Structural Proteomics pp 419-435

Part of the Methods in Molecular Biology™ book series (MIMB, volume 426) | Cite as

Automated Structure Solution with the PHENIX Suite

  • Peter H. Zwart
  • Pavel V. Afonine
  • Ralf W. Grosse-Kunstleve
  • Li-Wei Hung
  • Thomas R. Ioerger
  • Airlie J. McCoy
  • Erik McKee
  • Nigel W. Moriarty
  • Randy J. Read
  • James C. Sacchettini
  • Nicholas K. Sauter
  • Laurent C. Storoni
  • Thomas C. Terwilliger
  • Paul D. Adams

Significant time and effort are often required to solve and complete a macromolecular crystal structure. The development of automated computational methods for the analysis, solution, and completion of crystallographic structures has the potential to produce minimally biased models in a short time without the need for manual intervention. The PHENIX software suite is a highly automated system for macromolecular structure determination that can rapidly arrive at an initial partial model of a structure without significant human intervention, given moderate resolution, and good quality data. This achievement has been made possible by the development of new algorithms for structure determination, maximum-likelihood molecular replacement (PHASER), heavy-atom search (HySS), template- and pattern-based automated model-building (RESOLVE, TEXTAL), automated macromolecular refinement (phenix. refine), and iterative model-building, density modification and refinement that can operate at moderate resolution (RESOLVE, AutoBuild). These algorithms are based on a highly integrated and comprehensive set of crystallographic libraries that have been built and made available to the community. The algorithms are tightly linked and made easily accessible to users through the PHENIX Wizards and the PHENIX GUI.

