CRYSTAL GROWTH & DESIGN
The Biological Crystallization Resource: Facilitating Knowledge-Based Protein Crystallizations† Chunmin Li,*,‡ Kevin L. Kirkwood,# and Gary D. Brayer‡ Department of Biochemistry and Molecular Biology, UniVersity of British Columbia, VancouVer, British Columbia V6T 1Z3, Canada, and True Logic Consulting, Coquitlam, British Columbia, Canada V3E 2C7
2007 VOL. 7, NO. 11 2147–2152
ReceiVed July 25, 2007; ReVised Manuscript ReceiVed September 19, 2007
ABSTRACT: The Biological Crystallization Resource (BCR) is a fully standardized, searchable, and comprehensive database of known crystallization conditions of biological macromolecular structures determined by X-ray crystallographic techniques, with a current total of over 18 000 entries. It was created to facilitate the discovery of the relationships between the properties of biological molecules and their optimal crystallization conditions. Construction and maintenance of the database make use of advanced data mining and manipulation techniques, with the associated software being capable of executing single or multiple parameter searches of database entries to determine optimal crystallization conditions for new targets of structural studies. It is a knowledge-based approach to deriving crystallization conditions designed to improve upon the very limited success rates observed for the random sparse matrix based screening methods currently widely employed in the field. Test results clearly demonstrate the predictive ability of BCR-derived knowledge-based crystallization screens, which not only deliver a more focused set of trial conditions but also the expectation of much higher crystallization success rates. The full BCR database, a comprehensive manual, and example demonstration are available at the following Web site: http://www.growacrystal.com.
1. Introduction Since the earliest X-ray diffraction studies of the structures of biological macromolecules began in the early part of the 20th century, very little progress has been made in our understanding of how to facilitate the process of crystallizing such macromolecules for structural analyses. As a result, obtaining useful crystalline samples remains difficult, unpredictable, and frustrating. Furthermore, while many other formerly formidable aspects of the X-ray diffraction structure determination process, such as obtaining sufficient purified material, data collection, phasing, map fitting, and structural refinements, have now been largely overcome, current screening methods to determine initial crystallization conditions remain primarily empirical and random in nature.1–4 Even more troubling is the very low success rates of these approaches (in the range of 2–3%) and that this deficiency is now a serious impediment to the worldwide effort in structural genomics.5 Such structural genomics studies have the potential to provide fundamental new knowledge about the protein folding question and the principles behind protein structure–function relationships. A fundamental problem with the currently popular sparse matrix crystallization screening methodologies used to elucidate potential crystallization conditions is that these treat all biological macromolecules as if they were the same.6,7 This process ignores the unique physical and chemical characteristics of these molecules, which are at the heart of their crystallization behavior. For the most part, the response to the very poor crystallization success rates observed has been to simply increase the size of the sparse matrix crystallization screens. This approach has had little effect on the success rate of obtaining usable crystals for structure determinations, while leading to * To whom correspondence should be addressed: Tel: 604-822-5007. Fax: 604-822-5227. E-mail:
[email protected]. † Part of the special issue (Vol. 7, issue 11) on the 11th International Conference on the Crystallization of Biological Macromolecules, Québec, Canada, August 16–21, 2006 (preconference August 13–16, 2006). ‡ University of British Columbia. # True Logic Consulting.
the consumption of large amounts of the biological molecules under study and the related chemicals used in screening trials.5 Surprisingly, large reservoirs of data that could potentially be used to establish predictive links between the physio-chemical properties of biological macromolecules and how these crystallize have been largely ignored. These include the many descriptions of individual crystallization conditions of biological macromolecules documented in the scientific literature, as well as the descriptive records that accompany Protein Data Bank (PDB) deposits.8 These sources contain information not only about the structural aspects of the macromolecules but also concerning the conditions under which they were successfully crystallized. In the Protein Data Bank alone, by the end of June 2006, a total of 38 960 biological macromolecule structures were on deposit, with 32 302 involving X-ray diffraction studies. Following deletion of duplicate entries, a total of 18 228 independent crystallization conditions can be obtained by examining PDB holdings. Clearly this is a very significant source of data concerning the crystallization process and, in conjunction with additional data in the scientific literature, holds out the prospect of allowing one to discern general predictive relationships between biological macromolecules and their crystallization behaviors.
