Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions

Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA. <>
Journal of Structural Biology (Impact Factor: 3.23). 03/2010; 170(3):427-38. DOI: 10.1016/j.jsb.2010.03.007
Source: PubMed


Cryo-electron microscopy produces 3D density maps of molecular machines, which consist of various molecular components such as proteins and RNA. Segmentation of individual components in such maps is a challenging task, and is mostly accomplished interactively. We present an approach based on the immersive watershed method and grouping of the resulting regions using progressively smoothed maps. The method requires only three parameters: the segmentation threshold, a smoothing step size, and the number of smoothing steps. We first apply the method to maps generated from molecular structures and use a quantitative metric to measure the segmentation accuracy. The method does not attain perfect accuracy, however it produces single or small groups of regions that roughly match individual proteins or subunits. We also present two methods for fitting of structures into density maps, based on aligning the structures with single regions or small groups of regions. The first method aligns centers and principal axes, whereas the second aligns centers and then rotates the structure to find the best fit. We describe both interactive and automated ways of using these two methods. Finally, we show segmentation and fitting results for several experimentally-obtained density maps.

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Available from: Junjie Zhang, Oct 06, 2015
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    • "Reconstructions were visualised using UCSF Chimera (Pettersen et al., 2004) The threshold for contouring the isosurface was based on the calculated molecular weight of the T = 3 and T = 4 VLP structures unless stated otherwise. Segmentation was performed in UCSF Chimera using the Segger plugin (Pintilie et al., 2010). "
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    • "There are a number of automated segmentation methods available , which are predominantly based on watershed (Pintilie et al., 2010; Volkmann, 2002) or fast-marching algorithms (Bajaj et al., 2003; Zhang et al., 2012). However, the accuracy of these methods depends on various factors including the resolution, parameters inherent to the segmentations methods as well as manual intervention in specific cases (Pintilie et al., 2010). Unguided manual segmentation is time-consuming but can be more accurate when carried out iteratively. "
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    • "The cryo-EM map segmentation was performed using the Segger (29) plug-in in UCSF Chimera (30). Models of human eRF1 and eRF3 were built based on several experimental structures. "
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