References

  1. 1.
    Page, R., Grzechnik, S. K., Canaves, J. M., Spraggon, G., Kreusch, A., Kuhn, P., Stevens, R. C., and Lesley, S. A. (2003) Shotgun crystallization strategy for structural genomics: an optimized two-tiered crystallization screen against the Thermotoga maritima proteome. Acta Cryst. D59, 1028–1037.Google Scholar
  2. 2.
    Snell, G., Cork, C., Nordmeyer, R., Cornell, E., Meigs, G., Yegian, D., Jaklevic, J., Jin, J., Stevens, R. C., and Earnest, T. (2004) Automated sample mounting and alignment system for biological crystallography at a synchrotron source. Structure 12, 537–545.CrossRefPubMedGoogle Scholar
  3. 3.
    Adams, P. D., Grosse-Kunstleve, R. W., and Brunger, A. T. (2003) Computational aspects of high throughput crystallographic macromolecular structure determination. Methods Biochem. Anal. 44, 75–87.PubMedGoogle Scholar
  4. 4.
    Terwilliger, T. C., and Berendzen, J. (1999) Automated MAD and MIR structure solution. Acta Cryst. D55, 849–861.Google Scholar
  5. 5.
    de la Fortelle, E., and Bricogne, G. (1997) Maximum-likelihood heavy-atom parameter refinement for multiple isomorphous replacement and multiwavelength anomalous diffraction methods. Meth. Enzymol. 276, 472–494.CrossRefGoogle Scholar
  6. 6.
    Brunzelle, J. S., Shafaee, P., Yang, X., Weigand, S., Ren, Z., and Anderson, W. F. (2003) Automated crystallographic system for high throughput protein structure determination. Acta Cryst. D59, 1138–1144.Google Scholar
  7. 7.
    Schneider, T. R., and Sheldrick, G. M. (2002) Substructure solution with SHELXD. Acta Cryst. D58, 1772–1779.Google Scholar
  8. 8.
    Ness, S. R., de Graaff, R. A., Abrahams, J. P., and Pannu, N. S. (2004) CRANK: new methods for automated macromolecular crystal structure solution. Structure 12, 1753–1761.CrossRefPubMedGoogle Scholar
  9. 9.
    Holton, J., and Alber, T. (2004) Automated protein crystal structure determination using ELVES. Proc. Natl. Acad. Sci. USA 101, 1537–1542.CrossRefPubMedGoogle Scholar
  10. 10.
    Panjikar, S., Parthasarathy, V., Lamzin, V. S., Weiss, M. S., and Tucker, P. A. (2005) Auto-Rickshaw: an automated crystal structure determination platform as an efficient tool for the validation of an X-ray diffraction experiment. Acta Cryst. D61, 449–457.Google Scholar
  11. 11.
  12. 12.
    Navaza, J. (1994) AMoRe: an automated package for molecular replacement. Acta Cryst. A50, 157–163.Google Scholar
  13. 13.
    McCoy, A. J., Grosse-Kunstleve, R. W., Storoni, L. C., and Read, R. J. (2005) Likelihood-enhanced fast translation functions. Acta Cryst. D61, 458–464.Google Scholar
  14. 14.
    Kissinger, C. R., Gehlhaar, D. K., and Fogel, D. B. (1999) Rapid automated molecular replacement by evolutionary search. Acta Cryst. D55, 484–491.Google Scholar
  15. 15.
    Vagin, A., and Teplyakov, A. (2000) An approach to multi-copy search in molecular replacement. Acta Cryst. A56, 1622–1624.Google Scholar
  16. 16.
    Perrakis, A., Morris, R., and Lamzin, V. S. (1999) Automated protein model building combined with iterative structure refinement. Nat. Struct. Biol. 6, 458–463.CrossRefPubMedGoogle Scholar
  17. 17.
    Terwilliger, T. C. (2003) Automated main-chain model building by template matching and iterative fragment extension. Acta Cryst. D59, 38–44.Google Scholar
  18. 18.
    Terwilliger, T. C. (2003) Automated side-chain model building and sequence assignment by template matching. Acta Cryst. D59, 45–49.Google Scholar
  19. 19.
    Holton, T., Ioerger, T. R., Christopher, J. A., and Sacchettini, J. C. (2000) Determining protein structure from electron-density maps using pattern matching. Acta Cryst. D56, 722–734.Google Scholar
  20. 20.
    Levitt, D. G. (2001) A new software routine that automates the fitting of protein X-ray crystallographic electron-density maps. Acta Cryst. D57, 1013–1019.Google Scholar
  21. 21.
    Jones, T. A., Zou, J. Y., Cowan, S. W., and Kjeldgaard, M. (1991) Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Cryst. A47, 110–119.Google Scholar
