High-Throughput Protein Crystallization

HIGH-THROUGHPUT PROTEIN CRYSTALLIZATION By NAOMI E. CHAYEN Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, London SW7 2AZ, UK

I. II.

III.

IV. V.

VI.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Screening of Crystallization Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Screens: Problems and New Developments . . . . . . . . . . . . . . . . . . . . . . B. High-Throughput Crystallization Robotics . . . . . . . . . . . . . . . . . . . . . . C. How Many Experiments Should Be Set Up? Are There Certain Volumes That Are Ideal?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Choosing the Crystallization Method for High-Throughput Experiments . . A. Vapor Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Free Interface Diffusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Microbatch. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. The Effect of Different Oils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Crystallization of membrane proteins in high throughput . . . . . . . . . . . . . . Follow Up of “Hits” Obtained by Screening—Optimization Strategies. . . . . A. Separation of the Nucleation and Growth Phases of Crystallization . . . B. Influencing the Crystallization Environment: Crystallization in Gels. . . C. Control of Evaporation Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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ABSTRACT Structural genomics projects have led to great progress in the field of structural biology. Considerable advances have been made in the automation of all stages of the pipeline from clone to structure. This chapter focuses on crystallization that is one of the major bottlenecks in this pipeline. It discusses new developments and describes a variety of techniques for high-throughput screening and optimizing of conditions for crystallization.

ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY, Vol. 77 DOI: 10.1016/S1876-1623(09)77001-9

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Copyright 2009, Elsevier Inc. All rights reserved.

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I.

INTRODUCTION

For the first time ever, biological processes and human diseases are being understood at a molecular level. Protein crystallography plays a major role in this understanding because proteins, being the major machinery of living things, are often the targets for drugs. The function of these proteins is determined by their three-dimensional structures and hence a detailed understanding of protein structure is essential for rational design of therapeutic treatments (Blundell et al., 2002; Chayen, 2005). Producing high quality crystals has always been the bottleneck to structure determination because protein crystallization is a complex multiparametric process. In addition, most proteins of interest are limited in supply and are labor-intensive and costly to produce. Structural genomics, which aims to determine the structures of thousands of proteins, has put great pressure on the crystallography community to produce suitable crystals. To handle the enormous numbers of experiments required to crystallize all those proteins, it was important to automate the experimental procedures so that they can be conducted in high-throughput mode. Large investment by public and commercial organizations has made this feasible in the past 8 years. There are no “magic bullets” that will guarantee the production of decent crystals and finding conditions of crystallization for a new protein is compared to searching for a needle in a haystack. The first step is to set up screening trials, in other words to expose the protein to be crystallized to numerous different crystallizing agents to find “hits” or “leads” that point to conditions that may be conducive to crystallization. Crystals, crystalline precipitate, and phase separation are usually considered leads that are worth pursuing. Once a lead deemed to be conducive to crystallization is identified, optimization can be performed in one of two ways: (a) The most common way is to fine-tune the crystallization conditions by varying the concentration of protein, precipitants, pH, temperature, or by adding additives. This in fact is also screening that focuses on a more defined range of conditions.

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(b) A second means of optimization is to actively influence and control the crystallization environment as the trial takes place, to lead crystal growth in the direction that will give the best results (Chayen, 2003a). Examples of experimental methods for screening and for optimization will be given and discussed.

II.

