Optimod--an automated approach for constructing and optimizing initial models for single-particle electron microscopy

Abstract

Single-particle cryo-electron microscopy is now well established as a technique for the structural characterization of large macromolecules and macromolecular complexes. The raw data is very noisy and consists of two-dimensional projections, from which the 3D biological object must be reconstructed. The 3D object depends upon knowledge of proper angular orientations assigned to the 2D projection images. Numerous algorithms have been developed for determining relative angular orientations between 2D images, but the transition from 2D to 3D remains challenging and can result in erroneous and conflicting results. Here we describe a general, automated procedure, called OptiMod, for reconstructing and optimizing 3D models using common-lines methodologies. OptiMod approximates orientation angles and reconstructs independent maps from 2D class averages. It then iterates the procedure, while considering each map as a raw solution that needs to be compared with other possible outcomes. We incorporate procedures for 3D alignment, clustering, and refinement to optimize each map, as well as standard scoring metrics to facilitate the selection of the optimal model. We also show that small angle tilt-pair data can be included as one of the scoring metrics to improve the selection of the optimal initial model, and also to provide a validation check. The overall approach is demonstrated using two experimental cryo-EM data sets--the 80S ribosome that represents a relatively straightforward case for ab initio reconstruction, and the Tf-TfR complex that represents a challenging case in that it has previously been shown to provide multiple equally plausible solutions to the initial model problem.

Keywords: Automation; Common-lines; Initial model; Single-particle electron microscopy.

Fig. 1

Schematic of OptiMod: OptiMod is divided into 6 procedures – numbered and shaded regions represent specific algorithmic methods. (0) The first step is pre-processing of the class averages and is optional; options are provided to iteratively align, center, and/or scale the 2D class averages. (1) Multiple raw volumes are iteratively constructed using a common-lines based approach for Euler-angle assignment (see also Supplementary Fig. 1). (2) All raw volumes are aligned to a common scaffold. (3) Pair-wise similarities are calculated between all aligned volumes, enabling clustering into homogeneous groups to produce clustered and averaged volumes. (4) Each clustered and averaged volume is refined against a set of class averages (either from the original input, or separately specified by the user). (5) The refined volumes are assessed using one or several scoring metrics, and the best is selected.

Fig. 2

Application of OptiMod – 80S ribosome: (A) Class averages used as input to OptiMod. (B) The scoring metric in OptiMod compared to the published map as measured by the Fourier shell correlation (0.5 threshold) between each output model and the EMDB structure. Data points for the CCC_PR metric (y-axis) used for selecting the optimal 3D reconstruction are plotted against the Fourier shell correlation between the model and the true solution (x-axis). Representative best, middle, and worst models, according to the final selection metric, are shown in green, orange, and red, respectively. The Pearson correlation coefficient (CC) is −0.82. (C) EMDB model of the 80S eukaryotic ribosome (EMD-1076). (D) 3D reconstructions from the result of procedure 3, and after refinement, procedure 4, are shown for the best, middle, and worst model selections indicated in (B). Scale bars are 250 Å.

Fig. 3

Application of OptiMod – Tf–TfR complex: (A) Class averages used as input to OptiMod. (B) The scoring metric in OptiMod compared to the published map as measured by the Fourier shell correlation (0.5 threshold) between each output model and the PDB structure that has been filtered to 30 Å resolution. Data points for the CCC_PR metric (y-axis) used for selecting the optimal 3D reconstruction are plotted against the Fourier shell correlation between the model and the true solution (x-axis). Representative best, middle, and worst models, according to the final selection metric, are shown in green, orange, and red, respectively. The Pearson correlation coefficient (CC) is −0.64. (C) PDB model of the Tf–TfR complex (PDB 1SUV), low-pass filtered to 30 Å. (D) 3D reconstructions from the result of procedure 3, and after refinement, procedure 4, are shown for the best, middle, and worst model selections indicated in (B), and also after automatic identification of the 2-fold symmetry-axis and refinement using C2 symmetry. Scale bars are 120 Å. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Evaluation of the tilt test in combination with OptiMod: (A–C) Each scoring metric used in OptiMod (y-axis) is plotted against the Fourier shell correlation (0.5 threshold) between the output model and the EMDB structure (x-axis). (A) The ability of the TILTDEV scoring metric, which describes the average deviation from the nominal tilt angle, to assess the optimal solution is displayed for the 80S ribosome and compared with (B) the CCC_PR scoring metric from Fig. 2B. (C) The combination of the two into a single criterion provides a better prediction of the true structure of the 80S ribosome.