for Automated Macromolecular Crystal Structure Solution

Transcription

1 Structure, Vol. 12, , October, 2004, 2004 Elsevier Ltd. All rights reserved. CRANK: New Methods for Automated Macromolecular Crystal Structure Solution DOI /j.str Ways & Means Steven R. Ness, Rudolf A.G. de Graaff, Jan Pieter Abrahams, and Navraj S. Pannu* Biophysical Structural Chemistry Gorlaeus Laboratories Leiden University 2300 RA, Leiden We present here CRANK, a new package for the automated structure solution of proteins. CRANK is composed of a number of programs, including new methods for substructure detection and refinement, and for protein structure solution from an anomalous or heavy atom signal. Thanks to the novel algorithms used, the suite The Netherlands pushes the limits on structures that can be automatically solved with minimal user intervention. In determining the structure of a macromolecule, CRANK performs a number of steps. First, the substructure is determined. Summary The substructure parameters are then refined to generate optimal phase information. These initial experimental CRANK is a novel suite for automated macromolecular phases are improved using density modification tech- structure solution and uses recently developed pro- niques to produce a map suitable for iterative automated grams for substructure detection, refinement, and model building and structure refinement. Tasks are inte- phasing. CRANK utilizes methods for substructure de- grated into a single automated suite, designed so that tection and phasing and combines them with existing the user is required to input only a small number of crystallographic programs for density modification experimentally determined parameters along with a and automated model building in a convenient and structure factor file. The parameters include the number easy-to-use CCP4i graphical interface. The data model of residues, atomic scattering factors f and f, and an used conforms to the XML extensible Markup Language estimate of the number and type of the anomalous and/ specification and works as a common language or heavy atom scatterers present. to communicate data between many different applications In order to facilitate the communication between dif- inside and outside of the suite. The application ferent programs, each step of the CRANK pipeline out- of CRANK on various test cases has yielded promising puts extensive information to the global output CRANK results: with minimal user input, CRANK can produce XML (extensible Markup Language) file. Viewers contained within the suite transparently convert this infor- mation to HTML for easy viewing in any web browser or as a text file. The strategy of outputting program run information in HTML has been used to good effect before in the SHARP suite (La Fortelle and Bricogne, 1997), and more recently in the CCP4 suite (Potterton et al., 2003). It is often challenging, however, for programs to automatically extract information from these HTML files. The use of XML allows both for visualization of data by users and also for the transport of this data inside and outside the suite. The CRANK XML file format has been based in large part on the mmcif file format, and with the converters in the suite, it is easy to convert mmcif files to CRANK XML files and to convert CRANK XML to the mmcif format. In all stages of data processing by CRANK, XML files are used to transmit information and commands be- tween the various programs. XML is a standard language in computing, and is rapidly becoming a standard language in the macromolecular crystallography community (Hamelryck and Kjeldgaard, 2001; Murray-Rust, 1998; Ito et al., 2002; Winn et al., 2002) (Leslie et al., 2002). CRANK also allows for the input and output of mmcif files and dictionaries. By using these standard and extensible languages, it is hoped that data and results can be easily and automatically analyzed and shared. Further, using the functionality in XSLT (Extensi- ble Stylesheet Language Transformations), it is possible to transform CRANK XML into any of the other currently available XML standards for storing crystallographic data, with CML (Chemical Markup Language) and the XML files used by the PDB project being two obvious and immediately useful examples. In addition, by allowing better quality solutions over currently available programs. Introduction In the field of macromolecular crystallography methods development, a current goal is to automate the repetitive and time-consuming steps in structure solution. Over the last few years, expert knowledge in the underlying crystallographic methods has been utilized to automate structure solution (Brünger et al., 1998; Terwilliger and Berendzen, 1999; Emsley, 1999; Weeks et al., 2002; Schneider and Sheldrick, 2002; Holton and Alber, 2004; Brunzelle et al., 2003; Pape and Schneider, 2004). The increase in the power and automation of crystallographic software, combined with recent hardware advances, such as the advent of robotic platforms for crystal screening and growth, the availability of more powerful synchrotron radiation sources, and the auto- mated loading of crystals, has no doubt contributed to the success of high-throughput structure solution or proteomics campaigns. A recent survey of the PDB re- vealed that several hundred structures have been de- posited from these efforts (Brunzelle et al., 2003). Given the recent successes of genomics, proteomics, and structural proteomics efforts, it is inevitable that the trend to faster generation of datasets and structures will continue. As a result, the need for new fully automated and powerful tools will become even more intense. *Correspondence: raj@chem.leidenuniv.nl