SCREENING

OF

CRYSTALLIZATION CONDITIONS

A. Screens: Problems and New Developments The screens of choice so far are sparse-matrix screens. These are mainly based on the records of past success. The first screens relied on the compilation of crystallization results (Jancarik and Kim, 1991), but more recent sparse-matrix screens have surfaced, based either on precipitants and additives, the usefulness of which was discovered later (e.g., Cudney et al., 1994), or on the much wider record of (positive) results provided by the Biological Macromolecule Crystallization Database (Gilliland et al., 1994). Systematic screens having the advantage of providing useful solubility information even before crystals are obtained are also used (e.g., Brzozowski and Walton, 2001; Haire, 1999; Ries-Kautt, 1999; Segelke, 2001). Apart from not providing systematic information on the system, an additional problem of sparse-matrix screens is that the statistics are increasingly biased. As new trials concentrate around conditions that have already been selected to be part of the screen, new positive results tend to cluster around the old ones and other possible conditions are neglected. Thus, success breeds success and a sort of vicious circle is established (although not completely vicious, or it would have been abandoned). Thus, careful data mining and assessment of negative results are necessary (DeLucas et al., 2005; Page and Stevens, 2004). A straightforward way to somewhat reduce the bias would be to periodically reassess the screens in view of new published results, awarding a much higher “score” to rare conditions that have given “hits,” than to ones that are well represented in standard screens.

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The use of neural network technology has recently been applied to the prediction of successful crystallization conditions (DeLucas et al., 2005; Rupp and Wang, 2004). Relying on the results from an incomplete factorial screen, the software is developed to predict the outcomes of nonperformed (virtual) trials, that is, of the full factorial screen. The emergence of new screens for both the systematic and sparsematrix approaches is seen every year (e.g., Busso et al., 2005; McPherson and Cudney, 2006; Newman, 2006). A comprehensive review on the use of numerous screens has been written by Berry et al., 2006.

B. High-Throughput Crystallization Robotics High-throughput robotics renders the initial crystallization screening far less labor-intensive and requires much less sample than trials performed manually. The former is realized by automating as many stages of the procedure as possible, starting with the mixing of the stock solutions, dispensing the protein and crystallizing agents into plates, sealing the plates (e.g., Bard et al., 2004; Bergfors, 2007; Berry et al., 2006; Hui and Edwards, 2003; Luft et al., 2003), and scoring the outcomes with the use of image recognition (Bern et al., 2004; Cumbaa et al., 2003; Spraggon et al., 2002; Wilson, 2006). Some systems even suggest new experiments based on the results obtained. Apparatus that can distinguish between salt and protein crystals based on UV fluorescence imaging (e.g. Judge et al., 2005; Li et al., 2005) is now commercially available (Dierks et al., 2008), thus indicating which “hits” are worth pursing for optimization of the conditions.

C. How Many Experiments Should Be Set Up? Are There Certain Volumes That Are Ideal? The streamlined high-throughput procedures adopted by structural genomics projects allow for the first time a wide intra- and interlaboratory comparison between the conditions in different screens, which takes into account not only successes but also

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failures and total numbers of trials. The indications are that there is significant redundancy of conditions between and within those screens; therefore, minimal screens can be developed. For example, at the Joint Center for Structural Genomics, Scripps Research Institute, it was found that 67 out of 480 conditions tried would have been sufficient to crystallize 84% of the total 465 proteins that were crystallized, while an additional 29 conditions would have raised that score to 98% (Page and Stevens, 2004; Rupp and Wang, 2004). Rupp and Wang (2004) estimate that <300 trials (i.e., three 96-well plates) should be sufficient to find crystallization conditions in promising cases, above which time and resources would best be spent in changing organism or construct, or looking for orthologs (Chayen and Saridakis, 2008). The volumes of trials using robotics range between 5 and 300 nl depending on the method used. Miniaturization can increase the number of “false negatives,” that is, conditions that would have given crystalline material if tried at larger volumes, and, on the other hand, positive results that cannot be scaled up because the outcome relies crucially on the modified kinetics provided by the nanoliter scale. Drop volumes in the hundreds of nanoliters can therefore have some advantages over those in the tens of nanoliters (Chayen and Saridakis, 2008). Several laboratories have demonstrated that using 100 nl of protein solution þ 100 nl of crystallizing agents will produce crystals that can be x-rayed directly from such drops without having to scale up (Walter et al., 2003).

