Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

Macromolecular X-ray structure determination using weak, single-wavelength anomalous data

Nature Methods volume 12, pages 127–130 (2015)Cite this article

Subjects

Abstract

We describe a likelihood-based method for determining the substructure of anomalously scattering atoms in macromolecular crystals that allows successful structure determination by single-wavelength anomalous diffraction (SAD) X-ray analysis with weak anomalous signal. With the use of partial models and electron density maps in searches for anomalously scattering atoms, testing of alternative values of parameters and parallelized automated model-building, this method has the potential to extend the applicability of the SAD method in challenging cases.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Comparison of substructure completion algorithms.
Figure 2: Application of substructure completion algorithms to CysZ data sets merged from varying numbers of crystals.
Figure 3: Map correlation after structure determination.

Similar content being viewed by others

Accession codes

Accessions

Protein Data Bank

References

  1. Berman, H.M. et al. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  Google Scholar 

  2. Hendrickson, W.A. & Teeter, M.M. Nature 290, 107–113 (1981).

    Article  CAS  Google Scholar 

  3. Wang, B.-C. Methods Enzymol. 115, 90–112 (1985).

    Article  CAS  Google Scholar 

  4. Weeks, C.M., DeTitta, G.T., Miller, R. & Hauptman, H.A. Acta Crystallogr. D Biol. Crystallogr. 49, 179–181 (1993).

    Article  CAS  Google Scholar 

  5. Schneider, T.R. & Sheldrick, G.M. Acta Crystallogr. D Biol. Crystallogr. 58, 1772–1779 (2002).

    Article  Google Scholar 

  6. de La Fortelle, E. & Bricogne, G. Methods Enzymol. 276, 472–494 (1997).