2. Experimental Section Development of the Biological Crystallization Resource. To directly address the issue of improving biological macromolecule crystallization success rates, we have developed the Biological Crystallization Resource (BCR) database, which was first introduced at the 2005 annual meeting of the American Crystallographic Association.9 The organizational architecture and functional flow for this database is schematically shown in Figure 1. The guiding principle behind this work is that there are indeed distinct relationships to be discovered between the properties of biological macromolecules and their optimal crystallization conditions. The challenge, of course, is to extract and organize the large body of information available in a manner that such relationships become readily apparent. This requires the application of novel data mining and manipulation techniques, the complexity and
10.1021/cg700696h CCC: $37.00 2007 American Chemical Society Published on Web 11/07/2007
2148 Crystal Growth & Design, Vol. 7, No. 11, 2007
Li et al.
Figure 1. The organizational architecture and functional flow of information processing in the Biological Crystallization Resource database. Data entries are processed through three different streams, indicated as A, B, and C. magnitude of which has been a primary factor in blocking attempts to create a truly comprehensive and flexible crystallization protocol database.10–12 To develop a robust, comprehensive, and functional crystallization database for biological macromolecules, our work has followed a three step process: (1) Through a novel data mining and archival approach, all previous data concerning the crystallization of biological macromolecules are being retrieved in an ongoing process and combined into a single database. (2) Software development has been undertaken to allow for multivariant statistical analyses in the search for relationships between the characteristics of biological macromolecules and conditions that promote their crystal growth. (3) In a final step, the results of database analyses directed at particular targeted biological molecules (proteins, nucleic acids, etc.) can be used to produce a customized set of protocols for manual or robotic crystallization growth setups, designed to maximize the probability of successful crystallizations while minimizing sample and growth materials requirements. Data Acquisition Routes and Solutions. The first major challenge in the advancement of our ability to predict optimal protein crystallization conditions, based on the wide scope of existing scientific literature available, is the proper organization of this information. Furthermore, to be useful, such data must be organized in a manner that permits reliable and efficient data retrieval. Typically, the data in the available crystallization research literature and the PDB are very poorly organized. While it is possible to locate the information of
interest, the current organization is not sufficient to allow for even simple data retrievals or correlations with other work. In addition to the lack of organization, there is a lack of standards. For example, the same chemical name, method, or environment is often described in a multitude of different ways. This greatly complicates the process of data retrieval from both within a data source, such as the PDB, or between different data sources, making the subsequent integration and correlation of results across numerous pieces of work impossible. Therefore, the first major phase of our database development was to create software routines that identify, extract, and standardize variables such as pH, chemicals, methods, temperatures, units of measure, etc. The software we have developed for this purpose is now routinely successful in normalizing 85% of the data extracted from such sources as the PDB. Within the BCR, each individual record includes the following: BCR ID, data deposit date, decode date, validation date, protein name, number of residues, molecular weight, biological molecule categories, crystal information (space group and cell dimensions), crystallization methods, crystallization temperatures, protein concentrations (both in storage buffers and crystallization solutions), and pHs (in the storage buffers, reservoir solutions and initial crystallization droplets). Further data are stored concerning other chemical agents in the macromolecular storage buffer, low concentration additives to facilitate crystal growth that are part of crystallization solutions, and the chemical composition of the reservoir solution. Each entry is also linked to sequence data, amino acid frequency lists, the PDB, the Enzyme Commission Classification System (E.C.),13 the Kyoto Encyclopedia of Genes and Genomes (KEGG),14 and the Structural Classification of Proteins index.15,16
The Biological Crystallization Resource
Crystal Growth & Design, Vol. 7, No. 11, 2007 2149
