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Improvement of cryo-EM maps by density modification

Nature Methods volume 17, pages 923–927 (2020)Cite this article

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Abstract

A density-modification procedure for improving maps from single-particle electron cryogenic microscopy (cryo-EM) is presented. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography. Key differences from applications in crystallography are that the errors in Fourier coefficients are largely in the phases in crystallography but in both phases and amplitudes in cryo-EM, and that half-maps with independent errors are available in cryo-EM. These differences lead to a distinct approach for combination of information from starting maps with information obtained in the density-modification process. The density-modification procedure was applied to a set of 104 datasets and improved map-model correlation and increased the visibility of details in many of the maps. The procedure requires two unmasked half-maps and a sequence file or other source of information on the volume of the macromolecule that has been imaged.

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Fig. 1: Density modification of an apoferritin 3.1-Å map and evaluation using an apoferritin 1.8-Å map.
Fig. 2: Application of density modification to maps from the EMDB.
Fig. 3: Effect of a reconstruction procedure yielding more uniform noise on the outcome of density modification.

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Data availability

The source data for Figs. 1 and 2 are available as Excel worksheets. The spreadsheet and underlying data used to generate the figures in this work and examples of the density-modified maps presented in the figures are available at http://phenix-online.org/phenix_data/terwilliger/denmod_2020/.

References

  1. Nogales, E. The development of cryo-EM into a mainstream structural biology technique. Nat. Methods 13, 24–27 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Marques, M. A., Purdy, M. D. & Yeager, M. CryoEM maps are full of potential. Curr. Opin. Struct. Biol. 58, 214–223 (2019).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  3. Terwilliger, T. C., Adams, P. D., Afonine, P. V. & Sobolev, O. V. Cryo-EM map interpretation and protein model-building using iterative map segmentation. Protein Sci. 29, 87–99 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Wang, B. C. Resolution of phase ambiguity in macromolecular crystallography. Methods Enzymol. 115, 90–112 (1985).

    Article  PubMed  CAS  Google Scholar 

  5. Podjarny, A. D., Rees, B. & Urzhumtsev, A. G. in Crystallographic Methods and Protocols (eds Jones, C., Mulloy, B. & Sanderson, M. R.) 205–226 (Humana Press, 1996).

  6. Cowtan, K. Recent developments in classical density modification. Acta Crystallogr. D. 66, 470–478 (2010).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Terwilliger, T. Maximum-likelihood density modification. Acta Crystallogr. D. 56, 965–972 (2000).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  8. Scheres, S. H. A Bayesian view on cryo-EM structure determination. J. Mol. Biol. 415, 406–418 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Sindelar, C. V. & Grigorieff, N. Optimal noise reduction in 3D reconstructions of single particles using a volume-normalized filter. J. Struct. Biol. 180, 26–38 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Cheng, Y., Grigorieff, N., Penczek, P. A. & Walz, T. A primer to single-particle cryo-electron microscopy. Cell 161, 438–449 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Ramlaul, K., Palmer, C. M. & Aylett, C. H. S. A local agreement filtering algorithm for transmission EM reconstructions. J. Struct. Biol. 205, 30–40 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chen, S. et al. High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy. Ultramicroscopy 135, 24–35 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Cardone, G., Heymann, J. B. & Steven, A. C. One number does not fit all: mapping local variations in resolution in cryo-EM reconstructions. J. Struct. Biol. 184, 226–236 (2013).

    Article  PubMed  Google Scholar 

  14. Spiegel, M., Duraisamy, A. K. & Schröder, G. F. Improving the visualization of cryo-EM density reconstructions. J. Struct. Biol. 191, 207–213 (2015).

    Article  PubMed  CAS  Google Scholar 

  15. Murshudov, G. N. in Methods in Enzymology Vol. 579 (ed. Crowther, R. A.) 277–305 (Academic Press, 2016).

  16. Rosenthal, P. B. & Rubinstein, J. L. Validating maps from single particle electron cryomicroscopy. Curr. Opin. Struct. Biol. 34, 135–144 (2015).

    Article  PubMed  CAS  Google Scholar 

  17. Lawson, C. L. et al. EMDataBank.org: unified data resource for CryoEM. Nucleic Acids Res. 39, D456–D464 (2011).

    Article  PubMed  CAS  Google Scholar 

  18. Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Karplus, P. A. & Diederichs, K. Linking crystallographic model and data quality. Science 336, 1030–1033 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Afanasyev, P. et al. Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin. IUCrJ 4, 678–694 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. van Heel, M. & Schatz, M. Reassessing the revolution’s resolutions. Preprint at https://www.biorxiv.org/content/10.1101/224402v1 (2017).

  23. van Heel, M. & Schatz, M. Fourier shell correlation threshold criteria. J. Struct. Biol. 151, 250–262 (2005).

    Article  PubMed  CAS  Google Scholar 

  24. Urzhumtsev, A., Afonine, P. V., Lunin, V. Y., Terwilliger, T. C. & Adams, P. D. Metrics for comparison of crystallographic maps. Acta Crystallogr. D. 70, 2593–2606 (2014).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  25. Bartesaghi, A. et al. 2.2 Å resolution cryo-EM structure of β-galactosidase in complex with a cell-permeant inhibitor. Science 348, 1147 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Horst, B. G. et al. Allosteric activation of the nitric oxide receptor soluble guanylate cyclase mapped by cryo-electron microscopy. eLife 8, e50634 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Iudin, A., Korir, P. K., Salavert-Torres, J., Kleywegt, G. J. & Patwardhan, A. EMPIAR: a public archive for raw electron microscopy image data. Nat. Methods 13, 387–388 (2016).

