,

phase change, crystal. A variety of methods have been

adopted for evaluating the results of protein crystalli-

zation experiments. Historically, it is only those ex-periments that yield crystals that are used to generate a

second round of protein crystal growth experiments

for optimization (Gilliland, 1988). Optimization of

protein crystal growth is performed using multivariate

designs such as central composite, Box–Behnken, and

factorial (full and incomplete). These designs system-

atically evaluate several variables around a central

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 199

point or within a range (Box and Behnken, 1960; Boxand Hunter, 1957; Box et al., 1978; Carter, 1997;

Carter and Carter, 1979; Shaw Stewart and Baldock,

1999).

A comprehensive model for protein crystallization

does not exist. The optimization methods identify user-

defined variables in a systematic procedure to deter-

mine those variables most important for the particular

protein sample to be crystallized. There usually arefour variables that are common to all protein crys-

tallization experiments: protein concentration, pre-

cipitant concentration, pH, and temperature. Often

there are additional variables contributing to the suc-

cess or failure of crystallization experiments. If possi-

ble it is wise to select variables that have linearly

independent effects on protein crystal growth (Box

et al., 1978). In order to model the protein crystalli-zation data the results are scored. Hence an investi-

gator�s experience can play an important role in the

crystallization of a protein. If the experience of a

crystallographer could be captured into a model, much

of the subjectivity in protein crystallography could be

eliminated.

The recipes used for crystallization were developed by

the CBSE using 10 variables and 47 components. Thedesign for this screen is similar to that displayed in Table

3. Using this system there are (3� 3� 5� 5� 6� 3�3� 3� 3� 3¼ ) 328 050 combinations of components

that can be created. Depending upon the complexity of

the custom screens developed, the possibility of intro-

ducing human error and the amount of time needed to

generate the screens could be areas of potential prob-

lems. These challenges were considered an opportunityfor the development of technology that could facilitate

the high-throughput structural genomics programs and

industrial research.

In an effort to develop a tool for modeling protein

crystallization the screening results from a single pro-

tein were subjected to three types of analyses. The

‘‘best’’ analysis was then applied to protein samples 9c9

and Delta 8-10B. The incomplete factorial recipes de-veloped by the CBSE similar to that shown in Table 3

were used to screen all three protein samples for crys-

tallization. The actual experiments were implemented

using the NanoScreen NanoScreen. The robot prepared

360 microbatch crystallization experiments for each

protein sample. The crystallization experiments them-

selves were performed in a total volume of 80 nl. The

results from these experiments were visualized, ac-quired, and manually scored using CrystalScore. The

experimental preparation developed by the incomplete

factorial design was encoded in a 360� 10 matrix. The

score for each experiment was appended forming a new

column in the matrix. This matrix encoded the experi-

mental preparation and results formed the raw data for

analysis. Lactoglobulin gave a frequency of forming

crystals of 3.1–5.8% depending on the screen used. The

three types of crystallization analyses used for lacto-

globulin were (1) multiple-step regression analysis, (2)

neural net analysis, and (3) Chernov analysis. The

Table 3

Variables and treatments used for incomplete factorial screen

Variable/treatment Index

Variable 1: Temperature

4 �C 1

15 �C 2

20 �C 3

Variable 2: Protein dilution

4.382 1

3.912 2

3.442 3

Variable 3: Anionic precipitate

Chloride 1

Citrate 2

Acetate 3

Sulfate 4

Malonate 5

Thiocyanate 6

Variable 4: Organic precipitate

MPD 1

PEG 400 2

PEG 1450 3

PEG 4000 4

PEG 5000 MME 5

PEG 8000 6

Variable 5: Buffer pH

5.5 1

6 2

6.5 3

7 4

7.5 5

8 6

Variable 6: Precipitation strength

3 1

5.47 2

9.97 3

Variable 7: Organic moment

0.05 1

0.4 2

0.75 3

Variable 8: Percentage glycerol

0 1

5 2

10 3

Variable 9: Additive

None 1

Arginine 2

BOG 3

Variable 10: Divalent ion

None 1

Mg2þ 2

Ca2þ 3

200 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

multiple-step regression analysis is an extension of thepublished work of Drs. Carter and Carter, Jr., in which