Figure 2. Generic flowchart illustrating the course of a typical BCR search for optimal crystallization condition ranges for use in the creation of customized crystal growth screens for specific protein/macromolecule targets. A second phase of database preparation involved the development of rapid and comprehensive data validation screens that allowed for efficient manual oversight of the results obtained and the ability to apply minor manual corrections where required. This procedure allows an experienced individual to validate ∼100 entries per day and ensures maintenance of a high level of data quality in the database. Using this approach, the database can be easily and efficiently updated with each quarterly release of the PDB (stream A; Figure 1) or from input from other sources. A third phase of this project involved provision of the ability to carry out manual coding of entries directly into the database after examination of the scientific literature, thereby allowing us to capture additional crystallization protocol entries (stream C; Figure 1). A significant amount of such data arises from either publications in journals not requiring PDB deposit or the fact that many published results predate the requirement for inclusion of crystallization parameters in PDB entries. To facilitate this aspect of data acquisition, future software development will be directed toward machine reading of the scientific literature to obtain these earlier crystallization records. In many other instances, manual coding has also proven necessary to properly complete crystallization records only partially reported in the PDB. Also available is software to allow for direct external user deposit of crystallization protocols for biological macromolecules (stream B; Figure 1). It is expected that this route of data entry will be of particular interest to both structural genomics initiatives and commercial laboratories. Such enterprises generate a large volume of crystallization data in the course of structural studies, much of which is not publicly available. Inclusion of crystallization data from these sources could potentially serve to greatly fine tune the predictive functions of the BCR database, either globally for the whole crystallization community or internally in a specific laboratory setting. Knowledge-Based Crystallization Screen Design. Our successful compilation of a comprehensive set of crystallization records has led
to the development of user-driven custom definable searches that take full advantage of the indexed and normalized database available. A flowchart illustrating a typical BCR search for optimal crystallization conditions is shown in Figure 2. Notably, the software in use allows one to query the database in either single or multiple parameter modes. The database search process is extremely efficient and can be adapted to include additional new user-specified parameters as necessary. The end result is a list of user-identified database records that are analyzed to produce comprehensive summaries of crystallization conditions relevant to the targeted biological macromolecule. These summaries can then form the basis for the formulation of optimized custom crystallization screens directed at the target of interest. To facilitate high throughput structural studies, database records could be made to interface with available robotic platforms capable of setup and visualization of screening experiments. As part of the development process, analyses of test groups of crystallization records derived from the database have been done. These experiments clearly show the presence of strong underlying parameter correlations in crystallization conditions and the predictive power of the database. Examples of such studies are discussed in greater detail later herein. Database Implementation and Access. The software used in the data acquisition and utilization processes was chosen so as to maximally facilitate database development while minimizing the computational resources required. All of this work was done using enterprise class open source tools. The operating system chosen was CENTOS Linux version 4.2, the Database used was GreyStone Technology M (GT.M), and user screens and interface were HTML pages delivered via the Apache Web Server. These efficient, high performance software packages required minimal hardware configurations such as an Intel Pentium III with 256 MB RAM. The data mining process was carried out in three major phases. The first phase involved rapid screening of
2150 Crystal Growth & Design, Vol. 7, No. 11, 2007
Li et al.
Figure 3. A histogram illustrating the relationship found from BCR records between the total number of amino acids in proteins and the use of 2-methyl-2,4-pentanediol (MPD) in crystallizing these molecules. the entire PDB database. In this phase, the block of text that contained the crystallization conditions was isolated, and entries that did not contain any discernable crystallization conditions were eliminated. The second phase involved applying a dictionary of over 1000 common chemical terms and chemical alias names to this block of text to isolate and standardize the component chemicals. Once a chemical component was isolated, a pattern-matching algorithm was applied to any numeric data found in close proximity to the identified chemical component to resolve concentration parameters. The third and final phase was that of manual validation and correction. This involved the presentation of the components and concentrations automatically identified in a data entry form. This data entry form allowed for rapid acceptance of the results found. In cases where minor modifications were required to the data prior to acceptance, any new chemical components identified by manual intervention would be added to the chemical dictionary. Subsequent data mining activity would then take advantage of the constantly updated chemical dictionary. The BCR is accessible at http:// www.growacrystal.com.
