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Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7

Abstract

ARP/wARP is a software suite to build macromolecular models in X-ray crystallography electron density maps. Structural genomics initiatives and the study of complex macromolecular assemblies and membrane proteins all rely on advanced methods for 3D structure determination. ARP/wARP meets these needs by providing the tools to obtain a macromolecular model automatically, with a reproducible computational procedure. ARP/wARP 7.0 tackles several tasks: iterative protein model building including a high-level decision-making control module; fast construction of the secondary structure of a protein; building flexible loops in alternate conformations; fully automated placement of ligands, including a choice of the best-fitting ligand from a 'cocktail'; and finding ordered water molecules. All protocols are easy to handle by a nonexpert user through a graphical user interface or a command line. The time required is typically a few minutes although iterative model building may take a few hours.

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Figure 1: A flowchart of the ARP/wARP procedure.
Figure 2: Troubleshooting for partially ordered ligands.
Figure 3: Model completeness achieved by ARP/wARP 7.0 'Classic' as a function of resolution.

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References

  1. Stevens, R.C., Yokoyama, S. & Wilson, I.A. Global efforts in structural genomics. Science 294, 89–92 (2001).

    Article  CAS  Google Scholar 

  2. Banci, L. et al. First steps towards effective methods in exploiting high-throughput technologies for the determination of human protein structures of high biomedical value. Acta Crystallogr. D Biol. Crystallogr. 62, 1208–1217 (2006).

    Article  CAS  Google Scholar 

  3. Lamzin, V.S. & Perrakis, A. Current state of automated crystallographic data analysis. Nat. Struct. Biol. 7 Suppl, 978–981 (2000).

    Article  CAS  Google Scholar 

  4. C.C.P.N. The CCP4 suite: programs for protein crystallography. Acta Crystallogr. D D50, 760–763 (1994).

  5. Brunger, A.T. Version 1.2 of the Crystallography and NMR system. Nat. Protoc. 2, 2728–2733 (2007).

    Article  CAS  Google Scholar 

  6. Adams, P.D. et al. PHENIX: building new software for automated crystallographic structure determination. Acta Crystallogr. D Biol. Crystallogr. 58, 1948–1954 (2002).

    Article  Google Scholar 

  7. Jones, T.A., Zou, J.-Y., Cowan, S.W. & Kjeldgaard, M. Improved methods for the building of protein models in electron density maps and the location of errors in these models. Acta Crystallogr. A 47, 110–119 (1991).

    Article  Google Scholar 

  8. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).

    Article  Google Scholar 

  9. Perrakis, A., Morris, R. & Lamzin, V.S. Automated protein model building combined with iterative structure refinement. Nat. Struct. Biol. 6, 458–463 (1999).

    Article  CAS  Google Scholar 

  10. Morris, R.J., Perrakis, A. & Lamzin, V.S. ARP/wARP and automatic interpretation of protein electron density maps. Methods Enzymol. 374, 229–244 (2003).

    Article  CAS  Google Scholar 

  11. Terwilliger, T. SOLVE and RESOLVE: automated structure solution, density modification and model building. J. Synchrotron Radiat. 11, 49–52 (2004).

    Article  CAS  Google Scholar 

  12. Ioerger, T.R. & Sacchettini, J.C. TEXTAL system: artificial intelligence techniques for automated protein model building. Methods Enzymol. 374, 244–270 (2003).

    Article  CAS  Google Scholar 

  13. Cowtan, K. The Buccaneer software for automated model building. 1. Tracing protein chains. Acta Crystallogr. D Biol. Crystallogr. 62, 1002–1011 (2006).

    Article  Google Scholar 

  14. DiMaio, F. et al. Creating protein models from electron-density maps using particle-filtering methods. Bioinformatics 23, 2851–2858 (2007).

    Article  CAS  Google Scholar 

  15. Jeyaprakash, A.A. et al. Structure of a survivin–borealin–INCENP core complex reveals how chromosomal passengers travel together. Cell 131, 271 (2007).

    Article  CAS  Google Scholar 

  16. Mapelli, M., Massimiliano, L., Santaguida, S. & Musacchio, A. The Mad2 conformational dimer: structure and implications for the spindle assembly checkpoint. Cell 131, 730 (2007).

    Article  CAS  Google Scholar 

  17. Penengo, L. et al. Crystal structure of the ubiquitin binding domains of rabex-5 reveals two modes of interaction with ubiquitin. Cell 124, 1183 (2006).

