Identification of Secondary Structure Elements in Intermediate-Resolution Density Maps

Abstract

An increasing number of structural studies of large macromolecular complexes, both in X-ray crystallography and cryo-electron microscopy, have resulted in intermediate-resolution (5-10 A) density maps. Despite being limited in resolution, significant structural and functional information may be extractable from these maps. To aid in the analysis and annotation of these complexes, we have developed SSEhunter, a tool for the quantitative detection of alpha helices and beta sheets. Based on density skeletonization, local geometry calculations, and a template-based search, SSEhunter has been tested and validated on a variety of simulated and authentic subnanometer-resolution density maps. The result is a robust, user-friendly approach that allows users to quickly visualize, assess, and annotate intermediate-resolution density maps. Beyond secondary structure element identification, the skeletonization algorithm in SSEhunter provides secondary structure topology, which is potentially useful in leading to structural models of individual molecular components directly from the density.

Subnanometer-resolution electron cryomicroscopy-based domain models for the cytoplasmic region of skeletal muscle RyR channel

“…At subnanometer resolution, secondary structure elements can be determined by using the feature detection program SSEHunter (9). Our earlier analysis of the TM region revealed the positions of the porelining helix and the pore helix (4), which bore a remarkable similarity to those of the MthK channel (10).…”

Achievable resolution from images of biological specimens acquired from a 4k×4k CCD camera in a 300-kV electron cryomicroscope

“…The segmentation for the ε 15 bacteriophage subunit was performed with Chimera (Goddard et al, 2005), which was also used for the surface representations of the cryo-EM density maps. The secondary structure element assignment from the segmented subunit was made using SSEhunter (Baker et al, 2007).…”

“…Cylindrical densities representing α helices could be observed directly from the 3D density map (Figure 6a), which confirms that the resolution is definitely in the subnanometer range. In this map, the segmented subunit (Figure 7a) shows not only seven α helices (in green) and two β sheets (in cyan) (Figure 7b) as quantified by SSEHunter (Baker et al, 2007), but also connectivity in the densities of the N-arm and the long E-loop regions (Figure 7a). All of the observed structural features in this map match well with a higher resolution (4.5Å) map of ε 15 with image data recorded on photographic film in the same electron cryomicroscope (Jiang et al, 2008) …”

Shape modeling and matching in identifying protein structure from low-resolution images

“…Instead, we first consider an intermediate step towards this goal; which is the locating of secondary structures, helices in particular, in the density volume. Progress has been made in the biology community for detecting positions, orientations and lengths of possible helices in a density volume [Jiang et al 2001;Baker et al 2007] based on their cylindrical density distributions (an example is shown in Figure 1 (c)). What is missing however, is the knowledge of which helix detected in the volume corresponds to a given helix in the sequence.…”

“…As in the sequence graph, the edge attribute function β C returns a 2-tuple, where β C,1 assumes H or L indicating a helix or link edge, and β C,2 returns the length information. For a helix edge {x, y} ∈ E C , β C,2 (x, y) is the Euclidean length of the detected helix in the density volume, which can be normalized by the resolution of the volume to approximate the number of amino acids in the helix [Baker et al 2007]. An example of such edges are shown in green 1 in Figure 3 (c) representing the helices detected in the density volume in (a).…”