    Article  PubMed  CAS  Google Scholar 

  28. Tang, G. et al. EMAN2: An extensible image processing suite for electron microscopy. J. Struct. Biol. 157, 38–46 (2007).

    Article  PubMed  CAS  Google Scholar 

  29. Jakobi, A. J., Wilmanns, M. & Sachse, C. Model-based local density sharpening of cryo-EM maps. eLife 6, e27131 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Terwilliger, T. Improving macromolecular atomic models at moderate resolution by automated iterative model building, statistical density modification and refinement. Acta Crystallogr. D. 59, 1174–1182 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Skubak, P. et al. A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model. IUCrJ 5, 166–171 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Bricogne, G. Geometric sources of redundancy in intensity data and their use for phase determination. Acta Crystallogr. A. 30, 395–405 (1974).

    Article  CAS  Google Scholar 

  33. Abrahams, J. P. & Leslie, A. G. Methods used in the structure determination of bovine mitochondrial F1 ATPase. Acta Crystallogr. D. 52, 30–42 (1996).

    Article  PubMed  CAS  Google Scholar 

  34. Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D. 75, 861–877 (2019).

    Article  CAS  Google Scholar 

  35. Masuda, T., Goto, F., Yoshihara, T. & Mikami, B. The universal mechanism for iron translocation to the ferroxidase site in ferritin, which is mediated by the well conserved transit site. Biochem. Biophys. Res. Commun. 400, 94–99 (2010).

    Article  PubMed  CAS  Google Scholar 

  36. Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D. 74, 531–544 (2018).

    Article  CAS  Google Scholar 

  37. Terwilliger, T. C., Sobolev, O. V., Afonine, P. V. & Adams, P. D. Automated map sharpening by maximization of detail and connectivity. Acta Crystallogr. D. 74, 545–559 (2018).

    Article  CAS  Google Scholar 

  38. Brown, A. et al. Tools for macromolecular model building and refinement into electron cryo-microscopy reconstructions. Acta Crystallogr. D. 71, 136–153 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  39. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  PubMed  CAS  Google Scholar 

  40. Terwilliger, T. Map-likelihood phasing. Acta Crystallogr. D. 57, 1763–1775 (2001).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  41. Terwilliger, T. Reciprocal-space solvent flattening. Acta Crystallogr. D. 55, 1863–1871 (1999).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  42. Scheres, S. H. Processing of structurally heterogeneous Cryo-EM Data in RELION. Methods Enzymol. 579, 125–157 (2016).

    Article  PubMed  CAS  Google Scholar 

  43. Shaikh, T. R., Hegerl, R. & Frank, J. An approach to examining model dependence in EM reconstructions using cross-validation. J. Struct. Biol. 142, 301–310 (2003).

    Article  PubMed  Google Scholar 

  44. Sousa, D. & Grigorieff, N. Ab initio resolution measurement for single particle structures. J. Struct. Biol. 157, 201–210 (2007).

    Article  PubMed  CAS  Google Scholar 

  45. Zhang, K. Y. J., Cowtan, K. & Main, P. Combining constraints for electron-density modification. Meth. Enzymol. 277, 53–64 (1997).

    Article  CAS  Google Scholar 

  46. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D. 66, 12–21 (2010).

    Article  PubMed  CAS  Google Scholar 

  47. Barad, B. A. et al. EMRinger: side chain–directed model and map validation for 3D cryo-electron microscopy. Nat. Methods 12, 943–946 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Park, E. & MacKinnon, R. Structure of the CLC-1 chloride channel from Homo sapiens. eLife 7, e36629 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the National Institutes of Health (grant no. GM063210 to P.D.A., R.J.R. and T.C.T. and grant no. R01-GM080139 to S.J.L.), the Wellcome Trust (grant no. 20947/Z/17/Z to R.J.R.) and the Phenix Industrial Consortium. This work was supported in part by the US Department of Energy under Contract No. DE-AC02-05CH11231 at the Lawrence Berkeley National Laboratory.

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Authors and Affiliations

  1. Los Alamos National Laboratory, Los Alamos, NM, USA

    Thomas C. Terwilliger

  2. New Mexico Consortium, Los Alamos, NM, USA

    Thomas C. Terwilliger

  3. Baylor College of Medicine, Houston, TX, USA

    Steven J. Ludtke

  4. Cambridge Institute for Medical Research, Cambridge, UK

    Randy J. Read

  5. Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Paul D. Adams & Pavel V. Afonine

  6. Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA

    Paul D. Adams

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  5. Pavel V. Afonine
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Contributions

S.J.L. carried out image processing of test datasets to evaluate varying reconstruction procedures. R.J.R. and T.C.T. contributed ideas on the form of errors in cryo-EM. P.V.A. developed tools for the testing infrastructure. T.C.T. developed the software for error analysis. P.D.A. and T.C.T. supervised the work.

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Correspondence to Thomas C. Terwilliger.

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Peer review information Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Terwilliger, T.C., Ludtke, S.J., Read, R.J. et al. Improvement of cryo-EM maps by density modification. Nat Methods 17, 923–927 (2020). https://doi.org/10.1038/s41592-020-0914-9

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