    Article  CAS  Google Scholar 

  18. Bono, F., Ebert, J., Lorentzen, E. & Conti, E. The crystal structure of the exon junction complex reveals how it maintains a stable grip on mRNA. Cell 126, 713 (2006).

    Article  CAS  Google Scholar 

  19. Allingham, J.S., Sproul, L.R., Rayment, I. & Gilbert, S.P. Vik1 modulates microtubule-Kar3 interactions through a motor domain that lacks an active site. Cell 128, 1161 (2007).

    Article  CAS  Google Scholar 

  20. Nakatsu, T. et al. Structural basis for the spectral difference in luciferase bioluminescence. Nature 440, 372 (2006).

    Article  CAS  Google Scholar 

  21. Molina, D.M. et al. Structural basis for synthesis of inflammatory mediators by human leukotriene C4 synthase. Nature 448, 613 (2007).

    Article  CAS  Google Scholar 

  22. Fisher, C., Beglova, N. & Blacklow, S.C. Structure of an LDLR-RAP complex reveals a general mode for ligand recognition by lipoprotein receptors. Mol. Cell 22, 277 (2006).

    Article  CAS  Google Scholar 

  23. Moukhametzianov, R. et al. Development of the signal in sensory rhodopsin and its transfer to the cognate transducer. Nature 440, 115 (2006).

    Article  CAS  Google Scholar 

  24. Törnroth-Horsefield, S. et al. Structural mechanism of plant aquaporin gating. Nature 439, 688 (2006).

    Article  Google Scholar 

  25. Ye, S., Li, Y., Chen, L. & Jiang, Y. Crystal structures of a ligand-free MthK gating ring: insights into the ligand gating mechanism of K+ channels. Cell 126, 1161 (2006).

    Article  CAS  Google Scholar 

  26. Qian, B. et al. High-resolution structure prediction and the crystallographic phase problem. Nature 450, 259 (2007).

    Article  CAS  Google Scholar 

  27. Brunzelle, J.S. et al. Automated crystallographic system for high-throughput protein structure determination. Acta Crystallogr. D Biol. Crystallogr. 59, 1138–1144 (2003).

    Article  Google Scholar 

  28. Fu, Z.Q., Rose, J. & Wang, B.C. SGXPro: a parallel workflow engine enabling optimization of program performance and automation of structure determination. Acta Crystallogr. D Biol. Crystallogr. 61, 951–959 (2005).

    Article  Google Scholar 

  29. Holton, J. & Alber, T. Automated protein crystal structure determination using ELVES. Proc. Natl. Acad. Sci. USA 101, 1537–1542 (2004).

    Article  CAS  Google Scholar 

  30. Liu, Z.J. et al. Parameter-space screening: a powerful tool for high-throughput crystal structure determination. Acta Crystallogr. D Biol. Crystallogr. 61, 520–527 (2005).

    Article  Google Scholar 

  31. Minor, W., Cymborowski, M., Otwinowski, Z. & Chruszcz, M. HKL-3000: the integration of data reduction and structure solution—from diffraction images to an initial model in minutes. Acta Crystallogr. D Biol. Crystallogr. 62, 859–866 (2006).

    Article  Google Scholar 

  32. Ness, S.R., de Graaff, R.A., Abrahams, J.P. & Pannu, N.S. CRANK: new methods for automated macromolecular crystal structure solution. Structure 12, 1753–1761 (2004).

    Article  CAS  Google Scholar 

  33. Panjikar, S., Parthasarathy, V., Lamzin, V.S., Weiss, M.S. & Tucker, P.A. Auto-rickshaw: an automated crystal structure determination platform as an efficient tool for the validation of an X-ray diffraction experiment. Acta Crystallogr. D Biol. Crystallogr. 61, 449–457 (2005).

    Article  Google Scholar 

  34. Vonrhein, C., Blanc, E., Roversi, P. & Bricogne, G. In Crystallographic Methods (ed. Doublié, S.) (Humana Press, Totowa, NJ, 2006).

    Google Scholar 

  35. Morris, R.J., Perrakis, A. & Lamzin, V.S. ARP/wARP's model-building algorithms. I. The main chain. Acta Crystallogr. D Biol. Crystallogr. 58, 968–975 (2002).

    Article  Google Scholar 

  36. Morris, R.J. et al. Breaking good resolutions with ARP/wARP. J. Synchrotron. Radiat. 11, 56–59 (2004).

    Article  CAS  Google Scholar 

  37. Colf, L.A., Juo, Z.S. & Garcia, K.C. Structure of the measles virus hemagglutinin. Nat. Struct. Mol. Biol. 14, 1227–1228 (2007).