III. CHOOSING

THE

CRYSTALLIZATION METHOD EXPERIMENTS

FOR

HIGH-THROUGHPUT

A. Vapor Diffusion The most widely used method of crystallization has always been and still is vapor diffusion using either hanging drops or sitting drops. This is the case when conducting experiments manually and also when harnessing robotics. Vapor diffusion involves a crystallization

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drop that equilibrates against a reservoir containing crystallizing agents, at either higher or lower concentrations than in the drop. There are however instances when vapor diffusion is not an ideal method because of a number of problems associated with it such as: (i) (ii) (iii) (iv)

Uncontrollable changes in drop volume Changes in pH due to volatile ions Slight temperature change can cause dissolution of crystals Uncontrollable changes in the composition of the drop during the crystallization process (v) The trials are not easily transportable (vi) Minimum quantity of protein is larger than other methods. The above issues have led to a demand for alternative methods that would overcome these problems as well as enable the experimenter to obtain maximum information on the molecule to be crystallized while using minimum amounts of sample. B.

Free Interface Diffusion

Alternatives that have recently become practical are counterdiffusion and microfluidics. Counterdiffusion, similar to free interface diffusion (FID), is a technique where the protein and precipitant solutions are juxtaposed in a capillary. By slow diffusion of one into the other, a concentration gradient that changes with time is established inside the capillary (Ng et al., 2003) and the system thus “self-selects” the optimal nucleation and growth supersaturation level. Although this technique is most powerful when suitable precipitant(s), additive(s), pH, and buffers are already established, that is, as a fine-tuning screening technique (that can be thought of as a first-line optimization), an initial screening version using the method has appeared, as a crystallization cassette in which multiple capillaries can be simultaneously filled with the protein solution and then come into contact each with a different precipitant solution well (Ng et al., 2003). This device thus combines screening and preliminary optimization. Several variations of the FID basic method, such as diffusion into or from gelled solutions or through intermediate gel

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plugs to further slow down the diffusion, have been developed (Garcia-Ruiz and Ng, 2007). The most recent variant, designed for very high throughput, is the use of microfluidic chips. Up to eight proteins under 96 conditions can be screened (with the in-built fine-tuning of supersaturation provided by FID) simultaneously in the TOPAZ 8.96 chips (Fluidigm Corp., San Francisco, CA, USA). The protein and precipitant solutions are loaded into the chips and put into contact through microfabricated valves (Hansen et al., 2002; Lau et al., 2007). The power of the method lies not only in the miniaturization, which leads to minimal protein consumption and maximal speed, but also in the special physics of fluid flow and mass transport in microchannels (Chayen and Saridakis, 2008), such as the dominance of laminar over turbulent flow, large surface/ volume ratio, and so on (Hansen and Quake, 2003). Experiments using microvalve-controlled microfluidic chips have been reported that have resulted in increased numbers of “hits” compared to conventional screens (Lau et al., 2007; Sommer and Larsen, 2005). There is no doubt that microfluidics is very successful but it currently requires dedicated and relatively expensive hardware and consumables. C. Microbatch A simpler alternative crystallization method is microbatch (Chayen, 1997; Chayen et al., 1990). This is a microscale batch experiment in which crystallization trials are dispensed and incubated under lowdensity (0.87 g cm3) paraffin oil to prevent their evaporation (Fig. 1). The thickness of the oil layer is 4 mm that transforms to 6 ml in standard microbatch plates, or a ratio of 1:50 between the drop and the oil. The aqueous crystallization drops are denser than the paraffin oil, and therefore they remain under the oil where they are protected from airborne contamination and shock, thus making the trays easily transportable (Chayen and Saridakis, 2008). The fundamental difference between the diffusion (i.e., vapor diffusion and FID) and microbatch methods is that diffusion methods are dynamic systems in which conditions are changing throughout the crystallization process, while in batch, the samples are mixed at their final concentration at the start of the experiment thus conditions are

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FIG. 1. Microbatch crystallization. (A) Schematic diagram of crystallization drops under oil representing hundreds of trials that are dispensed in the plate. The drops remain covered and protected by the oil throughout the experiment. (B) Dispensing of a microbatch trial under oil. The dashed circle represents the initial position of the crystallization drop at the time of dispensing. As the dispensing tip is withdrawn from the oil, the aqueous drop detaches from it and sinks to the bottom of the vessel (based on Fig. 1 in Chayen, 1997). The tip does not necessarily need to enter the oil; some robotic systems dispense the drops in a noncontact mode by “shooting” them into the oil.