3. Results and Discussion One of the unique features of the BCR is the ease and efficiency with which this crystallization database can be queried simultaneously for any combination of multiple parameters. The software architecture employed can therefore just as easily facilitate global studies of crystallization parameters related to overall methodology or focused studies related to a particular crystallization target of interest. Representative examples of both these types of analyses are discussed below. Examples of the Capability of the BCR To Examine General Crystallization Trends. A simple two parameter query of the BCR database related to the total number of amino acids in crystallized proteins and the use of the common crystallization agent 2-methyl-2,4-pentanediol (MPD)17 readily allows for the relationship between these parameters to be examined and illustrated as shown in Figure 3. The BCR database indicates that MPD is a common constituent in crystallization solutions, with 1275 individual proteins being reported as crystallized with its use. These results also point out that MPD is particularly useful as a precipitant with proteins having in the range of between 100 to 149 amino acids. Another simple two parameter query shows that nearly 50% of oligonucleotides are crystallized with MPD. Clearly general queries of this type can be very useful in formulating general approaches to crystallizing particular classes of biological macromolecules. Significantly, BCR database queries can also be useful in assisting in the construction of generalized sparse matrix screens. For example, the pH distribution of MPD usage in the database indicates a preference for pHs in the order of 7.5 > 6.0 > 7.0 > 6.5 > 8.0 > other. However, as illustrated in Figure 4, a commonly used commercial screen that makes use of MPD in
Figure 4. A plot showing the overall pH distribution of MPD usage in the crystallization of proteins as determined from the BCR database (thick grey line). Overlaid is a plot indicating the distribution of MPD vs. pH use in a representative commercial sparse matrix screening kit (thin black line).
Figure 5. A BCR database query histogram showing the usage of the overall best five crystallization precipitants with proteins from 40 to 1500 and 1000–1500 amino acids in length.
its trial set, does not include MPD use at pHs 6.0, 7.0, or 8.0, while including trials at pHs 4.5, 5.5, and 8.5. BCR database results clearly suggest that greatly improved crystallization success rates are likely to occur if trials are included in such screens with MPD in buffers in the optimal ranges of pH. Much more general BCR database queries can also be very informative. A query related to the overall crystallization statistics for proteins from 40 to 1500 amino acids in length shows that the best five major precipitants include ammonium sulfate and PEGs 3350, 4000, 6000, and 8000 (Figure 5). Overall, this makes PEGs the most useful of the biological crystallization agents. Significantly, the BCR data also indicate that there are useful patterns in the relationship between protein size and PEG molecular weight. As can be seen in Figure 5,
The Biological Crystallization Resource
Crystal Growth & Design, Vol. 7, No. 11, 2007 2151
Table 1. Summary of BCR Data Analysis Results for the Carboxylic Ester Hydrolases highest chemical frequencies Main Precipitants PEG4000 > MPD > AMS > PEG200 > PEG6000 > NaCl > PEG8000 > ethanol > PEG3350 > PEG3500 ) PEG5000 MME Buffers TRIS > MES > acetate > HEPES > cacodylate > nonea Salts AMS > CaCl2 > NaCl > Li2SO4 > MgCl2 > nonea highest pH frequencies 5.8–6.0 > 7.0–7.2 > 7.5–7.8 (74% crystallized between pH 5.8–8.0) other unique condition sets dioxane (2–3%), AMS isopropanol (10–20%), 50% PEG4000 a
Included as a parameter for these low concentration categories is the option of “none” or the absence of buffer or salt.