    Article  CAS  Google Scholar 

  7. McCoy, A.J., Storoni, L.C. & Read, R.J. Acta Crystallogr. D Biol. Crystallogr. 60, 1220–1228 (2004).

    Article  Google Scholar 

  8. Pannu, N.S. & Read, R.J. Acta Crystallogr. D Biol. Crystallogr. 60, 22–27 (2004).

    Article  Google Scholar 

  9. Langer, G., Cohen, S.X., Lamzin, V.S. & Perrakis, A. Nat. Protoc. 3, 1171–1179 (2008).

    Article  CAS  Google Scholar 

  10. Liu, Q. et al. Science 336, 1033–1037 (2012).

    Article  CAS  Google Scholar 

  11. Skubák, P. & Pannu, N.S. Nat. Commun. 4, 2777 (2013).

    Article  Google Scholar 

  12. Weiss, M.S., Sicker, T. & Hilgenfeld, R. Structure 9, 771–777 (2001).

    Article  CAS  Google Scholar 

  13. Barends, T.R. et al. Nature 505, 244–247 (2014).

    Article  CAS  Google Scholar 

  14. McCoy, A.J. & Read, R.J. Acta Crystallogr. D Biol. Crystallogr. 66, 458–469 (2010).

    Article  CAS  Google Scholar 

  15. Read, R.J. & McCoy, A.J. Acta Crystallogr. D Biol. Crystallogr. 67, 338–344 (2011).

    Article  CAS  Google Scholar 

  16. Grosse-Kunstleve, R.W. & Adams, P.D. Acta Crystallogr. D Biol. Crystallogr. 59, 1966–1973 (2003).

    Article  CAS  Google Scholar 

  17. Adams, P.D. et al. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).

    Article  CAS  Google Scholar 

  18. Sheldrick, G.M. Acta Crystallogr. D Biol. Crystallogr. 66, 479–485 (2010).

    Article  CAS  Google Scholar 

  19. Terwilliger, T.C. Acta Crystallogr. D Biol. Crystallogr. 56, 965–972 (2000).

    Article  CAS  Google Scholar 

  20. Terwilliger, T.C. et al. Acta Crystallogr. D Biol. Crystallogr. 65, 582–601 (2009).

    Article  CAS  Google Scholar 

  21. Terwilliger, T.C. et al. Acta Crystallogr. D Biol. Crystallogr. 64, 61–69 (2008).

    Article  CAS  Google Scholar 

  22. Blanc, E. et al. Acta Crystallogr. D Biol. Crystallogr. 60, 2210–2221 (2004).

    Article  CAS  Google Scholar 

  23. Terwilliger, T.C. Acta Crystallogr. D Biol. Crystallogr. 59, 1174–1182 (2003).

    Article  Google Scholar 

  24. Perrakis, A., Sixma, T.K., Wilson, K.S. & Lamzin, V.S. Acta Crystallogr. D Biol. Crystallogr. 53, 448–455 (1997).

    Article  CAS  Google Scholar 

  25. Read, R.J. Acta Crystallogr. A Found. Adv. 42, 140–149 (1986).

    Article  Google Scholar 

  26. Levin, I. et al. Proteins 59, 864–868 (2005).

    Article  CAS  Google Scholar 

  27. Xu, Q. et al. Proteins 62, 292–296 (2006).

    Article  CAS  Google Scholar 

  28. Kozbial, P. et al. Proteins 71, 1589–1596 (2008).

    Article  CAS  Google Scholar 

  29. Xu, Q. et al. Structure 17, 303–313 (2009).

    Article  CAS  Google Scholar 

  30. Dauter, Z., Wilson, K.S., Sieker, L.C., Meyer, J. & Moulis, J.M. Biochemistry 36, 16065–16073 (1997).

    Article  CAS  Google Scholar 

  31. Xu, Q. et al. Proteins 69, 433–439 (2007).

    Article  CAS  Google Scholar 

  32. Xu, Q. et al. Proteins 74, 1041–1049 (2009).

    Article  CAS  Google Scholar 

  33. Hwang, W.C. et al. Proteins 10.1002/prot.24679 (1 September 2014).

  34. Abendroth, J. et al. J. Struct. Funct. Genomics 12, 83–95 (2011).

    Article  CAS  Google Scholar 

  35. Martinez-Rucobo, F.W., Sainsbury, S., Cheung, A.C. & Cramer, P. EMBO J. 30, 1302–1310 (2011).

    Article  CAS  Google Scholar 

  36. Chattopadhyay, K., Ramagopal, U.A., Brenowitz, M., Nathenson, S.G. & Almo, S.C. Proc. Natl. Acad. Sci. USA 105, 635–640 (2008).

    Article  CAS  Google Scholar 

  37. Cianci, M., Helliwell, J.R. & Suzuki, A. Acta Crystallogr. D Biol. Crystallogr. 64, 1196–1209 (2008).

    Article  CAS  Google Scholar 

  38. Fabian, M.R. et al. Nat. Struct. Mol. Biol. 20, 735–739 (2013).

    Article  CAS  Google Scholar 

  39. Tanaka, Y. et al. RNA 15, 1498–1506 (2009).

    Article  CAS  Google Scholar 

  40. Thomas, S.R., McTamney, P.M., Adler, J.M., LaRonde-LeBlank, N. & Rokita, S.E. J. Biol. Chem. 284, 19659–19667 (2009).

    Article  CAS  Google Scholar 

  41. de Graaff, R.A.G., Hilge, M., van der Plas, J.L. & Abrahams, J.P. Acta Crystallogr. D Biol. Crystallogr. 57, 1857–1862 (2001).

    Article  CAS  Google Scholar 

  42. Winn, M.D. et al. Acta Crystallogr. D Biol. Crystallogr. 67, 235–242 (2011).

    Article  CAS  Google Scholar 

  43. Panjikar, S. et al. Acta Crystallogr. D Biol. Crystallogr. 61, 449–457 (2005).

    Article  Google Scholar 

  44. Wang, J.W. et al. Acta Crystallogr. D Biol. Crystallogr. 60, 1991–1996 (2004).

    Article  CAS  Google Scholar 

  45. Burla, M.C. et al. J. Appl. Cryst. 45, 357–361 (2012).

    Article  CAS  Google Scholar 

  46. Terwilliger, T.C. & Berendzen, J. Acta Crystallogr. D Biol. Crystallogr. 55, 849–861 (1999).

    Article  CAS  Google Scholar 

  47. Emsley, P. & Lohkamp, B. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Q. Liu, W. Henderickson and colleagues for generously providing the CysZ sulfur SAD data sets. Support received from the US National Institutes of Health (grant P01GM063210 to P.D.A., T.C.T. and R.J.R. and grant GM073210 to J.M.H.) and the Wellcome Trust (Principal Research Fellowship to R.J.R., grant 082961/Z/07/Z) is gratefully acknowledged. This work was partially supported by the US Department of Energy under contract DE-AC02-05CH11231.