2 Structure 1754 Figure 1. CRANK Flowchart An example of data flow and program execution that is possible within the CRANK suite. Square white boxes represent programs that have CRANK XML tasks already implemented for communication within CRANK. the reading and writing of mmcif files, we hope to utilize the large amount of information and structure contained in the mmcif language and dictionary. The program CRANK employs a data centric program structure, and integrates a wide variety of existing programs together to fully automate the process of protein structure determination. By using standardized data formats and modular programming practice, it has proved relatively easy to add new crystallographic programs to the CRANK suite. The data flow within the CRANK suite can best be visualized as a pipeline with optional branches. Data flows from one end of the pipeline to

3 Ways & Means 1755 Figure 2. CRANK CCP4i Screenshot CRANK interface as implemented in the CCP4i suite. The procedures to be run can be selected in the Procedure menu. Subpanels for the tasks of substructure determination, refinement and phasing, and density modification are shown in their closed state opening the panels allows additional specific parameters to be changed. the other, with programs acting on the data as it pro- need to be written. This concept is graphically illustrated ceeds down the pipeline, becoming transformed to a in Figure 3. The other advantage to this approach is that final validated output structure at the end. In this pipe- by using program prototypes and by modifying existing line, the basic dataflow is: substructure determination, code, new programs to convert data from program out- substructure refinement and phasing, density modifica- put to XML or from XML to program input can be written tion, and model building. This pipeline is shown graphically and debugged in a very short amount of time. This facili- in Figure 1. tates the use of CRANK as an interface to many different Program control and execution is also implemented programs. CRANK interfaces have already been conin XML using a specialized dialect of XML called SOAP structed for various programs in the different stages of (Simple Object Access Protocol). SOAP is a new development a crystal structure solution. Thus, the CRANK system in the XML community and is commonly used creates a powerful and easy-to-use interface for our for applications like web services and RPC program new programs CRUNCH2 (de Graaff et al., 2001) for communication. In the CRANK suite, the CCP4i interface substructure detection and BP3 (Pannu et al., 2003; creates a SOAP request out of input and decisions from Pannu and Read, 2004) for substructure phasing, in ad- the user. A picture of the CRANK CCP4i interface is dition to allowing crystallographers to interface with existing shown in Figure 2. The CCP4i interface transmits this powerful tools for structure solution. SOAP request to the CRANK program, which interprets the XML contained within and runs the various programs Results and Discussion specified in the SOAP request. We also use a custom XML data structure for the rule-based systems within Datasets the CRANK suite. These simple methods allow us to Recently, we have performed a number of tests using make surprisingly complex decisions based on various data from single wavelength anomalous diffraction criteria of the data output by the various programs within (SAD) experiments to test the CRANK suite. We have the suite. compared the results with other programs currently There are two main advantages to using a system used for substructure detection and refinement. Inforbased on a central, standardized data communication mation and references for the datasets used may be language. The first is that without this, individual programs found in Table 1. As can be seen from this table, the must be written to convert each type of program datasets used are quite diverse, come from a wide vari- output into a form usable by other programs this leads ety of sources, and contain a wide variety of anoma- to a case where N 2 programs must be written, each lously scattering atoms. For example, there are structures transforming the data of one program into a form usable with endogenous atoms such as phosphorous as by other programs. In an implementation with a common is seen in the DNA dataset, with essential heavy metal data description language, on average 2N programs will ions like the calcium in subtilisin and the iron in C. acidur-

4 Structure 1756 Figure 3. CRANK Data Flow Diagram Illustrated is the concept of 2N connections needed in a method with a common language for transferring data between many different programs. Substructure Detection In these tests, we compare the results obtained by CRUNCH2 (de Graaff et al., 2001) with the programs SHELXD (Schneider and Sheldrick, 2002), SOLVE (Ter- williger and Berendzen, 1999), and HySS (Grosse-Kunstleve and Adams, 2003) evaluating the differences between the heavy atom positions, both in number of sites found and also the rms displacement in heavy atom ici ferredoxin, with metabolic modification in the case of selenium atoms in the E. coli thioesterase, carbohydrate binding module CBM27, and MutS datasets and with heavy halide ions soaked into the crystal, as is the case in both the human acyl-protein thioesterase and the pseudomonas serine carboxyl proteinase. In addition, the datasets represent a wide variation in the wavelength at which they were collected (from 0.88 Å to 1.54 Å), in resolution of the dataset (from 0.94 Å to 3.0 Å) and in the amount of anomalous signal present. The performance of the various algorithms on a large variety of datasets should show if methods discussed here are widely applicable in macromolecular crystallography. Table 1. Dataset Information Anomalous Wavelength f (approx.) Resolution Number of Description Spacegroup Scatterers (Å) (e ) (Å) Residues References C. acidurici P Fe (Dauter et al., 1997; Dauter ferredoxin et al., 2002) Carbohydrate P Se (Boraston et al., 2003; binding module Dodson, 2003) DNA oligomer P P (Dauter & Adamiak 2001; (CGCGCG) 2 Dauter et al., 2002) Human acyl-protein C Br (Devedjiev et al., 2000; thioesterase Dauter et al., 2002) E. coli thioesterase II P Se (Li et al., 2000; Dauter et al., 2002) Lysozyme (high P S8Cl (Dauter et al., 1999, Dauter redundancy) et al., 2002) Pseudomonas serine P6 2 9 Br (Dauter et al., 2001; Dauter carboxyl et al., 2002) proteinase Calcium subtilisin P Ca (Betzel et al., 1988; Dauter et al., 2002) MutS binding to G-T P Se (Lamers, Perrakis et al., mismatch 2000) Lysozyme (low P S2Cl (Weiss, 2001) redundancy) Crystallographic statistics and structural information of the various datasets used. Solvent content and number of residues were calculated from the final deposited PDB structure.

5 Ways & Means 1757 Table 2. Substructure Determination: Sites Found and RMS Deviations # Sites found RMS against PDB model Dataset CRUNCH2 SHELXD HySS Solve CRUNCH2 SHELXD HySS Solve C. acidurici ferredoxin 8/8 8/8 a 1/8 a 2/8 a a 1.32 a 1.57 a Carbohydrate binding 3/4 3/4 2/4 2/ module DNA (CGCGCG) 2 10/10 10/10 9/10 a 10/10 a a 0.14 a Human acyl-protein 19/22 21/22 21/22 a 18/ a 0.28 thioesterase E. coli Thioesterase II 8/8 8/8 8/8 7/ Lysozyme (high 17/18 17/18 a 13/18 a 17/ a 0.26 a 0.15 redundancy) Pseudomonas serine 8/9 7/9 8/9 8/ carboxyl proteinase Subtilisin 2/3 3/3 3/3 3/ MutS DNA binding 45/45 45/45 45/45 30/ protein Lysozyme 180 data 10/12 12/12 a 1/12 a 2/12 a a 0.98 a 2.02 a (lower redundancy) (14/18) b (14/18) b (1/18) b (2/18) b a Additional parameters were needed in program input to obtain better results over strictly default values. Details are given in the Experimental procedures section. b Shown in parentheses are the number of sites found when compared with the high redundancy lysozyme pdb file that contained more chlorine atoms. The fraction of number of sites found over the total number of sites along with the root mean squared distance of these sites, as calculated by Emma from final deposited PDB structure. positions compared to the final refined structures. These the starting point for its dual space search. For this results can be seen in Table 2, with the timing results paper, we have implemented a seeding program for in Table 3. In these comparisons, we have primarily CRUNCH2 that uses a Patterson Minimum Function used the program Emma (Grosse-Kunstleve and Adams, (PMF) algorithm. In our experiments, we have found that 2003) from the CCTBX suite, but have also used the the use of the PMF algorithm enhances the already high programs COMPARE (R.A.G.d.G., unpublished data), hit rate of CRUNCH2, allowing us to run fewer trials in and NANTMRF (Smith, 2002). our CRANK search strategies. We have also enhanced To run CRUNCH2, we use the DREAR suite (Blessing the performance of the algorithm by using heuristic rules and Smith, 1999) for the generation of E(A) substructure determined in these experiments, for example in determining factor amplitudes. We are currently developing an interface the relative difficulty of different datasets and to the DREAR suite as well as writing a new program modifying the CRUNCH2 strategy in response. to obtain multivariate likelihood estimates of E(A) values As can be seen in Table 2, CRUNCH2 was able to for a variety of diffraction experiments using equations determine the anomalously diffracting substructure in all similar to those derived previously (Burla et al., 2003). cases. In the case of ferredoxin, CRUNCH2 and SHELXD The SHELXC program ( were the only programs that could determine the full SHELX/) can also be used to generate substructure factor substructure consisting of the iron atoms in two iron amplitudes. sulfur clusters. In almost all cases, if the atoms were The traditional CRUNCH algorithm uses random starting found, the RMS distance from the PDB deposited atomic atom positions and the resulting random phases as coordinates were below 1 Å. Table 3. Substructure Determination Timing Information CRUNCH2 Time SHELXD Time HySS Time Solve Time Code (sec) (sec) (normal) (sec) (sec) C. acidurici ferredoxin (500 trials) Carbohydrate Binding Module DNA (CGCGCG) (full) 457 Human Acyl Protein Thioesterase (full) 1661 E. coli Thioesterase 1401 (20 trials) Lysozyme (strong signal) (1000 trials) 4547(full) 3202 Pseudomonas serine carboxyl proteinase Subtilisin MutS DNA binding protein Lysozyme 180 data (weaker signal) (20 trials) 4218 (1000 trials) Run times in seconds of the substructure determination programs used. In cases where more than the default 10 runs were needed, the number of runs is presented in brackets. For HySS, the normal search protocol was used, in cases where the full search protocol gave better results, the word full in parentheses is appended. All runs were performed on a 2.4GHz Intel Pentium 4 running Linux 2.4.

6 Structure 1758 Table 4. Refinement and Phasing Map Correlation and Phase Difference BP3 map SHARP map MLPHARE SOLVE map BP3 SHARP MLPHARE SOLVE code correl correl map correl correl PDIF PDIF PDIF PDIF C. acidurici ferredoxin Carbohydrate binding module DNA (CGCGCG) Human acyl-protein thioesterase E. coli thioesterase Lysozyme (strong signal) Pseudomonas serine carboxyl proteinase Subtilisin MutS DNA binding protein Lysozyme (weaker signal) Map correlation and phase difference as compared to phases calculated from the final deposited PDB structure. All phases comparisons were performed with the CCP4 program SFTOOLS on the set of phases common to all phasing and refinement programs. Although both CRUNCH2 and SHELXD were able to MLPHARE and SOLVE in all measures, including map find the substructure in the difficult cases, CRUNCH2 did correlation and phase difference, and also in the agreenot require any additional information about the target ment between the cosine of the phase difference and the structure. For SHELXD to find the full substructure in reported figure of merit. The run time of each program is the case of the ferredoxin dataset, the minimum distance shown in Table 6. criteria was lowered to 1.5. With the two lysozyme In Table 3, both map correlation and phase difference datasets in order to find both sulfurs involved in disulfide with the published structure are shown. In this table, it bonds, the minimum distance criteria was also lowered can further be seen that BP3 outperforms SHARP in 8 to 1.5. of the 10 test cases by a small amount in terms of map All of these data sets were also run with the Shake- correlation. Although the absolute difference between and-bake (SnB) suite (Weeks and Miller, 1999) that per- the results shown is small in most cases, an improveformed similarly to the results obtained with HySS. SnB ment in phase error of approximately one degree in was able to determine substructures for all but the ferro- BP3 over SHARP (as seen in the human acyl-protein doxin and low redundancy lysozyme case. However, it thioesterase and ferrodoxin cases) has led to better map is not known if these structures can be solved through building in ARP/wARP. Thus, even small improvements varying some of SnB s program parameters. in solution quality at this early stage of structure determination can have an impact at the end of structure refinement. Substructure Refinement and Phasing As can be directly seen from the results, presented in In Table 5, the figure of merit is compared to the cosine Tables 4 and 5, both BP3 and SHARP outperform of the phase difference. The figure of merit is calculated Table 5. Refinement and Phasing Figure of Merit and Cosine of Phase Difference BP3 BP3 SHARP SHARP MLPHARE MLPHARE SOLVE SOLVE code FOM cos(pdif) FOM cos(pdif) FOM cos(pdif) FOM cos(pdif) C. acidurici ferredoxin Carbohydrate binding module DNA (CGCGCG) Human acyl-protein thioesterase E. coli thioesterase Lysozyme (strong signal) Pseudomonas serine carboxyl proteinase Subtilisin MutS DNA binding protein Lysozyme (weaker signal) Figure of merits as obtained as estimates from all phasing and refinement programs. Also shown are the cosine of the phase difference cos(pdif) as compared to phases calculated from the final deposited PDB structure. All phases comparisons were performed with the CCP4 program SFTOOLS on the set of phases common to all phasing and refinement programs.