PEGs 6000 and 8000 are far more useful in crystallizing large proteins (>1000 amino acids), whereas ammonium sulfate works best with proteins of smaller size. Predictive Knowledge-Based Analyses To Derive Optimal Crystallization Trials for Specific Targets. In a more focused mode, the BCR can be used to examine the crystallization trends in a particular family of proteins (or biological macromolecules) that is related to a protein that one would like to crystallize for structural analyses (Figure 2). To demonstrate the effectiveness of this approach in efficiently determining an optimal set of crystallization trial conditions, a query to the BCR database was made for entries related to the carboxylic ester hydrolases. This was done simply by a single parameter search for the E.C. no. 3.1.1.- and produced 139 individual crystallization records. This protein family catalyzes the hydrolysis of carboxylic acid esters to form an alcohol and a carboxylic acid anion. Our next step in the analysis of these previously documented crystallization conditions followed the general flowchart pattern shown in Figure 2. In the first instance, the chemical frequency usage profiles of the isolated records were analyzed with respect to the main precipitants, buffers, and salts forming a part of crystallization conditions. The goal here was to identify and rank the most productive chemicals and their concentrations in each of these categories. As listed in Table 1, a total of 11 main precipitants can be identified as playing a role in numerous crystallizations of carboxylic ester hydrolases. Taking both the chemical frequency
and the concentration data into account, PEG 4000 is the most successful precipitant in such crystallizations. Our analysis also shows that for the most part there are five buffers involved in previous crystallizations and a further five salts, with the latter group defined as chemicals (additives) used at low concentrations to facilitate crystallization and not as the main precipitant. The next step in this analysis involved an examination of the pH frequency profile of carboxylic ester hydrolase crystallizations. BCR database records indicate that the vast majority of previous crystallizations occurred between pH 5.8–8.0 and that within this range pHs 5.8–6.0, 7.0–7.2 and 7.5–7.8 were particularly productive at producing crystals (Table 1). Beyond being able to efficiently provide frequency lists of the types discussed, a notable advantage of the BCR database approach is its ability to also identify pockets of distinctly unique crystallization condition sets that differ considerably from those normally found for a protein family. A case in point for the carboxylic ester hydrolases is the discovery of two sets of such unique crystallization conditions involving dioxane and AMS, and isopropanol and PEG 4000, which should also be considered as prospects for crystallization trials (Table 1). Therefore, a BCR database user has the option to pursue the most frequent chemical/pH frequencies in trying to crystallize a protein, or if this proves unsuccessful, to follow up with attempts using more unique, but less frequent, crystallization conditions that have been identified by the database. Having available the ranked chemical and pH frequency lists as outlined in Table 1, it is now possible to construct an optimized knowledge-based crystallization trial screen customized for the carboxylic ester hydrolases. This can be conveniently done by inputting the crystallization parameter summary lists generated by the BCR database query system into a software tool such as CRYSTOOL18 capable of calculating the necessary grid intervals needed to test the BCR recommended crystallization conditions. To test the ability of the BCR database to predict optimal crystallization conditions for carboxylic ester hydrolases, our results were compared against the recently published conditions used in the crystallization of nine proteins in this family. None of these recent results had been included as part of the information present in the BCR database. Table 2 lists the relevant crystallization data for these test proteins, along with a comparison of the predicted and observed pH ranges, as well as the rank order of the predicted BCR precipitants (Top 1–10), buffers (Top 1–6) and salts (Top 1–6) that apply to a given protein. These results clearly demonstrate that the BCR database can, with a high degree of probability, predict the likely
Table 2. Results of a Test Comparison of a BCR Analysis of Carboxylic Ester Hydrolase Crystallization Trends with Recent Independently Crystallized Members of This Enzyme Family in the Literature enzymea
pH
AHL-lactonase acetyl xylan esterase Lys49-phospholipase A2 Form1 Form2 EstE1 carboxylesterase PA3859 juvenile hormone esterase peak 1 peak 2 phospholipase A2 predictability ratio (%)
8.0 (within range) 6.0 (within range)
PEG4000 Top1 PEG6000 Top5
MgCl2 Top5 LiCl outlier
TRIS Top1 MES Top2
6.5 8.5 6.5 6.5
PEG 8000 Top7 AMS Top3 PEG 3350 Top9 PEG5000 MME Top10
AMS Top1 none Top 6 AMS Top1 AMS Top1
cacodylate Top5 TRIS Top1 Bis-Tris outlier MES Top2
AMS Top3 PEG3000 outlier ethanol Top8 89
none Top 6 none Top 6 CaCl2 Top2 89
citrate outlier citrate outlier phosphate outlier 56
(within range) outlier (within range) (within range)
5.5 outlier 5.5 outlier 6.0 (within range) 67
precipitant
salt
buffer
a Test cases for this comparison were obtained by taking relevant crystallization data from publications in the recent scientific literature and that were not included in the BCR database.19–25 Below each crystallization parameter entry is an indication of the rank order of this chemical/pH in the predictive analysis obtained by using the BCR database.