Author information

Authors and Affiliations

  1. Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK

    Gábor Bunkóczi, Airlie J McCoy & Randy J Read

  2. Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Nathaniel Echols, Ralf W Grosse-Kunstleve, Paul D Adams & James M Holton

  3. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA

    James M Holton

  4. Los Alamos National Laboratory, Los Alamos, New Mexico, USA

    Thomas C Terwilliger

Authors
  1. Gábor Bunkóczi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Airlie J McCoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nathaniel Echols
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ralf W Grosse-Kunstleve
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paul D Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. James M Holton
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Randy J Read
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Thomas C Terwilliger
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.J.R. and T.C.T. led the work; R.J.R. and A.J.M. developed the likelihood-based scoring and LLG maps in Phaser; R.W.G.-K. and P.D.A. developed the HySS framework; J.M.H. developed the synthetic data tests; N.E., T.C.T. and G.B. integrated the likelihood-based methods into HySS; T.C.T. carried out the tests and optimized the Phenix AutoSol and AutoBuild algorithms.

Corresponding authors

Correspondence to Randy J Read or Thomas C Terwilliger.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparison of Phenix with Crank2 structure determination using sites found by Shelxc/d.

Comparison of Phenix and Crank2 structure determination as in Fig. 3c, except that substructure determination was carried out by Shelxc/d5,18 in the Crank2 analysis.

5. Schneider, T. R. & Sheldrick, G. M. (2002). Acta Cryst. D58, 1772–1779.

18. Sheldrick, G. M. (2010). Acta Cryst. (2010). D66, 479–485.

Source data

Supplementary Figure 2 Comparison of Phenix with Crank2 structure determination using sites found with Crunch2.

Comparison of Phenix and Crank2 structure determination as in Fig. 3c, except that substructure determination is carried out by Crunch243 in the Crank2 analysis. Values of map correlation from –0.015 to 0.0 are displayed as zero for clarity.

43. Graaff, R.A. G. de, Hilge, M., van der Plas, J. L. & Abrahams, J. P. (2001). Acta Cryst. D57, 1857-1862.

Source data

Supplementary Figure 3 Application of substructure completion algorithms to a series of challenging synthetic data sets.

All algorithms were tested using default parameters except as noted. SOLVE42 (filled orange circles). HySS dual-space substructure identification16 (filled blue squares), and HySS likelihood-based substructure identification (default procedure, black filled circles, brute-force procedure, open black circles) were carried out in Phenix17, whereas Crunch243 (purple open squares) and Shelxc/d5,18 (1000 tries, blue closed circles and 100,000 tries, green closed circles) substructure determinations were carried out using the Crank2 CCP4i interface41.

5. Schneider, T. R. & Sheldrick, G. M. (2002). Acta Cryst. D58, 1772–1779.

16. Grosse-Kunstleve, R.W & Adams, P.D. (2003). Acta Cryst. D59, 1966–1973.

17. Adams, P. D. et al. (2010). Acta Cryst. D66, 213–221.

18. Sheldrick, G. M. (2010). Acta Cryst. (2010). D66, 479–485.

41. Winn, M.D. et al. (2011). Acta Cryst. D67, 235-242.

42. Terwilliger, T.C. & Berendzen, J. (1999). Acta Cryst. D55, 849-861.

43. Graaff, R.A. G. de, Hilge, M., van der Plas, J. L. & Abrahams, J. P. (2001). Acta Cryst. D57, 1857-1862.

Source data

Supplementary Figure 4 Mean Z scores for trial substructures depending on the number of correct sites.

Scoring was carried out by correlation of structure factor amplitudes (open symbols) or likelihood (closed symbols). Z-scores were averaged for merged datasets with anomalous signal less than or greater than 7.5 as defined in the legend to Fig. 1.

Source data

Supplementary Figure 5 Distribution of likelihood-based scores.