7 Ways & Means 1759 Table 6. Refinement and Phasing Timing information bp3 time sharp time mlphare time solve time code (sec) (sec) (sec) (sec) C. acidurici ferredoxin Carbohydrate Binding Module DNA (CGCGCG) Human acyl-protein thioesterase E. coli Thioesterase Lysozyme (strong signal) Pseudomonas serine carboxyl proteinase Subtilisin MutS DNA binding protein Lysozyme (weaker signal) Run times in seconds for all refinement and phasing programs. All runs were performed on a 2.4GHz Intel Pentium 4 running Linux 2.4. by the refinement program, while the cosine of the phase difference is measured from the PDB deposited structure. How well these parameters agree is critical for the performance of density modification and other subsequent steps in structure determination. As can be seen from this table, the agreement between these quantities for BP3 and SHARP is quite good. The agreement is less good in the cases of MLPHARE and SOLVE. Notably, in the latest version of SHARP, which was used for the results in this paper, its performance in estimating figures of merit has significantly improved. An interesting result was seen with the ferredoxin dataset. The best and second best CRUNCH2 solutions were input into both BP3 and SHARP. With the best CRUNCH2 solution, BP3 and SHARP gave similar phase errors, BP3 had a phase error of 44.62, and SHARP had a phase error of However, with the second best CRUNCH2 solution, while BP3 still had a phase error of approximately 44, SHARP gave a phase error of 49. This may imply that, in this test case, BP3 has a larger radius of convergence than the other refinement programs examined here. Further testing to verify the convergence radius of BP3 is being conducted using all datasets presented in this paper. Concluding Remarks Calculation of E(A) Values The first step in anomalous substructure determination is the con- version of F values to a form more amenable to the mathematical treatments used in direct methods. Each substructure determination programs does this procedure in a slightly different way. For CRUNCH2, we are currently using the DREAR module from the SnB suite (Weeks and Miller, 1999). Generally, default values were used in all cases in the DREAR interface (Blessing and Smith, 1999), with the exception that it was found useful to lower the Xmin and Ymin data cutoff limits to values of 0.1 (Weiss, 2001). In addition, for all substructure determination trials with all pro- grams, we started with a high-resolution cutoff of 0.5 Å below the maximum resolution of the dataset. This cutoff improved the perfor- mance of most the programs. In cases where this 0.5 Å cutoff failed to produce good results, different resolution cutoffs were used. The above test cases show the effectiveness of the programs CRUNCH2 and BP3 and CRANK s ability to control the execution and decision making of the programs. CRUNCH2 was able to find substructures in all test cases with its default run parameters. SHELXD was the only other program able to solve all the substructures, but required extra program parameters that may not be known by the user (i.e., the minimum distance between atoms) in order for two of the test cases to be solved. Furthermore, using default settings, BP3 pro- duced better map correlations for 8 out of the 10 data sets. The results quoted here for SHARP were the same or better than results obtained in previous papers (Dauter et al., 2002). Tables 3 and 6 show a comparison of the time the various programs used to perform its task. As can be seen, the programs CRUNCH2 and BP3 run in a time comparable to existing programs, thus the CRANK suite is equally suitable for cases with a strong anomalous signal as well as more difficult cases that may not be solvable with current suites. Furthermore, to significantly improve CRUNCH2 run times, we are currently working on identifying CRUNCH2 solutions early, as they are generated, rather than requiring the full 10 or 20 trials. The above test cases used SAD data sets exclusively, but our methods are also applicable to other classes of diffraction experiment, including multiple-wavelength anomalous diffraction and multiple isomorphous replacement experiments. CRANK has been designed from the start as a modular and flexible program for structure determination, and can thus easily be extended to use other programs. We are currently implementing a strategy to combine different algorithms for substructure detection in CRANK: to first run SHELXD to obtain a substructure solution and to verify this solution within CRUNCH2. We believe that the combination of these programs will be a powerful, efficient, and robust technique for substructure detection. Experimental Procedures In order to evaluate the effectiveness of the CRUNCH2 and BP3 algorithms and the CRANK pipeline, standardized tests of these programs were performed against the current leading programs in anomalous substructure detection, and in refinement and phasing. These other programs are used by many of the pipelines discussed in the introduction. All programs were first run with default values only. In cases when strictly default values failed to produce acceptable solutions, recommended values from the authors or program documentation were used instead. For CRUNCH2 and BP3, strictly default values only were used for all test cases.