  22. 22.
    McRee, D. E. (1999) XtalView/Xfit—a versatile program for manipulating atomic coordinates and electron density. J. Struct. Biol. 125, 156–165.CrossRefPubMedGoogle Scholar
  23. 23.
    Emsley, P., and Cowtan, K. (2004) Coot: model-building tools for molecular graphics. Acta Cryst. D60, 2126–2132.Google Scholar
  24. 24.
    Turk, D. (1992) Weiterentwicklung eines Programms fuer Molekuelgraphik und Elektrondichte-Manipulation und seine Anwendung auf verschiedene Protein-Strukturaufklaerungen. Technical University of Munich, Munich.Google Scholar
  25. 25.
    Adams, P. D., Grosse-Kunstleve, R. W., Hung, L.-W., Ioerger, T. R., McCoy, A. J., Moriarty, N. W., Read, R. J., Sacchettini, J. C., Sauter, N. K., and Terwilliger, T. C. (2002) PHENIX: building new software for automated crystallographic structure determination. Acta Cryst. D58, 1948–1954.Google Scholar
  26. 26.
    Adams, P. D., Gopal, K., Grosse-Kunstleve, R. W., Hung, L. W., Ioerger, T. R., McCoy, A. J., Moriarty, N. W., Pai, R. K., Read, R. J., and Romo, T. D., et al. (2004) Recent developments in the PHENIX software for automated crystallo-graphic structure determination. J. Synchrotron Radiat. 11, 53–55.CrossRefPubMedGoogle Scholar
  27. 27.
    Grosse-Kunstleve, R. W., Sauter, N. K., Moriarty, N. W., and Adams, P. D. (2002) The Computational Crystallography Toolbox: crystallographic algorithms in a reusable software framework. J. Appl. Crystallogr. 35, 126–136.CrossRefGoogle Scholar
  28. 28.
    Grosse-Kunstleve, R. W., and Adams, P. D. (2003) Substructure search procedures for macromolecular structures. Acta Cryst. D59, 1966–1973.Google Scholar
  29. 29.
    Weeks, C. M., and Miller, R. (1999) Optimizing Shake-and-Bake for proteins. Acta Cryst. D55, 492–500.Google Scholar
  30. 30.
    Read, R. (2001) Pushing the boundaries of molecular replacement with maximum likelihood. Acta Cryst. D57, 1373–1382.Google Scholar
  31. 31.
    Schomaker, V., and Trueblood, K. (1968) On rigid-body motion of molecules in crystals. Acta Cryst. B24, 63.Google Scholar
  32. 32.
    Winn, M. D., Isupov, M. N., and Murshudov, G. N. (2001) Use of TLS parameters to model anisotropic displacements in macromolecular refinement. Acta Cryst. D57, 122–133.Google Scholar
  33. 33.
    Brunger, A. T., Adams, P. D., and Rice, L. M. (1999) Annealing in crystallography: a powerful optimization tool. Prog. Biophys. Mol. Biol. 72, 135–155.CrossRefPubMedGoogle Scholar
  34. 34.
    Rice, L. M., and Brunger, A. T. (1994) Torsion angle dynamics: reduced variable conformational sampling enhances crystallographic structure refinement. Proteins 19, 277–290.CrossRefPubMedGoogle Scholar
  35. 35.
    Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E. (2000) The protein data bank. Nucl. Acids Res. 28, 235–242.CrossRefPubMedGoogle Scholar
  36. 36.
    Bernstein, F. C., Koetzle, T. F., Williams, G. J., Meyer, E. F., Jr., Brice, M. D., Rodgers, J. R., Kennard, O., Shimanouchi, T., and Tasumi, M. (1977) The Protein Data Bank: a computer-based archival file for macromolecular structures. J. Mol. Biol. 112, 535–542.CrossRefPubMedGoogle Scholar
  37. 37.
    Vagin, A. A., Steiner, R. A., Lebedev, A. A., Potterton, L., McNicholas, S., Long, F., and Murshudov, G. N. (2004) REFMAC5 dictionary: organization of prior chemical knowledge and guidelines for its use. Acta Cryst. D57, 2184–2195.Google Scholar
  38. 38.
    Weininger, D. (1988) SMILES 1. Introduction and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31.Google Scholar
  39. 39.
    Morris, R. J., Zwart, P. H., Cohen, S., Fernandez, F. J., Kakaris, M., Kirillova, O., Vonrhein, C., Perrakis, A., and Lamzin, V. S. (2004) Breaking good resolutions with ARP/wARP. J. Synchrotr. Radiat. 11, 56–59.CrossRefGoogle Scholar
  40. 40.
    Fisher, R. G., and Sweet, R. M. (1980) Treatment of diffraction data from crystals twinned by merohedry as intended. Acta Cryst. A36, 755–760.Google Scholar
  41. 41.
