High-throughput crystallization-to-structure pipeline at RIKEN SPring-8 Center

  • Michihiro Sugahara
  • Yukuhiko Asada
  • Katsumi Shimizu
  • Hitoshi Yamamoto
  • Neratur K. Lokanath
  • Hisashi Mizutani
  • Bagautdin Bagautdinov
  • Yoshinori Matsuura
  • Midori Taketa
  • Yuichi Kageyama
  • Naoko Ono
  • Yuko Morikawa
  • Yukiko Tanaka
  • Hiroki Shimada
  • Takanobu Nakamoto
  • Mitsuaki Sugahara
  • Masaki Yamamoto
  • Naoki Kunishima


A high-throughput crystallization-to-structure pipeline for structural genomics was recently developed at the Advanced Protein Crystallography Research Group of the RIKEN SPring-8 Center in Japan. The structure determination pipeline includes three newly developed technologies for automating X-ray protein crystallography: the automated crystallization and observation robot system “TERA”, the SPring-8 Precise Automatic Cryosample Exchanger “SPACE” for automated data collection, and the Package of Expert Researcher’s Operation Network “PERON” for automated crystallographic computation from phasing to model checking. During the 5 years following April, 2002, this pipeline was used by seven researchers to determine 138 independent crystal structures (resulting from 437 purified proteins, 234 cryoloop-mountable crystals, and 175 diffraction data sets). The protocols used in the high-throughput pipeline are described in this paper.


Structural genomics High-throughput X-ray diffraction Crystal structure 





Advanced Protein Crystallography Research Group


Molecular replacement


Protein Data Bank


Knowledge of the three-dimensional (3-D) structures of proteins at the atomic level is essential for understanding proteins’ detailed molecular mechanisms. For example, determining the 3-D structural information around the active site is an indispensable prerequisite for structure-based drug design. X-ray crystallography is one of the most powerful techniques available for determining the 3-D structures of proteins. Recently, various high-throughput pipelines for determining protein crystal structures have been developed by several structural genomics initiatives [1, 2, 3, 4, 5, 6, 7]. In Japan, a 5-year structural genomics project called the “National Project on Protein Structural and Functional Analyses” (also known as the “Protein 3000 project”) funded by the Ministry of Education, Sports, Science and Technology (MEXT) was conducted from April 2002 to March 2007 [8]. On behalf of the RIKEN Structural Genomics Initiative (RSGI; http://www.rsgi.riken.go.jp), the RIKEN SPring-8 Center contributed to the Protein 3000 project through the X-ray protein crystallography utilizing the synchrotron radiation of SPring-8, Japan. In the RIKEN SPring-8 Center, several high-throughput facilities for protein crystallography have recently been developed, including the automated crystallization and observation robot system “TERA” [9], the SPring-8 Precise Automatic Cryo-sample Exchanger “SPACE” [10] at beamline BL26 [11] for automated data collection, the heavy-atom searching program “HATODAS” for derivatization of protein crystals (ref: http://hatodas.harima.riken.go.jp) [12], the versatile cryoprotectant and its application to heavy-atom derivatization [13], the microporous zeolite as a hetero-epitaxic nucleant for protein crystallization [14], and the automated crystallographic computation system “PERON” (Package of Expert Researcher’s Operation Network; Asada et al., in preparation). The efficiency of the structure determination pipeline greatly depends upon the protocols by which the developed elemental technologies are integrated with it. Here, we introduce the high-throughput crystallization-to-structure pipeline at the Advanced Protein Crystallography Research Group (i.e., the APCR-group, formerly known as the Highthroughput Factory) of the RIKEN SPring-8 Center.

Target selection in structural genomics

The APCR-group focuses on the elucidation of the structure-function relationship of oligomeric proteins [15, 16, 17, 18, 19, 20, 21, 22, 23, 24]. Proteins from ten microorganisms were selected for this project: mesophilic eubacterium Esherichia coli K-12; moderate thermophilic eubacteria Geobacillus kaustophilus HTA426 and Symbiobacterium toebii; extremely thermophilic eubacterium Thermus thermophilus HB8; hyperthermophilic eubacteria Aquifex aeolicus VF5 and Thermotoga maritima MSB8; hyperthermophilic archaea Pyrococcus horikoshii OT3, Aeropyrum pernix K1, Methanocaldococcus jannaschii DSM2661, and Sulfolobus tokodaii strain7. We selected not only the structure-unknown proteins but also the orthologs of the structure-known proteins. A structural comparison between multiple orthologs facilitates understanding the structure-function relationship of oligomeric proteins (i.e., the evolution of oligomeric states [19, 20], the contribution of oligomeric states to thermo-stabilization [15], the functional significance of oligomeric states [21, 22], and the discovery of new oligomerization modes [18, 24]). The results may be applied to development in protein crystal engineering, because the protein crystal can be regarded as an infinite oligomer of protein molecules through crystal contacts. For instance, the elucidation of the structure-stability relationship of protein oligomers may allow us to improve the quality of protein crystals by an appropriate modification at the crystal contacts. In structural genomics, the cyclopedic structural analysis tends to be a tedious task. Notably, a broad research theme such as the protein oligomerization for a structural genomics research group practically allows each researcher to select specific targets based on his/her scientific interest, which is quite favorable for maintaining a high quality of research in their cyclopedic structural analysis. The oligomeric states of target proteins were analyzed as follows. For the first step, to predict the oligomeric state of the target protein, a sequence similarity search against the proteins of known oligomeric state was performed using the programs BLAST and PSI-BLAST [25]. Subsequently, the oligomeric state of the target protein in solution was analyzed by a dynamic light-scattering (DLS) experiment [26] using the DynaPro MS/X (Protein Solutions) instrument. Of the 138 proteins whose crystal structures were determined in the APCR-group and deposited in the Protein Data Bank (PDB), the oligomeric states in solution were analyzed by DLS for 103 proteins. Notably, 58% (60/103) of the crystal structures were consistent with the oligomeric state estimated by DLS, indicating that other methods should be employed to re-analyze the oligomeric state for the remaining 42% of the proteins. A size-distribution analysis by analytical ultracentrifugation may be applicable for this purpose, because it is one of the most accurate methods for determining the oligomeric state of proteins in solution. A total of 1260 proteins from ten organisms were targeted in the APCR-group. Of these proteins, 991 were selected for expression, 887 were successfully expressed, and 725 were purified with sufficient quality for crystallization trials (Table 1).
Table 1

Cumulative summary of protein structure determination in the APCR-group


Thermus thermophilus HB8

Pyrococcus horikoshii OT3




92 (55)

162 (227)

183 (6)

437 (288)


73 (76)

113 (234)

48 (4)

234 (314)

X-ray data

59 (62)

73 (221)

43 (4)

175 (287)


51 (54)

61 (142)

37 (3)

149 (199)

PDB deposition

49 (45)

58 (121)

31 (3)

138 (169)

Percent of solved proteinb

55.4 (98.2)

37.7 (62.6)

20.2 (50.0)

34.1 (69.1)

Percent of solved crystalc

69.9 (71.1)

54.0 (60.7)

77.1 (75.0)

63.7 (63.4)

The numbers of independent protein samples from the indicated organisms are shown on the left of each column for each analysis step, where “independent protein” is defined as a protein which shares limited sequence similarities of less than 90% in amino-acid identity with structure-known proteins in the PDB. The values in parentheses indicate the numbers of protein samples for functional analysis, such as mutant and complex

aThe numbers of mountable crystals with a score greater than 6 are shown

b100 × n(phasing)/n(purified)

c100 × n(phasing)/n(crystallized)


The purified recombinant protein samples for crystallization were supplied mainly from the RIKEN Genomic Science Center and the Structurome Research Group [8]. The protein samples were crystallized using the oil-microbatch method [27] implemented in the automated crystallization and observation robot system, TERA [9]. A sparse-matrix crystal screening of TERA was performed using both the original kit (144 conditions for the initial screening and 3456 (144 × 24) conditions for optimization) and the following commercially available kits: Crystal Screen I/II, PEG Ion, Crystal Screen Cryo, and Crystal Screen Lite from Hampton Research; Wizard I/II and Cryo I/II from Dedode Genetics. The oil-microbatch crystallization was carried out at 291 K using a Nunc HLA crystal plate with 72 wells (Nalge Nunc International). The crystal plate for TERA was assigned a unique barcode address. A crystallization drop of 1.0 μl was set up by using a 1:1 mixing of protein solution and precipitant solution in a well of the crystal plate which was then covered with 15 μl of paraffin oil. Utilizing the automatic observation function of TERA, the photographic image of each well was automatically taken and recorded once a week for two months. TERA provided access to the images via a WEB user interface. The droplet images were evaluated by manual scoring on an integer scale: 0 for a clear drop; 1 for a grainless, heavy precipitate; 2 for a fine, white precipitate; 3 for a granulated, transparent precipitate; 4 for a coarse, granulated precipitate; 5 for a micro-crystal with dimensions less than 0.05 mm; 6 for a mountable needle or plate crystal with a thickness of 0.01–0.03 mm; 7 for a mountable cluster of crystals; 8 for a mountable single crystal with a thickness greater than 0.03 mm and with dimensions less than 0.2 mm; 9 for a mountable single crystal with dimensions greater than 0.2 mm; −1 for a dried-up drop that left no solution [28]. Computerized evaluation and scoring of crystallization droplets is a key technology that is required for fully automated crystallization experiments. Although this technology is not yet available, the development is in progress using image classification software [28, 29]. Crystals from the initial screening with scores ranging from 5 to 7 were optimized using a 24 well grid screening around the initial condition with variations in the buffer pH and the precipitant/salt concentrations.