8 Structure 1760 These special cases are described in the individual program sec- generated for the parameters of phase error, cosine of the phase tions below. error, figure of merit and map correlation with the final refined model. Automated scripts within CRANK were set up to run these programs, Substructure Determination allowing us to try many different atomic models and parameters CRUNCH2 was run with ten trials for most of the test cases prework. and to compare these different programs within the CRANK framesented below. For cases with a very low anomalous signal (i.e., To ensure that results were consistent with the original pack- lysozyme with low redundancy), twenty trials were employed. In all age, all final SHARP results were run through the SHARP program, cases, the PMF (Patterson Minimum Function) algorithm was used it is these results that are presented in the tables. to generate starting positions compliant with the Patterson map. These starting models were used for input to CRUNCH2 since they Program Availability proved to lead to better solutions than purely random starts. CRANK, CRUNCH2, and BP3 are available from the web site: CRUNCH2 requires only knowledge of the number of anomalous scatterers present and is therefore quite amenable to both inclusion in an automated suite, and is also used by users just beginning Acknowledgments crystallography. The program SHELXD does not have built in termination condi- We would like to thank the people who contributed datasets for our tions. Instead, it is designed so that the user interactively checks analysis. Z. Dauter and colleagues provided all the data from the intermediate SHELXD results, or alternatively, specifies the number Jolly SAD paper; E. Dodson and G. Davies provided us with the of trials for SHELXD to perform. For these tests, we ran SHELXD CBM1 and CBM2 datasets; M. Weiss for the Lysozyme data; and on all datasets with runs of 10, 100, and 1000 total trials. Results T. Sixma provided us with the MutS dataset. The authors would and timings from the 10 trial runs were taken in all cases, except also like to thank Fabio Dall Antonia for kindly providing us with a when a longer run produced markedly better results. On advice from beta version of the SITCOM program for substructure comparison. the program author, in the difficult test cases, a range of different Funding for this research was provided by the N.W.O. cutoffs were tried, starting at the high resolution limit and going in steps of 0.1 Å up to 1 Å below the high resolution cutoff. In addition, Received: May 18, 2004 in three of the test cases, additional parameters needed to be added Revised: June 30, 2004 to the SHELXD.ins file in order to generate solutions. In the case of Accepted: July 25, 2004 the ferredoxin and the two lysozyme datasets, the MIND parameter, Published: October 5, 2004 governing the minimum distance between atoms needed to be lowered to MIND -1.5, allowing atoms to be found at distances from 1.5 Å and up. References For the program HySS, two different search modes are available: Betzel, C., Dauter, Z., Dauter, M., Ingelman, M., Papendorf, G., Wilfast and full. All datasets were run with both search options. Again, son, K.S., and Branner, S. (1988). Crystallization and preliminary in all cases, the results from the fast search mode were taken, X-ray diffraction studies of an alkaline protease from Bacillus lentus. except when more matching atom sites were found by the full search J. Mol. Biol. 204, procedure. In communications with the author of HySS, we have been informed that in future versions, the termination criteria of Blessing, R.H., and Smith, G.D. (1999). Difference structure factor HySS will be refined, allowing users to run HySS with the fast search normalization for determining heavy-atom or anomalous scattering option in all cases. On advice from the program author, in cases substructures. J. Appl. Crystallogr. 