Distribution of likelihood-based scores for merged CysZ dataset based on crystals 2,3 and 7. LLG scores are plotted as a function of the number of sites in the substructure for 3781 trial substructures considered during Phenix brute-force substructure determination. Solutions with 0 or 1 correct sites are indicated with crosses, those with 2–4 correct sites with solid purple circles, and solutions with 19 or more correct sites with solid blue circles (no solutions were obtained with 5–18 correct sites).

Source data

Supplementary Figure 6 Map correlation as function of anomalous signal for SAD data sets from the PDB after parallel autobuilding.

Phenix structure determination was carried out as in Fig. 3b, then the resulting density-modified maps were used as the starting point for parallel model-building as described in the text.

Source data

Supplementary Figure 7 Effects of optimizations on performance of Phenix structure determination.

Each panel contains the same datasets that are shown in Figs. 3a and 3b, and the map correlation to the model-phased electron density map is shown for each dataset and each treatment. (a) Comparison of using only data corrected for anisotropy and sharpened to an effective overall B-value of 10 times the resolution (i.e., if the data extend to 2.5 A the B-value was set to 25 A2) with using these data in parallel with uncorrected data in substructure determination and including substructures obtained with each approach in the analysis. (b) Comparison of using only a dual-space substructure search with using dual-space and likelihood-based searches in parallel. (c) Comparison of not using parameter testing or iteration of substructure searches with using both.

Source data

Supplementary Figure 8 Map correlation after structure determination based on synthetic data sets.

Map correlation is relative to the map based on starting coordinates in the simulation. Filled triangles are map correlations for final maps from Crank2, open squares from Phenix Autosol/Autobuild, closed circles from Phenix Autosol followed by parallel autobuilding and map averaging as described in the text.

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 1374 kb)

Data for Figures 1a, 1b

Spreadsheet containing the data and calculations for Figures 1a and 1b (XLSX 146 kb)

Data for Figures 2a, 2b

 Spreadsheet containing the data and calculations for Figures 2a and 2b. See Supplementary Data 8 for the datasets used in this analysis. (XLSX 58 kb)

Data for Figures 3a,3b,3c Supplemental Figures 1,2,6,7

 Spreadsheet containing the data and calculations for Figures 3a,3b,3c Supplemental Figures 1,2,6,7 (XLSX 142 kb)

Data for Supplemental Figure 3

 Spreadsheet containing the data and calculations for Supplemental Figure 3 (XLSX 57 kb)

Data for Supplemental Figure 4

 Spreadsheet containing the data and calculations for Supplemental Figure 4 (XLSX 56 kb)

Data for Supplemental Figure 5

 Spreadsheet containing the data and calculations for Supplemental Figure 5 (XLSX 215 kb)

Data for Supplemental Figure 8

Spreadsheet containing the data and calculations for Supplemental Figure 8 (XLSX 50 kb)

Merged scaled CysZ datasets used for Figures 2a and 2b

 This file contains scaled, merged data used in Fig. 2 of this paper. See Supplementary Data 2 for details of results of analysis of this data. The original data are available at: http://x4.nsls.bnl.gov/native-SAD/CysZ_native-SAD_individual_plus_merged.tar.bz2. The datasets from Q. Liu and W. Hendrickson have names CysZ_XX_TOMT_scala.sca. These datasets were renamed and rescaled with SOLVE local scaling to yield merged datasets with varying numbers of contributing datasets. The renaming was done in an attempt to sort the datasets based on completeness so that scaling could be easily done starting with dataset “1” and then followed by adding other datasets. The correspondence between the original datasets and renamed/rescaled datasets is given below: XX in CysZ_XX_TOMT_scala.sca rescaled data number 2  1 7  2 3  3 4  4 5  5 6  6 1  7 For example: CysZ_7_TOMT_scala.sca is merged and called merge_2.sca Merge of CysZ_2_TOMT_scala.sca and CysZ_6_TOMT_scala.sca is called merge_16.sca (ZIP 15465 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bunkóczi, G., McCoy, A., Echols, N. et al. Macromolecular X-ray structure determination using weak, single-wavelength anomalous data. Nat Methods 12, 127–130 (2015). https://doi.org/10.1038/nmeth.3212

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.3212

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing