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

Abstract

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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Acknowledgements

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

Authors

Contributions

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). https://doi.org/10.1038/nmeth.3286

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