The TERA system is capable of setting up 40 crystal plates (2880 drops) of crystallization screening per day and taking observation images for 200 crystal plates (14,400 drops) per day. During the 5 years course of the Protein 3000 project, 234 (53.5%) of 437 purified independent protein samples yielded cryoloop-mountable crystals in the APCR-group, where “independent protein” is defined as a protein which shares limited sequence similarities of less than 90% in amino-acid identity with structure-known proteins in the PDB (Table 1). Of the 138 PDB structures in Table 1, 67 are derived from optimization screening, indicating that more than 48% of the structures were determined using crystals from optimized crystallization conditions. The sitting-drop and hanging-drop methods of vapor diffusion are the most commonly used techniques for the screening of protein crystallization. Accordingly, the sitting-drop vapor diffusion method is popular in the European structural genomics project “SPINE” (Structural Proteomics In Europe) [30]. The sitting-drop vapor diffusion method employs currently available nanolitre-scale systems such as the T2K crystallization system [31] which can process a 20 nl protein solution per droplet. However, we adopted the oil-microbatch method for TERA which processes 0.5 μl protein solution per droplet, because this method is the simplest technique mechanically, which is an important factor for the robustness of automated systems. Over a million of crystallization conditions have been screened using the TERA system since 2002, indicating its high reliability and durability in the case of protein samples with sufficient quantities. However, with current X-ray crystallography, the crystallization of proteins with poor crystallizability is becoming a major problem that cannot be resolved by further minimization in sample amounts. Therefore, in order to overcome this limitation, we need new methods to improve protein crystallizability, such as nucleant-mediated crystallization methods using mineral substances [32], porous silicon [33], mesoporous bioactive gel-glass [34], and microporous zeolite [14].

Robotic screening for diffraction quality of crystals

The diffraction experiment was carried out using an in-house Cu diffractometer with a Rigaku R-AXIS VII imaging-plate detector and the SPring-8 Precise Automatic Cryosample Exchanger SPACE [10]. Protein crystals with a score of 6 or higher were manually mounted one at a time in a SPACE cryoloop [35]. One of the advantages of the screw thread type pin of SPACE is the ability to reproduce the sample position among multiple mounting trials, although other synchrotron facilities worldwide employ sample changers using a magnetic-base sample pin, such as the EMBL/ESRF/BM13 robotic sample changer (SC3) [36] and the LBL-ALS automount robot [37]. The crystals were then flash-frozen under a nitrogen gas stream at 100 K and automatically stored in a sample storage tray at cryogenic temperatures by SPACE. The bar-coded sample storage tray was a metal block with 52 cylindrical wells for storing the same number of cryoloops. On the initial stage of the diffraction experiments, a protein crystal with a unique identification number was treated with the versatile cryoprotectant [13], in order to adapt it to the large variety of crystals from TERA. Two diffraction images from two orthogonal crystal orientations were collected at 100 K, with the oscillation angle and the exposure time per frame of 1° and 3 min, respectively. The in-house diffraction checking for 52 crystals (a block of the SPACE tray) was performed within 10 hours, using the procedure described above. The crystal mosaicity and the resolution limit were evaluated using the program HKL2000 [38]. Based on the results of the diffraction experiments, the crystallization conditions and the cryoprotectant conditions were optimized to reach the best quality of crystals. For heavy-atom derivatization, protein crystals were treated by the versatile cryoprotectant with heavy-atom reagents [13]. The trial order of heavy-atom reagents was decided based on suggestions from the heavy-atom searching program HATODAS (refer to http://hatodas.harima.riken.go.jp) [12]. A combined use of the program HATODAS and the versatile cryoprotectants with heavy-atom reagents enabled an efficient heavy-atom derivatization screening with the automated SPACE system. After the diffraction checking, crystals of high-quality diffraction were placed in liquid nitrogen with the storage tray until the time of data collection. The in-house robotic screening of diffraction quality helped us to use the synchrotron radiation beamtime more efficiently. However, the crystal harvesting process from the crystallization plate still requires human intervention and is therefore the most time-consuming step, depending upon the specific person’s skill with crystal handling. Unfortunately, current robotic technology’s accuracy for this process is not yet sufficient to replace manual handling. This should be resolved in the near future through further advance in robotic technology.