2152 Crystal Growth & Design, Vol. 7, No. 11, 2007 Table 3. Comparison of BCR Knowledge-Based Screen for the Crystallization of Carboxylic Ester Hydrolases with Two Commonly Used Commercial Screens parameters 1. Precipitants in screen no. of relevant precipitants 2. Salts in screen no. of relevant salts 3. Buffers in screen no. of relevant buffers 4. pH ranges in screen no. of relevant pHs 5. Overall relevant conditions
BCR Knowledge-Based Screen
Two Commercial Screens
11 8/9a
36 7/9a
6 8/9a 6 5/9a 5.5 6.0 6.5 7.0 7.5 8.0 6/9a 20/96 trials
23 8/9a 10 7/9a 4.6 5.6 6.5 7.5 8.5 9.0 6/9a 4/98 trials
a Refers to the number of these components found in these screens, when compared to the nine in the most recently reported carboxylic ester hydrolase crystallizations.
crystallization conditions of members of the carboxylic ester hydrolase family. This predictability appears to be highest in terms of the main precipitants and salts used, with a predictability ratio of 89%. Here predictability ratio is defined as the rate of occurrence of predicted chemicals/pHs forming a part of the set of conditions actually used in crystallizations. The pH and buffer chemical constituents appear to have a greater degree of variability, although the BCR database predictability ratio was better than 50% in both cases. To further test the ability of the BCR database to produce more focused crystallization matrix screening trial kits targeted at the carboxylic ester hydrolases, a set of 96 trial conditions was generated based on the parameters outlined in Table 1. The resultant trial conditions were then compared against those conditions published as being successful for the test proteins mentioned earlier. The results obtained are shown in Table 3 and indicate that of the 96 trial conditions generated from BCR database analyses, a total of 20 are sufficiently close to expect crystal growth of test proteins. It is also instructive to further compare this result with two popular commercially available crystallization screening kits (together having 98 trial conditions). Here, even though the commercial kits collectively scanned more precipitants (36 vs. 11), salts (23 vs. 6) and buffers (10 vs. 6) than the BCR knowledgebased screen, only 4 of 98 conditions were close enough to those actually used for the test proteins to be expected to produce crystals. Table 3 clearly shows that the BCR knowledge-based crystallization screen was not only able to deliver a more focused set of trial conditions but also the expectation of a much higher crystallization success rate. Notably, given such increased success rates and the more focused nature of a knowledge-based screen, it would be expected that this approach would significantly reduce the amounts of both proteins (biological macromolecules) and associated screening materials required to determine ideal crystallization conditions.
4. Conclusion The BCR was created on the premise that there are distinct relationships to be discovered between the properties of biological macromolecules and the optimal conditions for their crystallization. It is a knowledge-based approach to deriving crystallization conditions designed to improve upon the very limited success rates observed for the random sparse matrix based screening methods currently widely employed in the field. Searches generally begin with more global identifiers such as structural category, functional class, or E.C. no., and as the
Li et al.
search continues, progressing to more detailed analyses involving such specific parameters as pH and chemical frequencies (precipitants, buffers, salts), physical properties, temperature, methodology, etc. The end result of user-directed BCR analyses is optimized lists containing defined ranges of the specific parameters best suited to producing crystals of the target molecule. Test results clearly demonstrate the predictive ability of BCR derived knowledge-based crystallization screens that not only deliver a more focused set of trial conditions but also the expectation of much higher crystallization success rates. This approach to crystallization trials should also significantly reduce the amounts of both proteins (or other targeted biological macromolecules) and associated screening materials required to determine ideal crystallization conditions. Acknowledgment. This work was supported by operating grant MOP-13338 from the Canadian Institutes of Health Research (CIHR) to G.D.B. The authors thank Profs. Joël Janin, Leslie D. Burtnick, and Stephen G. Withers for helpful discussions.