linear, quadratic, and cross products were used to build

a model based on the screening results. The neural net

analysis was performed using a proprietary neural net

analysis program developed at Diversified Scientific,

Inc. The Chernov analysis was performed in collabo-

ration with the University of Alabama at Birming-

ham Department of Mathematics by Dr. NikolaiChernov. The performances of crystallization analyses

were compared using two metrics, the R2 value and the

ability to correctly identify/predict crystallization out-

come in experimental trials withheld from the training

data set. The best performing crystallization analysis

was then applied to protein 9c9 and Delta 8-10B

screens.

Multiple-step regression analysis of the lactoglobu-lin crystallization screening was inadequate at gener-

ating a model for crystallization. The regression

analysis was performed by Mrs. Shuying Yu, a SAS

programmer employed at the Jefferson County De-

partment of Health in Alabama. The major finding

was a significant lack of fit (R2 ¼ 0:54). This could be

interpreted as requiring further design or additional/

different variables. The regression model responsesurface has a saddle point suggesting that no optimum

response exist. Furthermore, the cross product of the

variables used in designing the screen had a significant

effect on score. The ability of this type of analysis to

model the data for lactoglobulin is still in the inves-

tigative stage. Preliminary research using the Chernov

algorithm and neural network suggested that these

methods produced better R2 values and therefore,more accurately predict the crystallization of Lacto-

globulin.

Preliminary research using a neural network accu-rately predicted the crystallization of lactoglobulin. A

partial sampling of the incomplete factorial design ex-

periment was used to train a neural net to recognize

conditions that result in crystallization with R2 values

between 0.63 and 0.75. The neural network was trained

with 87.5% (315 experiments) of the incomplete factorial

screen results (Fig. 17). A representative sampling of the

training set is shown in Table 4. Table 4 translates theincomplete factorial design in Table 3 to index values that

represent the physical components of the crystallization

recipe. Experiments 316 to 360 were withheld during

training and subsequently used for validation. The 315

experiments allowed the neural network to converge with

an R2 value of 0.754, an acceptable value, but still not

optimal. The input to the neural network is the indexed

variables and the output is the predicted score. Theweights of the hidden neurons are determined by back-

propagation. The remaining 12.5% (45 experiments) of

the incomplete factorial screen results were used for ver-

ification. Fig. 18 displays a comparison between the pre-

dicted and actual scores for lactoglobulin in experiments

316–360. The neural network successfully predicted the

two crystal outcomes (experiments 322 and 340) in the

validation set. The score or result from a protein crys-tallization experiment (y axis) versus the crystallization

experiment (x axis) is displayed. The results from a ‘‘real’’

experiment and those from the predicted experiment us-

ing a neural network are displayed. Fig. 19 illustrates the

physical outcomes of the two predicted crystallization

conditions (experiments 322 and 340). The trained net-

work was able to predict every crystallization outcome in

the 12.5% test set even though the data had never beeninput into the network. This result in the face of the lowR2

value highlighted the neural network�s ability to recognize

Fig. 17. Neural network training data for lactoglobulin. Experiments 1–315 were used to train the neural network.

Table 4

Representative training data for lactoglobulin neural network (experiments 1–315)

Experiment V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 Score

1 1 3 4 1 2 3 4 5 2 3 1

2 1 3 1 2 3 3 2 2 2 2 1

3 1 3 1 1 1 2 1 1 3 3 3

4 1 3 3 1 3 2 5 4 1 3 1

5 1 3 5 3 2 3 3 2 2 2 1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .315 3 1 4 3 1 1 5 3 2 2 1

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 201

crystallization conditions for lactoglobulin. A neuralnetworkwas created using a partial (315/360 experiments)

incomplete factorial design for lactoglobulin. This ap-

proach may be used as an optimization tool to identify

crystal-yielding experiments not physically tested in the

incomplete factorial design. This is a relatively dramatic

example of how well the trained neural net functions as a

predictor of crystallization conditions. Graphs of the

crystallization results require specific ordinate scales toaccommodate different weighting schemes required for

training optimization.