Data collection and processing

Diffraction data sets for the selected crystals were collected using the beamline SPACE facility [10] with a RIGAKU Jupiter CCD and a RIGAKU RAXIS V imaging-plate detector at the BL26B1/B2 of SPring-8 [11]. In the early stages of the Protein 3000 project, diffraction data were collected using manual mounting of crystals on Hampton cryoloops (Hampton Research) at BL26B1. The shift to automation, using SPACE, started in 2003 and completed in 2006. At the beamline BL26B1/B2, the Beamline Scheduling Software (BSS) for automated operation of beamline experiments was developed [39]. The BSS system’s client software provides a graphical user interface that allows users to manage the experiment schedules, communicating between device servers and databases. The experiment schedules can be registered as a list of experiments including the preliminary diffraction measurement, the MAD experiment and the XAFS profile measurement. All beamline data is processed with HKL2000 [38]. To date, 175 (74.8%) diffraction data sets from 234 protein crystals have been collected for independent structures (Table 1). However, only about half of the data sets (83 of 175, or 47.4%) were of resolution higher than 2.0 Å (Table 2). One of the major obstacles in X-ray crystallography is the production of crystals with quality high enough for the determination of structures. Diffraction resolution has an especially critical influence on the phasing and refinement processes. To improve the efficiency of the structure determination pipeline requires further incorporation of elemental technologies that help with production of high-quality crystals. For instance, we recently found a hetero-epitaxic nucleant for protein crystallization microporous zeolite which can enhance the crystallizability of a large variety of proteins [14]. Several other techniques in protein engineering have also been reported from other groups: the removal of tag [40], the isolation of stable domains [41], the reductive methylation of lysine residues [42], and the mutagenesis of surface lysine and glutamine residues [43, 44]. Furthermore, techniques such as crystal annealing, dehydration and cross-linking have been reported to be effective in improving the diffraction quality of protein crystals [45, 46]. We intend to employ these various elemental technologies in our pipeline to improve the quality of the X-ray diffraction data.
Table 2

Resolution limit of diffraction data in the APCR-group

Resolution limit d (Å)

Thermus thermophilus HB8

Pyrococcus horikoshii OT3



3.0 ≤ d

2 (0)

3 (9)

1 (0)

6 (9)

2.5 ≤ d < 3.0

4 (1)

7 (31)

4 (0)

15 (32)

2.0 ≤ d < 2.5

21 (16)

32 (121)

18 (0)

71 (137)

1.5 ≤ d < 2.0

25 (40)

28 (53)

18 (3)

71 (96)

d < 1.5

7 (5)

3 (7)

2 (1)

12 (13)

The numbers of independent protein samples from the indicated organisms are shown on the left of each column in order of resolution limit. The values in parentheses indicate the numbers of protein samples for functional analysis, such as mutant and complex

High-throughput determination of crystal structure

One of the major challenges in structural genomics is the communication of complex specifications from an expert crystallographer to the person in charge of structural analysis (who is usually inexperienced in protein crystallography). The novice user is likely to make mistakes which can make more work for the expert to correct at a later stage. The program system PERON (Package of Expert Researcher’s Operation Network; Asada et al., in preparation) enables the inexperienced user to perform the entire process of crystallographic computation in an automated and controlled manner. PERON integrates commonly available analysis programs with exhaustive combinations of parameters: SOLVE [47] for experimental phasing, Molrep [48] and EPMR [49] for molecular replacement (MR) phasing, and RESOLVE [50] and ARP/wARP [51] for model building. This PERON system allowed us to simplify project communications so that experts could recommend trying subsequent crystals if PERON calculations proved unsuccessful. The input to the PERON WEB server is a scaled reflection file from HKL2000 [38] or d*TREK (Rigaku/MSC). The efficiency in the subsequent manual model building process depends highly upon the quality of the initial model from PERON. The systematic calculation protocol of PERON screens the parameters including the resolution range, the solvent content, and the number of protein molecules in an asymmetric unit, which are critical to improving the quality of the model. The multiple calculations with varying parameters in PERON were performed in parallel using a PC cluster with 20 CPUs. In case the PERON auto-phasing failed, a manual phase calculation was tried using phasing programs such as SHARP [52], SHELXD [53], and SnB [54, 55]. Poor MR phasing from a search model sharing a low amino-acid identity (less than 30%) with the target protein was not suitable for automatic model building by RESOLVE [50] or ARP/wARP [51], and it tended to make the model-rebuilding process difficult. In such cases, experimental phasing methods using selenomethionyl proteins [56] and other heavy-atom derivatives were tried in parallel. Of phased 149 independent crystal structures in the APCR-group, more than 35% were solved by the MAD, SAD, MIR and SIR phasing methods, suggesting that the experimental phasing was effective for high-throughput structure determination (Table 1).

