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Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement


We describe a general approach for refining protein structure models on the basis of cryo-electron microscopy maps with near-atomic resolution. The method integrates Monte Carlo sampling with local density-guided optimization, Rosetta all-atom refinement and real-space B-factor fitting. In tests on experimental maps of three different systems with 4.5-Å resolution or better, the method consistently produced models with atomic-level accuracy largely independently of starting-model quality, and it outperformed the molecular dynamics–based MDFF method. Cross-validated model quality statistics correlated with model accuracy over the three test systems.

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Figure 1: Refinement of 20S proteasome crystal structure into high-resolution cryo-EM density.
Figure 2: Dependence of model accuracy on starting-model quality and map resolution.
Figure 3: Model evaluation using independent maps.

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  1. Milazzo, A.C. et al. Initial evaluation of a direct detection device detector for single particle cryo-electron microscopy. J. Struct. Biol. 176, 404–408 (2011).

    Article  Google Scholar 

  2. Li, X. et al. Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM. Nat. Methods 10, 584–590 (2013).

    CAS  Article  Google Scholar 

  3. 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 

  4. Langer, G., Cohen, S.X., Lamzin, V.S. & Perrakis, A. Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7. Nat. Protoc. 3, 1171–1179 (2008).

    CAS  Article  Google Scholar 

  5. Terwilliger, T.C. et al. Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard. Acta Crystallogr. D Biol. Crystallogr. 64, 61–69 (2008).

    CAS  Article  Google Scholar 

  6. Tjioe, E., Lasker, K., Webb, B., Wolfson, H.J. & Sali, A. MultiFit: a web server for fitting multiple protein structures into their electron microscopy density map. Nucleic Acids Res. 39, W167–W170 (2011).

    CAS  Article  Google Scholar 

  7. Woetzel, N., Lindert, S., Stewart, P.L. & Meiler, J. BCL::EM-Fit: rigid body fitting of atomic structures into density maps using geometric hashing and real space refinement. J. Struct. Biol. 175, 264–276 (2011).

    CAS  Article  Google Scholar 

  8. Saha, M. & Morais, M.C. FOLD-EM: automated fold recognition in medium- and low-resolution (4–15 Å) electron density maps. Bioinformatics 28, 3265–3273 (2012).

    CAS  Article  Google Scholar 

  9. Lindert, S. et al. EM-fold: de novo atomic-detail protein structure determination from medium-resolution density maps. Structure 20, 464–478 (2012).

    CAS  Article  Google Scholar 

  10. Baker, M.L., Baker, M.R., Hryc, C.F., Ju, T. & Chiu, W. Gorgon and pathwalking: macromolecular modeling tools for subnanometer resolution density maps. Biopolymers 97, 655–668 (2012).

    CAS  Article  Google Scholar 

  11. Topf, M., Baker, M.L., Marti-Renom, M.A., Chiu, W. & Sali, A. Refinement of protein structures by iterative comparative modeling and cryoEM density fitting. J. Mol. Biol. 357, 1655–1668 (2006).

    CAS  Article  Google Scholar 

  12. DiMaio, F., Tyka, M.D., Baker, M.L., Chiu, W. & Baker, D. Refinement of protein structures into low-resolution density maps using Rosetta. J. Mol. Biol. 392, 181–190 (2009).

    CAS  Article  Google Scholar 

  13. Trabuco, L.G., Villa, E., Mitra, K., Frank, J. & Schulten, K. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 16, 673–683 (2008).

    CAS  Article  Google Scholar 

  14. Song, Y. et al. High-resolution comparative modeling with RosettaCM. Structure 21, 1735–1742 (2013).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  16. Egelman, E.H. et al. Structural plasticity of helical nanotubes based on coiled-coil assemblies. Structure 23, 280–289 (2015).

    CAS  Article  Google Scholar 

  17. Eswar, N. et al. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics 15, 5.6 (2006).

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  19. Davis, I.W. et al. MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res. 35, W375–W383 (2007).

    Article  Google Scholar 

  20. DiMaio, F. et al. Improved low-resolution crystallographic refinement with Phenix and Rosetta. Nat. Methods 10, 1102–1104 (2013).

    CAS  Article  Google Scholar 

  21. Wang, R.Y.-R. et al. De novo protein structure determination from near-atomic-resolution cryo-EM maps. Nat. Methods doi:10.1038/nmeth.3287 (23 February 2015).

  22. Fernández, J.J., Luque, D., Castón, J.R. & Carrascosa, J.L. Sharpening high resolution information in single particle electron cryomicroscopy. J. Struct. Biol. 164, 170–175 (2008).

    Article  Google Scholar 

  23. Henderson, R. et al. Outcome of the first electron microscopy validation task force meeting. Structure 20, 205–214 (2012).

    CAS  Article  Google Scholar 

  24. Scheres, S.H. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    CAS  Article  Google Scholar 

  25. Eswar, N. et al. Comparative protein structure modeling using MODELLER. Curr. Protoc. Protein. Sci. 50, 2.9 (2007).

    Article  Google Scholar 

  26. Trabuco, L.G., Villa, E., Schreiner, E., Harrison, C.B. & Schulten, K. Molecular dynamics flexible fitting: a practical guide to combine cryo-electron microscopy and X-ray crystallography. Methods 49, 174–180 (2009).

