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.
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