Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions
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Gossard_Quantitative analysis.pdf
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Author(s)
Pintilie, Grigore D.
Zhang, Junjie
Goddard, Thomas D.
Chiu, Wah
Gossard, David C.
Date Issued
March 2010
Journal
Journal of Structural Biology
Publisher
Elsevier
Citation
Pintilie, Grigore D., Junjie Zhang, Thomas D. Goddard, Wah Chiu, and David C. Gossard. “Quantitative Analysis of Cryo-EM Density Map Segmentation by Watershed and Scale-Space Filtering, and Fitting of Structures by Alignment to Regions.” Journal of Structural Biology 170, no. 3 (June 2010): 427–438.
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Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1016/j.jsb.2010.03.007