The Chernov analysis was developed by Dr. Nikolai

Chernov, at the University of Alabama at Birmingham.

The algorithm is structured similar to a regression

model, but instead of coefficients, the algorithm uses

mathematical basis functions that can be linear or

nonlinear, discrete or continuous. The mathematicalbasis functions take into account higher order interac-

tions between the various components. The mathemat-

ical functions are automatically varied in an organized

procedure until the R2 value, or coefficient of determi-

nation (this is essentially a measure of the difference

between observed and computed values), is within an

acceptable range, usually greater than 0.95. Once

trained, the Chernov algorithm mathematically de-scribes the various states of crystallization (output) in

terms of real, physical variables (inputs). The Chernov

crystallization cost function would look like

F1V1 þ F2V2 þ F3V3 þ � � � þ FnVn ¼ Output;

where Fn are the mathematical basis functions that

can be linear or nonlinear, discrete or continuous; Vn

are the input variables, and Output is the predictedscore.

The Chernov algorithm converged on the lacto-

globulin training set with a R2 value of 0.93. The al-

gorithm was used in a predictive manner to create

optimization conditions for lactoglobulin protein crys-

tallization. Our current screening protocol tests 360 of

328 050 possible permutations for protein crystalliza-

tion. The highest 20 predictions of the 328 050 per-mutations were identified and are in the process of

being evaluated. It is anticipated that these analyses

may improve initial screening results. The Chernov

analysis was performed only on lactoglobulin screen

results. The analysis can be used to construct a model

that will recognize combinations of reagents that will

most likely give a selected score. The simplicity of

training neural networks and the apparent accuracy ofpredicting crystallization conditions in such preliminary

work have very exciting implications for optimization.

Because of the ease in training a neural net on

screening results this analysis was applied to protein

9c9 (unknown C. elegans protein), a previously un-

crystallized sample and Delta 8–10B, a membrane as-

sociated molecule. For protein 9c9, experiments 1–315

were used to train the neural network. There was onlyone crystal-producing condition in the training set

(experiment 239) (Fig. 20). The neural network con-

verged with an R2 value of 0.604. The scoring system

used was binary with noncrystal scored as 0 and crystal

scored as 2000. Experiments 316–360 were withheld

from the training set and used to verify the neural

network. In the validation set (experiments 316–

360), the neural network accurately predicted the onlycrystal-yielding experiment number 350, Fig. 21. The

neural network was able to predict the only crystal-

yielding experimental conditions in experiments 316–

360 for the previously uncrystallized protein 9c9, even

though it was not trained on these conditions. This

analysis further reinforces the hypothesis that the

neural network can be used to predict crystallization

Fig. 19. Predicted crystallization experiments 322 and 340. The neural network successfully predicted the 2 crystal outcomes for the lactoglobulin

verification data set.

Fig. 18. Lactoglobulin actual vs. predicted crystallization.

202 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

conditions for previously uncrystallized proteins. The

9c9 training data and results are summarized in Figs.

20–22.

Neural networks may be useful for crystallization

optimization of membrane proteins. An incompletefactorial screen with 11 input variables was designed for

protein Delta 8-10B. This protein is considered a pe-

ripheral membrane protein that has undergone numer-

ous crystallization attempts. It is a therapeutically

important protein whose structural data would be of

great interest to the scientific community. The incom-

plete factorial comprised 288 samples. The incomplete

factorial screen yielded the following outcomes: 226 clear

drops, 3 phase separations, 39 precipitates, 10 micro-

crystals/precipitates, 8 rosettes/spherulites, 2 needles, 0plates, 0 small 3D crystals, and 0 large 3D crystals. By

combining the incomplete factorial variables with the

outcome for each experiment, a set of nonlinear equa-

tions with 11 independent variables and 1 dependent

variable is created. These nonlinear equations become

the basis for training the neural network to recognize

experimental conditions useful for crystallization. A

neural network, trained on 90% of an incomplete facto-rial screen for Delta 8-10B, was able to predict the only

crystallization condition in the remaining 10% of the

incomplete factorial screen. The 90% training set con-

tained only 1 needle condition while the test set con-

tained only 1 needle condition. The neural network was

able to predict the crystallization condition of the needle

in the test set even though the 2 needles crystallized with

very disparate conditions (Table 5).In order to validate the integrity of the training for

the third protein, Delta 8-10B, only 90% of the test set

(259 experiments) was used for training. The remain-

ing 10% (29 experiments) was withheld from training

and used for verification. If the analysis can perform

Fig. 22. Crystallization outcome for protein 9c9 (experiment 350). The

neural network for protein 9c9 successfully predicted the only crystal

yielding outcome in the test set.