    Article  CAS  Google Scholar 

  38. Wuerges, J. et al. Structural basis for mammalian vitamin B12 transport by transcobalamin. Proc. Natl. Acad. Sci. USA 103, 4386–4391 (2006).

    Article  CAS  Google Scholar 

  39. Agarwal, R.C. & Isaacs, G. Proceedings in the National Academy of Sciences 74, 2835–2839 (1977).

  40. Lamzin, V.S. & Wilson, K.S. Automated refinement for protein crystallography. Methods Enzymol. 277, 269–305 (1997).

    Article  CAS  Google Scholar 

  41. Lovell, S.C., Word, J.M., Richardson, J.S. & Richardson, D.C. The penultimate rotamer library. Proteins 40, 389–408 (2000).

    Article  CAS  Google Scholar 

  42. Joosten, K. et al. A knowledge-driven approach for crystallographic protein model completion. Acta Crystallogr. D Biol. Crystallogr. 64, 416–424 (2004).

    Article  Google Scholar 

  43. Zwart, P.H., Langer, G.G. & Lamzin, V.S. Modelling bound ligands in protein crystal structures. Acta Crystallogr. D Biol. Crystallogr. 60, 2230–2239 (2004).

    Article  CAS  Google Scholar 

  44. Evrard, G.X., Langer, G.G., Perrakis, A. & Lamzin, V.S. Assessment of automatic ligand building in ARP/wARP. Acta Crystallogr. D Biol. Crystallogr. 63, 108–117 (2007).

    Article  CAS  Google Scholar 

  45. Lamzin, V.S. & Wilson, K.S. Automated refinement of protein models. Acta Crystallogr. D Biol. Crystallogr. 49, 129–147 (1993).

    Article  CAS  Google Scholar 

  46. Cowtan, K. The Clipper C++ libraries for X-ray crystallography. IUCr Comput. Commission Newslett. 2, 4–9 (2003).

    Google Scholar 

  47. Murshudov, G.N., Vagin, A.A. & Dodson, E.J. Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr. D Biol. Crystallogr. 53, 240–255 (1997).

    Article  CAS  Google Scholar 

  48. Potterton, E., Briggs, P., Turkenburg, M. & Dodson, E. A graphical user interface to the CCP4 program suite. Acta Crystallogr. D Biol. Crystallogr. 59, 1131–1137 (2003).

    Article  Google Scholar 

  49. Brunger, A.T. Free R value: a novel statistical quantity for assessing the accuracy of crystal structures. Nature 355, 472–475 (1992).

    Article  CAS  Google Scholar 

  50. Cohen, S.X. et al. ARP/wARP and molecular replacement: the next generation. Acta Crystallogr. D Biol. Crystallogr. 64, 49–60 (2008).

    Article  CAS  Google Scholar 

  51. Cohen, S.X. et al. Towards complete validated models in the next generation of ARP/wARP. Acta Crystallogr. D Biol. Crystallogr. 60, 2222–2229 (2004).

    Article  Google Scholar 

  52. Kleywegt, G.J., Henrick, K., Dodson, E.J. & van Aalten, D.M. Pound-wise but penny-foolish: how well do micromolecules fare in macromolecular refinement? Structure 11, 1051–1059 (2003).

    Article  CAS  Google Scholar 

  53. Schuttelkopf, A.W. & van Aalten, D.M. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. D Biol. Crystallogr. 60, 1355–1363 (2004).

    Article  Google Scholar 

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Acknowledgements

This research has been supported by the NIH (grant number R01 GM62612). Part of this work has been performed under coordination of the EU BIOXHIT FW6 Integrated Project (grant number: LSHG-CT-2003-503420). S.X.C. thanks NWO/CW (VENT 700.55.405).

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Correspondence to Victor S Lamzin or Anastassis Perrakis.

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ARP/wARP version 7 is available to all users and free of charge for academic users. Commercial users or academic users that wish to use the software for commercial research can obtain a license from EMBLEM GMBH (http://www.embl-em.de) for a fee. EMBL, NKI and all authors receive financial support from that activity according to internal rules and regulations. All authors feel that the present publication is describing scientific facts and results in a manner that is not intended to promote the use of the program, and intends only to assist the user community and provide a scientific resource for the described protocol.

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Langer, G., Cohen, S., Lamzin, V. et al. Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7. Nat Protoc 3, 1171–1179 (2008). https://doi.org/10.1038/nprot.2008.91

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