    CAS  Article  Google Scholar 

  27. Peng, L.-M., Ren, G., Dudarev, S.L. & Whelan, M.J. Robust parameterization of elastic and absorptive electron atomic scattering factors. Acta Crystallogr. A 52, 257–276 (1996).

    Article  Google Scholar 

  28. Afonine, P.V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D Biol. Crystallogr. 68, 352–367 (2012).

    CAS  Article  Google Scholar 

  29. Radics, J., Königsmaier, L. & Marlovits, T.C. Structure of a pathogenic type 3 secretion system in action. Nat. Struct. Mol. Biol. 21, 82–87 (2014).

    CAS  Article  Google Scholar 

  30. Schraidt, O. & Marlovits, T.C. Three-dimensional model of Salmonella's needle complex at subnanometer resolution. Science 331, 1192–1195 (2011).

    CAS  Article  Google Scholar 

  31. Mindell, J.A. & Grigorieff, N. Accurate determination of local defocus and specimen tilt in electron microscopy. J. Struct. Biol. 142, 334–347 (2003).

    Article  Google Scholar 

  32. Yu, Y. et al. Interactions of PAN's C-termini with archaeal 20S proteasome and implications for the eukaryotic proteasome-ATPase interactions. EMBO J. 29, 692–702 (2010).

    CAS  Article  Google Scholar 

  33. Roseman, A.M. FindEM—a fast, efficient program for automatic selection of particles from electron micrographs. J. Struct. Biol. 145, 91–99 (2004).

    CAS  Article  Google Scholar 

  34. Li, X., Grigorieff, N. & Cheng, Y. GPU-enabled FREALIGN: accelerating single particle 3D reconstruction and refinement in Fourier space on graphics processors. J. Struct. Biol. 172, 407–412 (2010).

    CAS  Article  Google Scholar 

  35. 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).

    CAS  Article  Google Scholar 

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The authors thank K. Laidig and D. Alonso for setting up and maintaining computational resources. This work was supported by the US National Institutes of Health (NIH) grants R01GM092802 (D.B.), R01GM082893 and R01GM098672 (Y.C.), and EB001567 (E.E.).

Author information

Authors and Affiliations



F.D. and Y.S. developed the methods and ran experiments; F.D., Y.S. and D.B. wrote the manuscript. X.L. and Y.C. provided the 20S low-resolution data sets and provided feedback on the method. M.J.B. and T.C.M. analyzed the PrgH data set and provided feedback on the method. C.X., V.C. and E.E. collected the fiber data set and provided feedback on the method. All authors helped in editing the final manuscript.

Corresponding author

Correspondence to David Baker.

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Competing interests

Y.S. is a cofounder of Cyrus Biotechnology, Inc., which will develop and market graphic-interface software for using Rosetta.

Integrated supplementary information

Supplementary Figure 1 Overview of the density-guided rebuilding and refinement protocol.

(a) Flowchart of the protocol. The protocol uses 250 cycles of local backbone rebuilding, followed by five cycles of alternating coordinate and B factor refinement. (b) An inset illustrating the new “density-guided rebuilding” step. A segment with poor fit to density is selected at random. Next, different backbone conformations consistent with the local sequence are first superimposed on the current model, then optimized into the map. Finally, the best fitting backbone conformation is selected, and backbone geometry near the segment is regularized.

Supplementary Figure 2 Model quality as a function of map resolution.

For a given starting model of 20S, we compare refined model accuracy (the fraction of Cα atoms within 1 Å of the reference model on the y-axis) as a function of map resolution for Rosetta-refined models (solid) and MDFF-refined models (dashed). The comparison used (A) 1yar, (B) 1ryp and (C) 1m4y as representatives of easy, medium, and difficult refinement cases, respectively.

Supplementary Figure 3 Identification of model errors using the local density correlation.

For each refined model of 20S, local correlation between each residue and the testing map is calculated. The fraction of residues deviate more than 1Å (black) or 2Å (green) from the reference model (y-axis) is plotted for each local correlation bin (x-axis) at (A) 3.3, (B) 4.1, (C) 4.4, and (D) 6.0Å resolutions.

Supplementary Figure 4 Cross-validation of Rosetta-refined models using FSC.

The FSC (y-axis) is compared to the accuracy of refined models (x-axis). Refinement is carried out with reconstructed maps of (A) 20S proteasome at (magenta) 3.3Å, (cyan) 4.1 Å, (red) 4.4 Å, (blue) 5.0 Å and (green) 6.0 Å, and with maps of (B) prgH needle complex at (red) 4.6 Å, (blue) 5.4 Å and (green) 7.1 Å.

Supplementary Figure 5 An overview of map features necessary for accurate structure determination.

(a) The 20S proteasome dataset at 4.1 Å nicely illustrates the necessary features for structure determination to atomic accuracy. The pitch of helices and individual strands are resolved, and – while sidechains are not uniquely identifiable – density for large sidechains is visible. (b) The same dataset at 6 Å resolution lacks these features: the pitch of helices in not identifiable, and sidechain density for large sidechains is not observed.

Supplementary information

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Supplementary Figures 1–5 and Supplementary Table 1 (PDF 2033 kb)

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DiMaio, F., Song, Y., Li, X. et al. Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement. Nat Methods 12, 361–365 (2015).

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