Fig. 23. Experiments 1–259 were used to train the neural network for Delta 8–10B.

Fig. 24. Experiments 260–288 were used to verify the neural network

for Delta 8–10B.

Fig. 21. Comparison of crystallization scores between predicted and

actual experiments using input data the neural net has NEVER seen

for protein 9c9. Experiments 316–360 were used to verify the neural

network that was trained only on experiments 1–315.

Fig. 20. Neural network training data for protein 9c9. Experiments 1–315 were used to train the neural network.

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 203

well on a small test set it may recognize those con-ditions that yield crystals from the complete crystal-

lization space not previously tested (�35 million

experiments in this example). The experiments were

randomized to remove the sequential sampling due to

the three temperatures. Since there were only 2 needle

conditions that resulted from the 288-condition screen,

1 needle condition (100mM Bicine, pH 8.3, 0.567M

sodium acetate, 0.9% PEG 400, 0.01M CaCl2, 0.05%BOG, protein dilution encoded as 2.3 at 14 �C) was

placed into the training set (Fig. 23) while the other

needle condition, experiment number 282 (100mM

acetate, pH 4.5, 0.648M sodium chloride, 11.6% PEG

M5000, 0.01M CaCl2, protein dilution encoded as 1.5

at 22 �C) was placed into the verification set (Fig. 24).

The neural network�s highest predicted score in the

test set was the needle condition, even though theneural network had never been exposed to the crys-

tallization condition in the test set. Visual inspection

of the crystallization conditions for both crystal results

appear disparate (Table 5). Because the neural net-

work is trained with all the results, including failures,

traditional optimization techniques that use only the

hits probably would have not predicted the crystalli-

zation conditions for the test set. The network wasable to predict the crystallization condition for the

only crystal in the test set (experiment 282) even

though it had never been trained on the experiment.

There were two false positives (experiments 263 and

275) although the relative highest predicted score

was for the correctly predicted crystal condition.

We anticipate simulating the 35 000 000 permutations

not tested, using the highest 20 predicted scores forcrystallization optimization/verification experiments.

The relative importance of each variable is shown in

Fig. 25.

The ability of the neural network to identify patterns

of crystallization in complex nonlinear data sets may

provide a powerful method of optimization. The total

number of permutations is calculated by multiplying the

number of discrete values of each design variable. In thefirst two experiments (lactoglobulin and the C. elegans

protein 9c9), there are 320 050 possible permutations in

the incomplete factorial space. In the second experi-

ment, there are approximately 35 million possible per-

mutations because two of the variables, organic

percentage and salt concentration, each have more than

20 possible discrete values.

We anticipate using the trained neural network on theremaining untested experiments for each protein

(320 050 for lactoglobulin and 9c9 and 35 000 000 for

Delta 8-10B) and then actually perform a limited num-

ber of crystallization experiments (on the top 20 pre-

dicted scores) to determine if the neural net was able to

predict crystallization conditions and perhaps condi-

tions that yield different crystalline habits.Table5

Comparisonofexperimentalconditionthatyield

crystalforDelta

8-10B

Experiment

Tem

perature

[Protein],

dilution

[Buffer]

Buffer

pH

[Salt]

Salt

[Organic,

%]

Organic

[Glycerol]

[Divalent]

Divalent

[Additive]

