Cross-correlation of common lines: A novel approach for single-particle reconstruction of a structure containing a flexible domain

Abstract

We describe a novel approach to sorting class averages of a structure in multiple conformational states in order to generate 3D reconstructions that account for conformational variability present in the sample. The method assumes that the relative Euler angles between class averages are known, then uses a common lines approach to match any given class against a set of distinct conformations from a selected view of the structure. We show the effectiveness of the method both on model data and on an experimental dataset for which the conformational variability is limited to a defined region within the structure. During our studies of hepatitis C virus (HCV) internal ribosome entry site (IRES) interaction with the human translation initiation factor eIF3, we observed that the IRES RNA included a flexible region holding multiple conformations. While current classification methods were used to produce two-dimensional averages of the complex showing these different conformations, no method existed for relating these averages in three dimensions. Our approach overcame these limitations, giving us structural insight that was previously not possible.

Exploring conformational modes of macromolecular assemblies by multiparticle cryo-EM

“…Further, given the discrepancies it should be in principle possible to simultaneously sort out data into homogeneous subsets [29]. In a simpler application of common lines, it is assumed that orientations of projections are known, and discrepancies along common lines are only used for clustering of the 2D data [30]. The approach can be very effective, as in case of demonstration of the “breathing” core of the pyruvate dehydrogenase complex [9].…”

Automated multi-model reconstruction from single-particle electron microscopy data

“…Most are based on supervised methods in which user interaction is needed in order to sort the data into multiple homogeneous subsets (Burgess et al, 2004; Fu et al, 2007; Hall et al, 2007; Penczek et al, 2006; White et al, 2004). For example a user may partition the data based on average density of class-averages (Elad et al, 2008; Penczek et al, 2006), images with larger density represent ligand-bound states and images with lower density represent unbound configurations.…”

“…For example a user may partition the data based on average density of class-averages (Elad et al, 2008; Penczek et al, 2006), images with larger density represent ligand-bound states and images with lower density represent unbound configurations. In another case, supervised classification is performed after a user defines references based on visual inspection (Hall et al, 2007). Obviously, a supervised approach may not work well in all the cases, can be heavily biased by assumptions that the user holds about the data, and may be exceptionally labor intensive.…”

“…Some views show distinct structural differences while in others the structural differences may be small or indistinguishable. In related previous work (Hall et al, 2007), a manual visual inspection of subclass-averages was performed in order to pick a single representative view that shows the highest degree of variability. Here we apply a fully automated approach that considers all subclass-averages simultaneously and combines them into K groups that define K 3D models.…”

“…Here we apply a fully automated approach that considers all subclass-averages simultaneously and combines them into K groups that define K 3D models. In the last, fourth stage, we iteratively run a K -model refinement procedure that reassigns experimental images to the most similar models (Hall et al, 2007; Kostek et al, 2006). …”