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MAINMASTseg: Automated Map Segmentation Method for Cryo-EM Density Maps with Symmetry
Author(s) -
Genki Terashi,
Yuki Kagaya,
Daisuke Kihara
Publication year - 2020
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.9b01110
Subject(s) - segmentation , software , cryo electron microscopy , graph , symmetry (geometry) , computer science , salient , artificial intelligence , set (abstract data type) , pattern recognition (psychology) , geometry , mathematics , theoretical computer science , physics , nuclear magnetic resonance , programming language
For structural interpretation of cryo-electron microscopy (cryo-EM) density maps that contain multiple chains, map segmentation is an important step. If a map is segmented accurately into regions of individual protein components, the structure of each protein can be separately modeled using an existing modeling tool. Here, we developed new software, MAINMASTseg, for segmenting maps with symmetry. MAINMASTseg is an extension of the MAINMAST de novo cryo-EM protein structure modeling tool, which builds protein structures from a graph structure that captures the distribution of salient density points in the map. MAINMASTseg uses this graph and segments the map by considering symmetry corresponding density points in the graph. We tested MAINMASTseg on a data set of 38 experimentally determined EM density maps. MAINMASTseg successfully identified an individual protein unit for the majority of the maps, which was significantly better than two other popular existing methods, Segger and Phenix. The software is made freely available for academic users at http://kiharalab.org/mainmast_seg.

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