Additive

Score

Trained

14

2.300

0.100

MBicine

8.3

0.567

MNa

acetate

0.9

%PEG

400

0.0

0.010

MCaCl 2

0.050

%BOG

6

Predicted

22

1.500

0.100

MAcetate

4.5

0.648

MNa

chloride

11.6

%PEG

M5000

0.0

0.010

MCaCl 2

0.000

None

6

204 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206

5. Summary

In summary, the three technologies presented offer the

potential to significantly reduce the amount of time and

sample required to produce diffraction-quality crystals.Furthermore, preliminary experimental data indicate

that the incomplete factorial screen may be more effective

than available commercial screens are for determining

initial crystallization conditions. The ability to success-

fully use response surface optimization protocols may be

directly affected by protein purity, aggregation, and

process scale up. The accurate assessment of the potential

usefulness of response surface optimization may requireproteins that can be expressed/purified in large (6–10mg)

batches and are stable over a period of several weeks. The

NanoScreen crystallization system effectively minimizes

the amount of sample required while accommodating

both microbatch and traditional vapor diffusion meth-

ods. Both the neural network and Chernov algorithm

appear to accurately model crystallization using only a

small sample (�0.1%) of the crystallization space. Im-portantly, self-learning algorithms may be useful in pre-

dicting optimal crystallization conditions from the entire

set of crystallization conditions (328 050) for a particular

protein, thus addressing a major bottleneck in high-

throughput crystallization. An interesting observation is

that the actual scoring of the screening experiments can

have a dramatic outcome on the ability of mathematical

analyses to model crystallization. Furthermore, it is an-ticipated that class hierarchies may be created based

upon the crystallization functions, enabling more rapid

structure determination.

The eventual goal of this research is to incorporate

the incomplete factorial screen/response surface opti-

mization and the neural net crystallization prediction

program with the nanoliter crystallization robot. If

successful, this approach is expected to minimize ex-periment time and the quantity of protein required to

determine optimum crystallization conditions.

Acknowledgments

We thank Dr. Charles Carter for his consultation and

guidance with the development of our incomplete fac-torial design and response surface optimization pro-

gram. Funding for this research was provided by NIH

Grant P50-GM62407 and NASA Cooperative Agree-

ment NCC8-246.

References

Box, G.E.P., Behnken, D.W., 1960. Technometrics 2, 455.

Box, G.E.P., Hunter, J.S., 1957. Ann. Math. Stat. 28, 195.

Box, G.E.P., Hunter, W.G., Hunter, J.S., 1978. Statistics for Exper-

imenters. Wiley Interscience, New York.

Bray, T.L., Kim, L.J., Askew, R.P., Harrington, M.D., Rosenblum,

W.M., Wilson, W.W., DeLucas, L.J., 1998. New crystallization

systems envisioned for microgravity studies. J. Appl. Crystallogr.

31, 515–522.

Bray, T.L., Powell, D.L., DeLucas, L.J., 1997. Dynamic control of

vapor diffusion protein crystal growth. Am. Inst. Phys. Space

Technol. Appl. Int. Forum Proc., 705.

Carter Jr., C.W., 1997. Response surface methods for optimizing and

improving reproducibility of crystal growth. Methods Enzymol.

276, 74–99.

Carter Jr., C.W., Carter, C.W., 1979. Protein crystallization using

incomplete factorial experiments. J. Biol. Chem. 254, 12219–12223.

Chayen, N.E., 1997. The role of oil in macromolecular crystallization.

Structure 5, 1269–1274.

Collingsworth, P.D., Bray, T.L., Christopher, G.K., 2000. Crystal

growth via computer controlled vapor diffusion. J. Cryst. Growth

219, 283–289.

DeTitta, G.T., Biance, M.A., Collins, R.J., Faust, A.M.E., Kacz-

marek, J.N., Luft, J.R., Fehrman, N.A., Pangborn, W.A., Salerno,

J.M., Wolfley, J.R., 2001. Macromolecular crystallization in a high

throughput setting. Conference and Exhibit on International Space

Station Utilization, Cape Canaveral, FL, October 15–18.

Gilliland, G.L., 1988. A biological macromolecular crystallization

database: a basis for a crystallization strategy. J. Cryst. Growth 90,

51–59.

Hennessy, D., Buchanan, B., Subramanian, D., Wilkosz, P.A.,

Rosenberg, J.M., 2000. Statistical methods for the objective design

of screening procedures for macromolecular crystallization. Acta

Cryst. D Biological Crystallography 56, 817–827.

Hofmeister, F., 1888. On the understanding of the effects of salts.

