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.

Figure 1

Refinement of 20S proteasome crystal structure into high-resolution cryoEM density. Atomic B-factors obtained from cryoEM model refinement correlate with the deposited X-ray B factors. (A,B) The crystal structure (PDB code: 1PMA) and the cryoEM model refined against the 3.3 Å map. The model is colored by the B-factor in the crystal structure (A), and by the Rosetta real-space B-factor fit to the cryoEM map (B). (C) An example of loop region that reconfigures in the cryoEM model: green, crystal structure; magenta, Rosetta refined model. The independent map density (not used in refinement) is shown.

Figure 2

Dependence of model accuracy on starting model quality and map resolution. For a series of comparative models of 20S, Rosetta and MDFF refinement was initiated from comparative models based on templates indicated on the x-axis. The fraction of Cα atoms within 1 Å of the reference model is indicated on the y-axis for (blue) the starting comparative models, (magenta) the MDFF refined models, and (green) the Rosetta refined models. The templates are arranged from the best starting model to the worst based on the fraction of Cα atoms within 1 Å of the reference model. Sequence identity of the template and RMSD to the reference model is labeled under the PDB ID. Models were refined against 20S maps reconstructed using (A) 120,000 (B) 5,000 (C) 3,000 (D) 1,200 and (E) 1,000 particles, yielding 3.3, 4.1, 4.4, 5.0 and 6.0 Å resolution, respectively. (F) Deviations to the reference model (y-axis) from (black) the starting model based on 1g3k, (cyan) the MDFF refined model and (magenta) the Rosetta refined model for each residue (x-axis). Structure and electron density of the regions highlighted with red arrow is shown for (G) MDFF models and (H) Rosetta models.

Figure 3

Model evaluation using independent maps. For each Rosetta-refined model of 20S at (A) 3.3 Å, (B) 4.1 Å, (C) 4.4 Å, (D) 5.0 Å, and (E) 6.0 Å resolution, the integrated FSC between model and testing map is plotted (y-axis) against the fraction of residues within 1 Å of the reference model (x-axis). More accurate models have higher independent map integrated FSC. (F) Evaluation of (blue) input models, (magenta) MDFF-, and (green) Rosetta-refined models for prgH and fiber based on the independent map integrated FSC. (G) Fiber models based on models (green); the MDFF models also identify the correct threading but with much weaker signal different sequence threading possibilities were refined in (magenta) MDFF and (green) Rosetta. The correct threading is distinguished by the highest integrated FSC in the Rosetta refined (magenta). The integrated FSC between Rosetta models refined in this study and a higher resolution density map available more recently (black) validates the threading identified using Rosetta and the lower resolution map (green). (H,I) Expected phase error (y-axis) correlates with the accuracies of refined models (x-axis). Refinement was carried out with reconstructed maps of 20S proteasome (H) 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 prgH (I) at (red) 4.6 Å, (blue) 5.4 Å and (green) 7.1 Å. The expected phase error tracks absolute model quality better than the integrated FSC (Supplemental Fig. 3).