References (1) Kantardjieff, K. A.; Rupp, B. Bioinformatics 2004, 20, 2162–2168. (2) Kantardjieff, K. A.; Jamshidian, M.; Rupp, B. Bioinformatics 2004, 20, 2171–2174. (3) McPherson, A. Methods 2004, 34, 254–265. (4) Rupp, B.; Wang, J. Methods 2004, 34, 390–407. (5) Hosfield, D.; Palan, J.; Hilgers, M.; Scheibe, D.; McRee, D. E.; Stevens, R. C. J. Struct. Biol. 2003, 142, 207–217. (6) Cudney, R.; Patel, S.; Weisgraber, K.; Newhouse, Y.; McPherson, A. Acta Crystallogr. 1994, D50, 414–423. (7) Jancarik, J.; Kim, S.-H. J. Appl. Crystallogr. 1991, 24, 409–411. (8) Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. Nucleic Acid Res. 2000, 28, 235–242. (9) Li, C.; Kirkwood, K. L.; Brayer, G. D. American Crystallographic Association 2005 Annual Meeting, 2005, Orlando, Florida, USA. (10) Gilliland, G. L.; Tung, M.; Ladner, J. E. Acta Crystallogr. 2002, D58, 916–920. (11) Charles, M.; Veesler, S.; Bonnete, F. Acta Crystallogr. 2006, D62, 1311–1318. (12) Peat, T. S.; Christopher, J. A.; Newman, J. Acta Crystallogr. 2005, D61, 1662–1669. (13) IUBMB 2005, Recommendations of the nomenclature committee of the International Union of Biochemistry and Molecular Biology on the nomenclature and classification of enzyme-catalysed reactions. http://www.chem.qmul.ac.uk/iubmb/enzyme/. (14) Kanehisa, M.; Goto, S.; Kawashima, S.; Okuno, Y.; Hattori, M. Nucleic Acid Res. 2004, 32, D277–D280. (15) Murzin, A. G.; Brenner, S. E.; Hubbard, T.; Chothia, C. J. Mol. Biol. 1995, 247, 536–540. (16) Andreeva, A.; Howorth, D.; Brenner, S. E.; Hubbard, T. J. P.; Chothia, C.; Murzin, A. G. Nucleic Acid Res. 2004, 32, D226–D229. (17) Anand, K.; Pal, D.; Hilgenfeld, R. Acta Crystallogr. 2002, D58, 1722– 1728. (18) Segelke, B. W. J. Cryst. Growth 2001232, 553–562. (19) Kim, M. H.; Kang, H. O.; Kang, B. S.; Kim, K. J.; Choi, W. C.; Oh, T. K.; Lee, C. H.; and Lee, J. K. Biochim. Biophys. Acta 2005, 1750, 5–8. (20) Krastanova, I.; Guarnaccia, C.; Zahariev, S.; Degrassi, G.; Lamba, D. Biochim. Biophys. Acta 2005, 1748, 222–230. (21) Ambrosio, A. L.; Nonato, M. C.; de Araujo, H. S.; Arni, R.; Ward, R. J.; Ownby, C. L.; de Souza, D. H.; Garratt, R. C. J. Biol. Chem. 2005, 280, 7326–7335. (22) Byun, J. S.; Rhee, J. K.; Kim, D. U.; Oh, J. W.; Cho, H. S. Acta Crystallogr. 2006, F62, 145–147. (23) Pesaresi, A.; Lamba, D. Biochim. Biophys. Acta 2006, 1752, 197– 201. (24) Wogulis, M.; Wheelock, C. E.; Kamita, S. G.; Hinton, A. C.; Whetstone, P. A.; Hammock, B. D.; Wilson, D. K. Biochemistry 2006, 45, 4045–4057. (25) Jabeen, T.; Singh, N.; Singh, R. K.; Sharma, S.; Somvanshi, R. K.; Dey, S.; Singh, T. P. Acta Crystallogr. 2005, D61, 1579–1586.
CG700696H