    Yeates, T. O. (1988) Simple statistics for intensity data from twinned specimens. Acta Cryst. A44, 142–144.Google Scholar
  42. 42.
    Yeates, T. O. (1997) Detecting and overcoming crystal twinning. Meth. Enzymol. 276, 344–358.CrossRefPubMedGoogle Scholar
  43. 43.
    Lebedev, A. A., Vagin, A. A., and Murshudov, G. N. (2006) Intensity statistics in twinned crystals with examples from the PDB. Acta Cryst. D62, 83–95.Google Scholar
  44. 44.
    Hyman, J., Chen, H., Di Fiore, P. P., De Camilli, P., and Brunger, A. T. (2000) Epsin 1 undergoes nucleocytosolic shuttling and its eps15 interactor NH(2)-terminal homology (ENTH) domain, structurally similar to Armadillo and HEAT repeats, interacts with the transcription factor promyelocytic leukemia Zn(2)+ finger protein (PLZF). J. Cell Biol. 149, 537–546.CrossRefPubMedGoogle Scholar
  45. 45.
    Adolph, H. W., Zwart, P., Meijers, R., Hubatsch, I., Kiefer, M., Lamzin, V., and Cedergren-Zeppezauer, E. (2000) Structural basis for substrate specificity differences of horse liver alcohol dehydrogenase isozymes. Biochemistry 39, 12885–12897.CrossRefPubMedGoogle Scholar
  46. 46.
    Golovin, A., Oldfield, T. J., Tate, J. G., Velankar, S., Barton, G. J., Boutselakis, H., Dimitropoulos, D., Fillon, J., Hussain, A., and Ionides, J. M., et al. (2004) E-MSD: an integrated data resource for bioinformatics. Nucl. Acids Res. 32, D211–216.CrossRefPubMedGoogle Scholar
  47. 47.
    Sutton, R. B., Ernst, J. A., and Brunger, A. T. (1999) Crystal structure of the cytosolic C2A-C2B domains of synaptotagmin III. Implications for Ca(+2)-inde-pendent snare complex interaction. J. Cell Biol. 147, 589–598.CrossRefPubMedGoogle Scholar
  48. 48.
    Carr, P. D., Gustin, S. E., Church, A. P., Murphy, J. M., Ford, S. C., Mann, D. A., Woltring, D. M., Walker, I., Ollis, D. L., and Young, I. G. (2001) Structure of the complete extracellular domain of the common beta subunit of the human GM-CSF, IL-3, and IL-5 receptors reveals a novel dimer configuration. Cell 104, 291–300.CrossRefPubMedGoogle Scholar
  49. 49.
    Zwart, P. (2005) Anomalous signal indicators in protein crystallography. Acta Cryst. D61, 1437–1448.Google Scholar
  50. 50.
    Brunger, A. T., Adams, P. D., Clore, G. M., DeLano, W. L., Gros, P., Grosse-Kunstleve, R. W., Jiang, J. S., Kuszewski, J., Nilges, M., and Pannu, N. S., et al. (1998) Crystallography & NMR system: a new software suite for macromolecular structure determination. Acta Cryst. D54, 905–921.Google Scholar
  51. 51.
    Potterton, L., McNicholas, S., Krissinel, E., Gruber, J., Cowtan, K., Emsley, P., Murshudov, G. N., Cohen, S., Perrakis, A., and Noble, M. (2004) Developments in the CCP4 molecular-graphics project. Acta Cryst. D60, 2288–2294.Google Scholar
  52. 52.
    Merritt, E. A. (1999) Comparing anisotropic displacement parameters in protein structures. Acta Cryst. D55, 1997–2004.Google Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Peter H. Zwart
    • 1
  • Pavel V. Afonine
    • 1
  • Ralf W. Grosse-Kunstleve
    • 1
  • Li-Wei Hung
    • 2
  • Thomas R. Ioerger
    • 3
  • Airlie J. McCoy
    • 4
  • Erik McKee
    • 3
  • Nigel W. Moriarty
    • 1
  • Randy J. Read
    • 4
  • James C. Sacchettini
    • 5
  • Nicholas K. Sauter
    • 1
  • Laurent C. Storoni
    • 4
  • Thomas C. Terwilliger
    • 6
  • Paul D. Adams
    • 7
  1. 1.Lawrence Berkeley National LaboratoryBerkeleyCalifornia
  2. 2.Biophysics Group, Los Alamos National LaboratoryLos AlamosNew Mexico
  3. 3.Department of Computer ScienceTexas A&M UniversityCollege HillTexas
  4. 4.Department of HaematologyUniversity of Cambridge, Cambridge Institute for Medical ResearchCambridgeUnited Kingdom
  5. 5.Department of Biochemistry and BiophysicsTexas A&M UniversityCollege StationTexas
  6. 6.Biophysics DivisionLos Alamos National LaboratoryLos AlamosNew Mexico
  7. 7.Berkeley Structural Genomics Center, Lawrence Berkeley National Laboratory, and Department of ChemistryUniversity of CaliforniaBerkeleyCalifornia

Personalised recommendations