Structure refinement, validation and PDB deposition

After the PERON auto model building, we performed manual model revision and refinement using the programs QUANTA2000 (Accelrys Inc.) and CNS [57], respectively. A diffraction data set at a high resolution enabled the execution of the automated model building programs RESOLVE [50], ARP/wARP [51], and LAFIRE [58]. However, in spite of extensive optimization trials to achieve the best quality of crystals in the crystallization process, data sets with resolution worse than 2.0 Å comprised about 53% of total data sets in our pipeline (Table 2). Therefore, a pivotal task in the present pipeline is improving the quality of protein crystals. The stereochemical quality of refined models was evaluated by PROCHECK [59]. The manual model checking and revision of the refined structure before the PDB deposition [60] are also time-consuming and require a great deal of expertise in protein crystallography. To facilitate this process, we included PERON’s automated model checking function (Asada et al., in preparation) in our pipeline. The model fitness to electron density maps and non-covalent interactions including hydrogen-bond networks and unfavorable contacts were checked and semi-automatically revised in the PERON system. During the 5 year course of the Protein 3000 project, seven researchers in the APCR-group deposited 307 protein structures into the PDB from 725 purified samples (Table 1), indicating that each researcher determined about nine structures per year. As of January 2008, structural genomics centres worldwide purified 26930 soluble targets: 18.9% (5082) were crystallized and diffracted, and 14.4% (3872) resulted in crystal structures [61]. In our pipeline, the success rates from purified protein to crystal and from purified protein to structure were 53.4% (234/437) and 34.1% (149/437), respectively. These rates are significantly higher than those reported by structural genomics centres worldwide, although our higher success rate in the APCR-group may be greatly attributed to the higher thermostability of the sample proteins we used. Regarding the independent structures, 46.6% of the purified proteins failed to crystallize and 25.2% of the crystallized proteins did not yield diffraction data. The crystallization step had the lowest success rate: only half of the purified samples could be crystallized, suggesting that this is the most difficult step in the structure determination process. The X-ray data collection step has the second lowest success rate, emphasizing the importance of improving crystal quality. Finally, the ratio of structure-solved proteins versus purified or crystallized proteins tends to decrease year by year (data not shown), indicating rising difficulty in obtaining quality of protein samples. The next stage of structural genomics clearly requires further development in elemental technologies for the preparation of crystallizable samples and for the improvement of crystal quality.

Structural evaluation

Protein structure determination for functional analysis is an important component of our pipeline. Knowing a protein’s structure allows us to infer its biological function. Firstly, a DALI search [62] was made to check whether or not structurally similar proteins existed in the PDB. Any detected structural similarities to function-known proteins are useful to determine the function of the target protein. In addition, the programs BLAST and PSI-BLAST [25] were routinely used to predict the protein’s function. From these results, candidates for functional ligands were selected to prepare protein-ligand complexes by cocrystallization or soaking, and finally the crystal structures of the liganded forms were determined. Different crystal forms of the same protein which have variations in crystal packing were also determined to analyze the flexibility of the protein molecule. Furthermore, mutant proteins for functional analysis were purified and crystallized, if required. In the APCR-group, 199 structures were determined for functional analysis in total, and 169 structures of them were eventually deposited in the PDB (Table 1). Precise analysis of the structural changes between various crystal forms proved quite useful in understanding the structure-function relationship of the proteins. However, the structural changes observed in crystallography were not necessarily large in scale, and therefore only proteins that caused obvious structural changes had ever been the subject of research. In order to analyze the subtle structural change of proteins, we developed “the multiple superposition method” where structural changes of an oligomeric protein are precisely described as a combination of two components: an overall rearrangement of the subunit referred to as the rigid-body shift and an intra-subunit local deformation referred to as the local shift. This elemental technology was successfully applied to analyze the half-of-the-sites reactivity of the tetrameric phenylacetate degradation protein PaaI [20], in which the structure-function relationship is elucidated from its 3-D structure. While the conformational analysis procedure is convenient, successful cases such as in PaaI are still rare. Regarding the current technology in structure-based drug design, only 3-D structures at high resolution are useful for drug development. In our pipeline, the rate of collected data sets at resolution higher than 1.5 Å is less than 7% for independent proteins and 5% for functional analysis proteins (Table 2). Thus, the demand for high resolution data should be resolved with special urgency.


In the APCR-group, we developed a high-throughput crystallization-to-structure pipeline utilizing a variety of robotics, software and elemental technologies. The first 2 years of this effort focused on developing an automated environment for processing a huge number of samples in crystallization experiments, diffraction experiments and X-ray data analyses. Our automated environment enabled novice users in protein crystallography to perform structure determination. Utilizing the automated facilities, we spent the latter 3 years producing most of the research results, including the development of various elemental technologies for difficult target proteins. During the course of the Protein 3000 project, the present pipeline produced 307 PDB structures containing 169 structures for functional analysis, such as complexes and mutants (Fig. 1), indicating the pipeline made a significant contribution to the large-scale determination of protein crystal structures. As shown in Table 1, this pipeline works most effectively in the analysis of mutant structures and in ligand screening. Thus use of the present system will be focused on not only elucidating the structure-function relationship of proteins but also on developing new technologies in protein engineering toward the design of useful proteins on the basis of the large number of mutant structures. One of the most important tasks in structural genomics is to develop a high-throughput protocol for recalcitrant targets such as those from mammalian membrane proteins. The current pipeline should be continuously re-evaluated and updated to improve both the throughput and the ability to process important but difficult target proteins.
Fig. 1