Arch. Exp. Pathol. Pharmakol. (Leipzig) 24, 247–260.

Jurisica, I., Rogers, P., Glasgow, J., Fortier, S., Luft, J., Wolfley, J.,

Bianca, M., Weeks, D., DiTitta, G., 2001. Intelligent decision

support for protein crystal growth. IBM Syst. J. 40, 394–409.

Kuhn, P., Wilson, K., Patch, M.G., Stevens, R.C., 2002. The genesis of

high-throughput structure-based drug discovery using protein

crystallography. Curr. Opin. Chem. Biol. 6, 704–710, doi:10.1016.

Lamzin, V.S., Perrakis, A., 2000. Current state of automated crystal-

lographic data analysis. Nat. Struct. Biol. Struct. Genom. Suppl.,

978–981.

Luft, J.R., Wolfley, J., Jurisica, I., Glasgow, J., Fortier, S., DeTitta,

G.T., 2001. Macromolecular crystallization in a high throughput

laboratory: the search phase. J. Cryst. Growth 232, 591–595.

Mueller, U., Nyarsik, L., Horn, M., Rauth, H., Przewieslik, T.,

Saenger, W., Lehrach, H., Eickhoff, H., 2001. Development of a

technology for automation and miniaturization of protein crystal-

lization. J. Biotechnol. 85, 7–14.

Roses, A.D., 2002. Genome-based pharmacogenetics and the phar-

maceutical industry. Nat. Rev. Drug Discovery 1, 541–549.

Santarsiero, B.D., Yegian, D.T., Lee, C.C., Spraggon, G., Gu, J.,

Scheibe, D., Uber, D.C., Cornell, E.W., Nordmeyer, R.A., Kolbe,

W.F., Jin, J., Jones, A.L., Jaklevic, J.M., Schultz, P.G., Stevens,

Fig. 25. The chart demonstrates that the buffer index and pH were the two most important factors in the crystallization of Delta 8-10B.

L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206 205

R.C., 2002. An approach to rapid protein crystallization using

nanodroplets. J. Appl. Crystallogr. 35, 278–281.

Segelke, B.W., Rupp, B., Lekin, T.P., Krupka, H.I., Azarani, A.,

Todd, P., Wright, D., Wu, H.-C., 2002. The high speed Hydra-

Plus-One system for automated high-throughput protein crystal-

lography. Acta Crystallogr. D 58, 1523–1526.

Shaw Stewart, P.D., Baldock, P.F.M., 1999. Practical experimental

design techniques for automatic and manual protein crystallization.

J. Cryst. Growth 196, 665–673.

Further reading

Nagy, L., 2003. Screen conditions developed by CBSE investigator.

Temperature: 4, 15, and 22 �C. Protein concentration: 100, 65, and

35% of the provided stock. The stock is assumed to be highly

concentrated. Buffer pH: 4.5, 6, 7, 7.5, 8.3, 9. Total precipitant

(supersaturation S): chosen to give high, medium, and low concen-

trations relative to the amount of protein. For each precipitating

agent, an empirical constant, Ks, represents its molar strength as a

precipitating agent. The total amount of precipitant is calculated

using the empirical equation Ks1 ½Precip1 þ Ks2 ½Precip2 ¼ lnðSÞ�ln½macromolecule. Ratio of organic and ionic precipitants: chosen

to be 0.05 (organic precipitant is an additive), 0.4, 0.75. Glycerol

concentration: 0, 3, 6%. Ionic precipitant: chosen to reflect a variety

of basicities and Hofmeister behaviors, sodium acetate, sodium

chloride, sodium citrate, sodium malonate, ammonium sulfate, and

potassium thiocyanate.Where applicable, the salts are titrated to the

desired pH. Organic precipitant: MPD, polyethylene glycol 5000

monomethyl ether, and polyethylene glycols 400, 1450, 4000, and

8000. Divalent cations: none, 10mM calcium chloride and 10mM

magnesium chloride. Additives: none, 50mM arginine HCl, 0.05%

n-octyl-b-DD-glucopyranoside.

206 L.J. DeLucas et al. / Journal of Structural Biology 142 (2003) 188–206