constant within a normal time (1–3 weeks) of a crystallization experiment. Because of the stability of conditions in microbatch, there are usually no changes in drop volume, pH nor dissolution of crystals (Chayen, 1998). Microbatch is the simplest crystallization method and therefore lends itself easily to performing high-throughput trials. Current robots can dispense microbatch trials down to 1 nl volumes. Based on the type of oils used to cover the trials, this technique can be applied to screening, fine-tuning, and optimization experiments. Conversion of the conditions from vapor diffusion to microbatch and vice versa is easily achieved (Chayen, 1998).

D.

The Effect of Different Oils

The stability of the batch is an important benefit for conducting diagnostic studies on the process of crystal growth since the history of the sample can be followed reliably. However, this benefit may

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become a handicap in the case of screening for crystallization conditions since it is likely that the gradual change of conditions (en route to equilibrium), which takes place by the diffusion methods, may be the crucial factor for the formation of crystals (Chayen, 1997; Chayen et al., 1996; D’Arcy et al., 1996). A modification of the original microbatch method provides a means of simultaneously retaining the benefits of a microbatch experiment while gaining the inherent advantage of the self-screening process of a diffusion trial (D’Arcy et al., 1996). This modification is based on the following rationale: water can evaporate through different oils at different rates. Paraffin oil acts as a good sealant allowing only a negligible amount of water evaporation through it during the average time required for a crystallization experiment. In contrast, water can diffuse freely through silicone oils. A mixture of paraffin and silicone oils permits partial diffusion, depending on the ratio at which they are mixed (D’Arcy et al., 1996). It has been shown that for screening purposes it is preferable to use silicone oil or a mixture of paraffin and silicone oils (D’Arcy et al., 1996, 2003). This allows some evaporation of the drops leading to a higher number of “hits” and faster formation of crystals compared to trials that are set under paraffin oil. In the case of optimization, where the conditions need to be known and stable, the trials must be covered by paraffin oil. Microbatch can be used for almost all the known precipitants, buffers, and additives including detergents. The oils do not interfere with the visibility of the crystals under the microscope or with the common precipitants such as salts, polyethylene glycols (PEG), Jeffamine, methyl-penthane-diol (MPD), and even glycerol and ethanol. Indeed, one of the most intriguing aspects of crystallization in oils is its success with membrane proteins (see next section). Microbatch, although, cannot be used for crystallization trials containing small volatile organic molecules such as phenol, dioxane, or thymol, because these molecules dissolve into the oil (Chayen, 1998). All the other crystallization techniques do not have this constraint. The microbatch method has been adapted by several laboratories and Genomics Consortia for high-throughput screening experiments utilizing a variety of apparatus. Luft et al. (2001) were the first to adapt the microbatch method to high throughput

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by using a large bank of syringes (387), which dispensed 0.4 l volumes into 1536-well microassay plates. This enabled them to dispense 40,000 trials per day—a major breakthrough in 1999. The apparatus has since been modified to enable dispensing of nanoliter volumes and the Hauptman Woodward is now a major center where samples are sent from laboratories worldwide for screening experiments. Other devices for applying the microbatch technique have also been developed (e.g., Bodenstaff et al., 2002; Chayen, 2005; DeLucas et al., 2003; Juarez-Martinez et al., 2002). Moreover, apparatus that was initially built for dispensing vapor diffusion trials such as the Cartesian, Mosquito, and many others have also incorporated the microbatch technique.