Progress of structural genomics in the APCR-group.  The Highthroughput Factory was reorganized as the Advanced Protein Crystallography Research Group in 2004



Of the authors, Michi. S. contributed principally to this work, solving structures and writing the paper; YA solved structures and contributed to the automated structure determination using PERON; KS solved structures and contributed to the automated diffraction experiment using SPACE; HY solved structures and contributed to large-scale protein production; NKL, HM, and BB solved structures; YM, MT, YK, NO, YM, YT, HS, and TN contributed to the construction of the structure determination pipeline; Mitsu. S. contributed to automated crystallization using TERA; MY contributed to the automated diffraction experiment using SPACE; NK supervised this work and wrote the paper. The authors would like to thank the staff of the RIKEN Genomic Science Center and the Structurome Research Group for providing plasmids, the technical staff of the RIKEN SPring-8 Center for assistance in the large-scale protein production, the beamline staff for assistance during the data collection at the BL26B1/B2 of SPring-8, and Drs M. Miyano, T. Iizuka, S. Yokoyama, and T. Ishikawa for their direction of the APCR-group. This work was supported by the “National Project on Protein Structural and Functional Analyses” funded by the Ministry of Education, Sports, Science, and Technology of Japan.


  1. 1.
    Terwilliger TC (2000) Nat Struct Biol 7:935–939. doi:10.1038/80700 PubMedCrossRefGoogle Scholar
  2. 2.
    Service RF (2002) Science 298:948–950. doi:10.1126/science.298.5595.948 PubMedCrossRefGoogle Scholar
  3. 3.
    Pédelacq JD, Piltch E, Liong EC, Berendzen J, Kim CY, Rho BS et al (2002) Nat Biotechnol 20:927–932. doi:10.1038/nbt732 PubMedCrossRefGoogle Scholar
  4. 4.
    Chance MR, Bresnick AR, Burley SK, Jiang JS, Lima CD, Sali A et al (2002) Protein Sci 11:723–738. doi:10.1110/ps.4570102 PubMedCrossRefGoogle Scholar
  5. 5.
    Lesley SA, Kuhn P, Godzik A, Deacon AM, Mathews I, Kreusch A et al (2002) Proc Natl Acad Sci USA 99:11664–11669. doi:10.1073/pnas.142413399 PubMedCrossRefGoogle Scholar
  6. 6.
    O’Toole N, Grabowski M, Otwinowski Z, Minor W, Cygler M (2004) Proteins 56:201–210. doi:10.1002/prot.20060 PubMedCrossRefGoogle Scholar
  7. 7.
    Goh CS, Lan N, Douglas SM, Wu B, Echols N, Smith A et al (2004) J Mol Biol 336:115–130. doi:10.1016/j.jmb.2003.11.053 PubMedCrossRefGoogle Scholar
  8. 8.
    Yokoyama S, Hirota H, Kigawa T, Yabuki T, Shirouzu M, Terada T et al (2000) Nat Struct Biol 7:943–945. doi:10.1038/80712 PubMedCrossRefGoogle Scholar
  9. 9.
    Sugahara Mitsuaki, Miyano M (2002) Tanpakushitsu Kakusan Koso 47:1026–1032PubMedGoogle Scholar
  10. 10.
    Ueno G, Hirose R, Ida K, Kumasaka T, Yamamoto M (2004) J Appl Cryst 37:867–873. doi:10.1107/S0021889804019296 CrossRefGoogle Scholar
  11. 11.
    Ueno G, Kanda H, Hirose R, Ida K, Kumasaka T, Yamamoto M (2006) J Struct Funct Genomics 7:15–22. doi:10.1007/s10969-005-9005-5 PubMedCrossRefGoogle Scholar
  12. 12.
    Sugahara M, Asada Y, Ayama H, Ukawa M, Taka H, Kunishima N (2005) Acta Crystallogr D61:1302–1305Google Scholar
  13. 13.
    Sugahara M, Kunishima N (2006) Acta Crystallogr D62:520–526Google Scholar
  14. 14.
    Sugahara M, Asada Y, Morikawa Y, Kageyama Y, Kunishima N (2008) Acta Crystallogr D64:686–695Google Scholar
  15. 15.
    Lokanath NK, Shiromizu I, Ohshima N, Nodake Y, Sugahara Mitsuaki, Yokoyama S, Kuramitsu S, Miyano M, Kunishima N (2004) Acta Crystallogr D60:1816–1823Google Scholar