32, where the default 0.5 Å failed to produce results, resolution cutoffs Boraston, A.B., Revett, T.J., Boraston, C.M., Nurizzo, D., and Davies, from the high resolution cutoff up to 5 Å were tried in steps of 0.5 Å. G.J. (2003). Structural and thermodynamic dissection of specific In the ferredoxin case, a cutoff of 3 Å allowed HySS to find one Fe mannan recognition by a carbohydrate binding module, TmCBM27. atom, corresponding to one of the Fe atoms in one of the two Structure 11, FeS clusters. HySS requires very little information about the target Brünger, A.T., Adams, P.D., Clore, G.M., DeLano, W.L., Gros, P., structure, with only the number and type of anomalous scatterers Grosse-Kunstleve, R.W., Jiang, J.S., Kuszewski, J., Nilges, M., required as input. Pannu, N.S., et al. (1998). Crystallography & NMR system: a new SOLVE was run with default parameters, using the SAD script software suite for macromolecular structure determination. Acta supplied with the SOLVE package as a template. As with the other Crystallogr. D54, substructure determination programs, the high-resolution reflec- Brunzelle, J.S., Shafaee, P., Yang, X., Weigand, S., Ren, Z., and tions were truncated at 0.5 Å above the highest recorded reflections. Anderson, W.F. (2003). Automated crystallographic system for high- In cases where this procedure failed to produce results, resolution throughput protein structure determination. Acta Crystallogr. D59, values starting at the high-resolution cutoff and decreasing to 5.0 Å in steps of 0.5 Å were tried. In the few cases for which SOLVE did not have atomic form factors, these were obtained from CCP4 library Burla, M.C., Carrozzini, B., Cascarano, G.L., Giacovazzo, C., and files. SOLVE requires a little more information than CRUNCH2 and Polidori, G. (2003). SAD or MAD phasing: location of the anomalous HySS, detailed examples can be found in the example scripts included scatterers. Acta Crystallogr. D 59, with the SOLVE program and on the SOLVE website. Dauter, Z., Wilson, K.S., Sieker, L.C., Meyer, J., and Moulis, J.M. (1997). Atomic resolution (0.94 A) structure of Clostridium acidurici Refinement and Phasing ferredoxin. Detailed geometry of [4Fe-4S] clusters in a protein. Bio- After the anomalously diffracting substructure had been determined, chemistry 36, heavy atom positions were then sent to the next step in the pro- Dauter, Z., and Adamiak, D.A. (2001). Anomalous signal of phosphocessing pipeline, heavy atom refinement and phasing. In order to rus used for phasing DNA oligomer: importance of data redundancy. make the comparisons more manageable, only the highest scoring Acta Crystallogr. D 57, CRUNCH2 determined substructure was used as input to the various Dauter, Z., Dauter, M., and Dodson, E. (2002). Jolly SAD. Acta. Crysrefinement and phasing programs. In order to allow for the direct tallogr. D 58, comparison of phases, the atomic positions of the highest scoring CRUNCH2 trial was superimposed on the final PDB heavy atom Dauter, Z., Dauter, M., de La Fortelle, E., Bricogne, G., and Sheldrick, positions using Emma (Grosse-Kunstleve and Adams, 2003). G.M. (1999). Can anomalous signal of sulfur become a tool for solv- The heavy atom refinement and phasing programs used were BP3 ing protein crystal structures? J. Mol. Biol. 289, (Pannu and Read, 2004), SHARP (La Fortelle and Bricogne, 1997), Dauter, Z., Li, M., and Wlodawer, A. (2001). Practical experience MLPHARE (Otwinowski, 1991), and SOLVE (Terwilliger and Berendzen, with the use of halides for phasing macromolecular structures: a 1999). In all cases, only recommended settings were used, and powerful tool for structural genomics. Acta Crystallogr. D 57, automated scripts were set up to process all datasets. Results were

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