IV. CRYSTALLIZATION

OF MEMBRANE PROTEINS IN HIGH THROUGHPUT

From the crystallization point of view, there is no significant difference in the methods for crystallizing membrane proteins. The difficulties in crystallizing these proteins are mainly due to the inherent qualities of the membrane proteins. Special screens based on previous successes with membrane proteins have been developed (e.g., Iwata, 2003) and commercialized, such as the MembFac (Hampton Research, Aliso Viejo, CA, USA), and Memstart and Memsys (Molecular Dimensions, Newmarket, Suffolk, UK), but often these proteins crystallize in the standard screens used for soluble proteins. As for soluble proteins, the most commonly used method for crystallizing membrane proteins is vapor diffusion; however, microbatch, microfluidics and lipidic cubic phase, or lipidic mesophase are also used successfully. The idea of crystallizing membrane proteins under oil is counterintuitive because of doubts about the suitability of an oil-based method for crystallizing lipophilic compounds. Surprisingly, an increasing number of membrane proteins in a variety of different detergents have been crystallized in microbatch under oil. Some of these had failed to crystallize by all methods other than microbatch (e.g., Henkamer, 1992; Snijder, 2003; Stock et al., 1999) and more recently P-glycoprotein (P-gp), multidrug resistance protein 1 (MRP1) (M. Rosenberg and G. Nneji, personal communication), and several others.

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FIG. 2. A drop under oil containing crystals of a membrane protein. The outer circle identifies the boundary of the aqueous drop; the inner circle is the bottom of the crystallization plate. The crystals are of the chlorophyll-binding protein 43 (CP-43) of the Photosystem II complex from spinach. Courtesy of International University Line from Chayen (2003b). (See Color Insert.)

Dispensing is identical to that of soluble proteins: it is quick and simple; thousands of trials can be dispensed using nanoliter volumes. Figure 2 shows a “hit” consisting of crystals of a chlorophyll-binding protein (CP) of photosystem II that were obtained by screening with a robot using a standard screen, (i.e., the screen was not specially made for membrane proteins). The oil does not limit the type of detergent used and a large number of detergents have been successfully applied, among them dodecyl-b-D-maltoside, n-decyl-b-D-maltoside, n-decyl-b-Dglucopyranoside, Triton X-100, n-nonyl-b-D-glucopyranoside, Sulfobetaine-14, Sulfobetaine-12, n-octyl-b-D-glucopyranoside, and N,N-dimethyl-dodecylamine oxide (Nield, 1997). Over the past 8 years, miniaturized methods that exploit the ability of lipids to form liquid crystals or mesophases and to reconstitute

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membrane proteins have been designed (Caffrey, 2003; Nollert, 2002). A technique was described for setting up trials in lipidic cubic phase using standard microwell plates utilizing nanoliter quantities 200 nl of lipidic cubic phase compared with microliter quantities used previously. Crystallization setups were prepared using syringes assembled into a semiautomatic dispenser and dispensed into microwell plates such as Terazaki plates. The plates were sealed with clear transparent tape and stored at different temperatures. Diffracting crystals of bacteriorhodopsin were obtained using this procedure (Nollert, 2002). More recently, a robotic system for crystallizing membrane proteins in lipidic mesophases was reported (Cherezov et al., 2004), even down to picoliter scale (Muthusubramaniam et al., 2004). A magnificent breakthrough using the lipidic cubic phase for crystallizations that have led to the structure determinations of G-protein-coupled receptors has been made very recently (Cherezov et al., 2007; Hanson and Stevens, 2009; Jaakola et al., 2008).

V.

FOLLOW UP

OF

“HITS” OBTAINED BY SCREENING—OPTIMIZATION STRATEGIES

In some cases the screening procedures yield high quality crystals without having to set up further trials. Mostly, one needs to fine-tune the conditions of a “hit” by setting up experiments varying the concentrations of the protein, crystallization agents, and pH around the hit conditions (e.g., Walter et al., 2005). All robotic systems are able to perform such experiments. However, the majority of trials display one of the following outcomes: (i) no crystals at all (ii) too many tiny, low quality crystals or amorphous precipitate (iii) large, visibly good looking crystals that do not diffract at all. When these scenarios occur, most people tend to give up their attempts to crystallize. They usually go back to the construct and produce a modified sample of protein with the hope that it will be crystallizable. This often works but there is no way of knowing in advance which preparation will lead to successful crystallization