  16. 16.
    Asada Y, Sawano M, Ogasahara K, Nakamura J, Ota M, Kuroishi C, Sugahara Mitsuaki, Yutani K, Kunishima N (2005) J Biochem (Tokyo) 138:343–353Google Scholar
  17. 17.
    Sugahara M, Ohshima N, Ukita Y, Sugahara Mitsuaki, Kunishima N (2005) Acta Crystallogr D61:1500–1507Google Scholar
  18. 18.
    Bagautdinov B, Kuroishi C, Sugahara Mitsuaki, Kunishima N (2005) J Mol Biol 353:322–333. doi:10.1016/j.jmb.2005.08.032 PubMedCrossRefGoogle Scholar
  19. 19.
    Lokanath NK, Ohshima N, Takio K, Shiromizu I, Kuroishi C, Okazaki N et al (2005) J Mol Biol 352:905–917. doi:10.1016/j.jmb.2005.07.068 PubMedCrossRefGoogle Scholar
  20. 20.
    Kunishima N, Asada Y, Sugahara Mayumi, Ishijima J, Nodake Y, Sugahara Mitsuaki, Miyano M, Kuramitsu S, Yokoyama S, Sugahara M (2005) J Mol Biol 352:212–228. doi:10.1016/j.jmb.2005.07.008 PubMedCrossRefGoogle Scholar
  21. 21.
    Lokanath NK, Kuroishi C, Okazaki N, Kunishima N (2005) Proteins 58:769–773. doi:10.1002/prot.20345 PubMedCrossRefGoogle Scholar
  22. 22.
    Sugahara M, Nodake Y, Sugahara Mitsuaki, Kunishima N (2005) Proteins 58:249–252. doi:10.1002/prot.20281 PubMedCrossRefGoogle Scholar
  23. 23.
    Lokanath NK, Matsuura Y, Kuroishi C, Takahashi N, Kunishima N (2007) J Mol Biol 366:33–44. doi:10.1016/j.jmb.2006.11.088 CrossRefGoogle Scholar
  24. 24.
    Bagautdinov B, Kunishima N (2007) J Mol Biol 373:424–438. doi:10.1016/j.jmb.2007.08.017 PubMedCrossRefGoogle Scholar
  25. 25.
    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W et al (1997) Nucleic Acids Res 25:3389–3402. doi:10.1093/nar/25.17.3389 PubMedCrossRefGoogle Scholar
  26. 26.
    Kadima W, McPherson A, Dunn MF, Jurnak FA (1990) Biophys J 57:125–132PubMedGoogle Scholar
  27. 27.
    Chayen NE, Shaw Stewart PD, Maeder DL, Blow DM (1990) J Appl Cryst 23:297–302. doi:10.1107/S0021889890003260 CrossRefGoogle Scholar
  28. 28.
    Kawabata K, Takahashi M, Saitoh K, Asama H, Mishima T, Sugahara Mitsuaki, Miyano M (2006) Acta Crystallogr D62:239–245Google Scholar
  29. 29.
    Kawabata K, Saitoh K, Takahashi M, Sugahara Mitsuaki, Asama H, Mishima T, Miyano M (2006) Acta Crystallogr D62:1066–1072Google Scholar
  30. 30.
    Berry IM, Dym O, Esnouf RM, Harlos K, Meged R, Perrakis A et al (2006) Acta Crystallogr D62:1137–1149Google Scholar
  31. 31.
    Stevens RC (2000) Curr Opin Struct Biol 10:558–563. doi:10.1016/S0959-440X(00)00131-7 PubMedCrossRefGoogle Scholar
  32. 32.
    McPherson A, Shlichta P (1988) Science 239:385–387. doi:10.1126/science.239.4838.385 PubMedCrossRefGoogle Scholar
  33. 33.
    Chayen NE, Saridakis E, EI-Bahar R, Nemirovsky Y (2001) J Mol Biol 312:591–595. doi:10.1006/jmbi.2001.4995 PubMedCrossRefGoogle Scholar
  34. 34.
    Chayen NE, Saridakis E, Sear RP (2006) Proc Natl Acad Sci USA 103:597–601. doi:10.1073/pnas.0504860102 PubMedCrossRefGoogle Scholar
  35. 35.
    Ida K, Yamamoto T, Kumasaka T, Ueno G, Kanda H, Yokozawa Y et al (2002) Acta Crystallogr A 58(Suppl.):C300CrossRefGoogle Scholar
  36. 36.
    Cipriani F, Felisaz F, Launer L, Aksoy JS, Caserotto H, Cusack S et al (2006) Acta Crystallogr D62:1251–1259Google Scholar
  37. 37.
    Snell G, Cork C, Nordmeyer R, Cornell E, Meigs G, Yegian D et al (2004) Structure 12:537–545. doi:10.1016/j.str.2004.03.011 PubMedCrossRefGoogle Scholar
  38. 38.
    Otwinowski Z, Minor W (1997) Methods Enzymol 276:307–326. doi:10.1016/S0076-6879(97)76066-X CrossRefGoogle Scholar
  39. 39.
    Ueno G, Kanda H, Kumasaka T, Yamamoto M (2005) J Synchr Rad 12:380–384. doi:10.1107/S0909049505004735 CrossRefGoogle Scholar
  40. 40.
    Kim KM, Yi EC, Baker D, Zhang KYJ (2001) Acta Crystallogr D57:759–762Google Scholar
  41. 41.
    Johnson JL, Rajagopalan KV (1977) J Biol Chem 252:2017–2025PubMedGoogle Scholar
  42. 42.
    Rypniewski WR, Holden HM, Rayment I (1993) Biochemistry 32:9851–9858. doi:10.1021/bi00088a041 PubMedCrossRefGoogle Scholar
  43. 43.
    Czepas J, Devedjiev Y, Krowarsch D, Derewenda U, Otlewski J, Derewenda ZS (2004) Acta Crystallogr D60:275–280Google Scholar
  44. 44.
    Cooper DR, Boczek T, Grelewska K, Pinkowska M, Sikorska M, Zawadzki M et al (2007) Acta Crystallogr D63:636–645Google Scholar
  45. 45.
    Heras B, Martin JL (2005) Acta Crystallogr D61:1173–1180Google Scholar
  46. 46.
    Newman J (2006) Acta Crystallogr D62:27–31Google Scholar
  47. 47.
    Terwilliger TC, Berendzen J (1999) Acta Crystallogr D55:849–861Google Scholar
  48. 48.
    Vagin A, Teplyakov A (2000) Acta Crystallogr D56:1622–1624Google Scholar
  49. 49.
    Kissinger CR, Gehlhaar DK, Fogel DB (1999) Acta Crystallogr D55:484–491Google Scholar
  50. 50.
    Terwilliger TC (1999) Acta Crystallogr D55:1863–1871Google Scholar
  51. 51.
    Perrakis A, Morris RM, Lamzin VS (1999) Nat Struct Biol 6:458–463. doi:10.1038/8263 PubMedCrossRefGoogle Scholar
  52. 52.
    La Fortelle E, Bricogne G (1997) Methods Enzymol 276:472–494. doi:10.1016/S0076-6879(97)76073-7 CrossRefGoogle Scholar
  53. 53.
    Schneider TR, Sheldrick GM (2002) Acta Crystallogr D58:1772–1779Google Scholar
  54. 54.
    Miller R, Gallo SM, Khalak HG, Weeks CM (1994) J Appl Cryst 27:613–621. doi:10.1107/S0021889894000191 CrossRefGoogle Scholar
  55. 55.
    Weeks CM, Miller R (1999) Acta Crystallogr D55:492–500Google Scholar
  56. 56.
    Hendrickson WA, Horton JR, LeMaster DM (1990) EMBO J 9:1665–1672PubMedGoogle Scholar
  57. 57.
    Brünger AT, Adams PD, Clore GM, Delano WL, Gros P, Grosse–Kunstleve RW et al (1998) Acta Crystallogr D54:905–921Google Scholar
  58. 58.
    Yao M, Zhou Y, Tanaka I (2006) Acta Crystallogr D62:189–196Google Scholar
  59. 59.
    Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) J Appl Cryst 26:283–291. doi:10.1107/S0021889892009944 CrossRefGoogle Scholar
  60. 60.
    Bernstein FC, Koetzle TF, Williams GJ, Meyer EF Jr, Brice MD, Rodgers JR et al (1977) J Mol Biol 112:535–542. doi:10.1016/S0022-2836(77)80200-3 PubMedCrossRefGoogle Scholar
  61. 61.
    Chayen NE, Saridakis E (2008) Nat Methods 5:147–153. doi:10.1038/nmeth.f.203 PubMedCrossRefGoogle Scholar
  62. 62.
    Holm L, Sander C (1995) Trends Biochem Sci 20:478–480. doi:10.1016/S0968-0004(00)89105-7 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Michihiro Sugahara
    • 1
  • Yukuhiko Asada
    • 1
  • Katsumi Shimizu
    • 1
  • Hitoshi Yamamoto
    • 1
  • Neratur K. Lokanath
    • 1
  • Hisashi Mizutani
    • 1
  • Bagautdin Bagautdinov
    • 1
  • Yoshinori Matsuura
    • 1
  • Midori Taketa
    • 1
  • Yuichi Kageyama
    • 1
  • Naoko Ono
    • 1
  • Yuko Morikawa
    • 1
  • Yukiko Tanaka
    • 1
  • Hiroki Shimada
    • 1
  • Takanobu Nakamoto
    • 1
  • Mitsuaki Sugahara
    • 2
  • Masaki Yamamoto
    • 3
  • Naoki Kunishima
    • 1
  1. 1.Advanced Protein Crystallography Research GroupRIKEN SPring-8 Center, Harima InstituteSayo-gunJapan
  2. 2.Structural Biophysics LaboratoryRIKEN SPring-8 Center, Harima InstituteSayo-gunJapan
  3. 3.Division of Synchrotron Radiation InstrumentationRIKEN SPring-8 Center, Harima InstituteSayo-gunJapan

Personalised recommendations