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even when rational surface mutations (e.g., Derewenda and Vekilov, 2006), in situ proteolysis (Dong et al., 2007), or reductive methylation (Kim et al., 2008) have been made. All these involve considerable time, effort, and expense with no guarantee that crystals will be obtained (Chayen and Saridakis, 2008). Ways to overcome problems of obtaining either too many low quality crystals and precipitates, or good looking crystals that do not diffract include the following: (i) Separating the nucleation and growth phases of crystallization, (ii) Influencing the crystallization environment, and (iii) Influencing the kinetics of the crystallization process. Most of these strategies are still applied manually, yet gradually, they are being automated and adapted to high throughput. Several strategies for optimization of “hit” conditions that can be performed in high-throughput mode are described in the next sections.

A. Separation of the Nucleation and Growth Phases of Crystallization A very successful means to optimize crystal quality by separation of nucleation and growth is seeding that has been performed manually for over 30 years (reviewed by Bergfors, 2003). A new automated method to tackle cases where spontaneous nucleation was low or the crystal morphology was poor was devised by D’Arcy et al. (2007). This method achieves both screening and optimization by incorporating the addition of seeds into the screening procedure using a standard crystallization robot. In practice, crystals grown in one set of conditions are seeded into a secondary screen of 96 crystallization solutions. The seed stocks can be stored at 193 K and can survive many cycles of freezing and thawing without a decrease in the nucleation effect observed. In all cases tested, an improvement in the number of hits was observed, which varied from a 2.7-fold to a 65-fold increase compared with the initial screens. In some cases, the nucleation could be controlled and the crystal morphology considerably improved by simply using a more diluted seed stock (D’Arcy et al., 2007).

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Another means to separate nucleation and growth is the dynamic change of crystallization conditions, to shift the experiment from the spontaneous nucleation to the metastable zone as soon as the first stable nuclei have formed. The aim is to start the trial at nucleation conditions and after a given time to “back off” to conditions of growth. This can be achieved by adding protein-free buffer, thus diluting the protein and precipitating agent in the drops (Chayen and Saridakis, 2008). Robots can be programmed to dilute the trials at regular intervals (Saridakis et al., 1994). Dynamic light scattering (DLS) shows when nucleation is taking place before anything can be seen under a microscope and can therefore pinpoint the exact timing to dilute the drops (Fig. 3). DLS measurements have recently been automated and adapted to be used with nanoliter volumes in highthroughput mode (Dierks et al., 2008). Based on the dilution techniques, a method akin to dilution was developed using microfluidic technology (Gerdts et al., 2006). It involves mixing nanodroplets (plugs) formed and incubated at

(A)

1h

(B)

1 h 20′ 1 nm 10 nm 100 nm 1 µm

6h

6 h 20′ 10 µm

1 nm

10 nm 100 nm 1 µm

10 µm

FIG. 3. Dynamic light scattering (DLS) measurements during the crystallization process. Reprinted from Saridakis et al. (2002) with permission of the International Union of Crystallography http://journals.iucr.org. (A) Measurements at 1–1 h 20 min after setting up the experiment showing particles of a size between 1 and 10 nm. (B) Measurements at 6–6 h 20 min after set-up of the experiment showing particle size between 1 and 10 m indicating that nucleation has taken place. (See Color Insert.)

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spontaneous nucleation conditions with plugs at lower supersaturation, that is, at growth conditions. The procedure takes place in microfluidic channels; by controlling flow rates and length of the channels along which the nucleation plugs travel, the dilution time can be varied in as small increments as desired. Each nucleation plug can be used to “seed” multiple growth plugs. B.

Influencing the Crystallization Environment: Crystallization in Gels

Gelled media reduces convection and sedimentation and has been shown to yield crystals of improved quality compared with crystals grown in solution (Robert et al., 1999). In the past, the application of gels to crystallization was complicated; however, the last 6 years have seen major improvements in the use of gels by miniaturization as well as automation of gelled trials. Gelled trials can be dispensed by robots such as the IMPAX (Chayen, 2004; Chayen and Saridakis, 2002) and the Mosquito robot. The gel is dispensed while still a liquid and after a given time polymerization occurs. Crystals can also be grown in small volumes of gel inside capillaries, thus combining the advantages of growth in gel with those of the FID method (Garcia-Ruiz and Ng, 2007). A comparison of crystals grown in gels with those grown in standard trials (Fig. 4) shows an improvement in crystal size and order. Tetramethyl orthosilane (TMOS) at a final concentration of 0.2% in the drops is the most convenient gel to use with robotics as it takes longer to gel compared to agarose and other gels. C. Control of Evaporation Kinetics It is well known that nucleation is a prerequisite for and the first step in crystal growth, yet excess nucleation yields a large number of small crystals instead of a small number of useful ones. A means of controlling nucleation by reaching nucleation slowly and then stopping it before it becomes excessive can now be carried out in microbatch using any robot. This is achieved by controlled evaporation, and therefore concentration, of the drops through a thin oil layer. Evaporation is later arrested by increasing the thickness of the oil layer.

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(B)

FIG. 4. Standard and gelled microbatch drops dispensed by the IMPAX robot. Reprinted from Chayen and Saridakis (2002) with permission of the International Union of Crystallography http://journals.iucr.org. (A) C-Phycocyanin crystals in a standard drop. 1 cm = 0.32 mm. (B) C-Phycocyanin crystals in a gelled (TMOS) drop under otherwise similar conditions as (A). Scale 1 cm = 0.32 mm. (See Color Insert.)

The paraffin oil generally used in standard microbatch trials is not completely impermeable to the aqueous solution that constitutes the crystallization drops. The conventional microbatch method involves using a layer of oil thick enough (4 mm, corresponding to 8 ml covering all 72 wells of a microbatch plate measuring 8  5.5  0.9 cm3) to render evaporation through it negligible within the time scale of a crystallization experiment (typically 1-3 weeks). However, if controlled evaporation is required, the thickness of the layer can become an active parameter of the process. Instead of setting the microbatch conditions well inside the nucleation zone of the phase diagram, conditions are set to be undersaturated or metastable and water is allowed to evaporate slowly through a thin oil layer. This optimum range for the oil layer thickness is independent of the protein used. It is of course a function of the volume of the original drop. The solution therefore arrives at the nucleation zone in a controlled way. The thickness of the oil layer is then increased, rendering evaporation negligible, and the experiment progresses along the conventional batch route. Assuming that the evaporation has been arrested at the early stages of nucleation, this means that the trial spends most of its lifetime in the metastable zone of conditions, as the

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protein gets absorbed into the forming crystals. If trials are allowed to evaporate without arresting, showers and eventually drying out of the drops occurs. Arresting the evaporation can enhance the size and yield of useful crystals compared with crystals grown in the standard microbatch method (Chayen and Saridakis, 2002). The availability of apparatus using DLS (Dierks et al., 2008) is currently applied to determine the optimal time at which to add more oil to the trials. Several other optimization techniques involving the influence of the crystallization environment are covered in detail in a book entitled “Protein crystallization strategies for structural genomics” (Chayen, 2007).

VI. SUMMARY The past 7 years have seen great achievements in the field of protein crystallization. It is currently possible to test thousands of potential crystallization conditions by setting up trials of nanoliter volumes in a high-throughput mode. Monitoring the outcome of the experiments is also fully automated. These advances have cut the time of setting up and analyzing the experiments from weeks to minutes, a scenario that was inconceivable several years ago. The post-genomics era has enabled the development of new tools to overcome the bottleneck of protein crystallization. Recent research advances are opening up the scope for the development of new science-based techniques and sophisticated apparatus to monitor and control the crystallization process. The exploration of a variety of parameters that could previously not be investigated is now possible. The coming years promise to bring further advances to both screening and optimization procedures that will play a major role in raising the success rate of producing high quality crystals. ACKNOWLEDGMENTS I thank Dr. Lata Govada for helping with the references for this chapter. The European Commission OptiCryst Project LSHG-CT-623 2006–037793 and the UK Engineering and Physical Sciences Research Council (EP/G027005) are acknowledged for financial support.

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