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A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps.
MedLine Citation:
PMID:  22696406     Owner:  NLM     Status:  In-Data-Review    
The accuracy of the secondary structure element (SSE) identification from volumetric protein density maps is critical for de-novo backbone structure derivation in electron cryo-microscopy (cryoEM). It is still challenging to detect the SSE automatically and accurately from the density maps at medium resolutions (∼5-10 Å). We present a machine learning approach, SSELearner, to automatically identify helices and β-sheets by using the knowledge from existing volumetric maps in the Electron Microscopy Data Bank. We tested our approach using 10 simulated density maps. The averaged specificity and sensitivity for the helix detection are 94.9% and 95.8%, respectively, and those for the β-sheet detection are 86.7% and 96.4%, respectively. We have developed a secondary structure annotator, SSID, to predict the helices and β-strands from the backbone Cα trace. With the help of SSID, we tested our SSELearner using 13 experimentally derived cryo-EM density maps. The machine learning approach shows the specificity and sensitivity of 91.8% and 74.5%, respectively, for the helix detection and 85.2% and 86.5% respectively for the β-sheet detection in cryoEM maps of Electron Microscopy Data Bank. The reduced detection accuracy reveals the challenges in SSE detection when the cryoEM maps are used instead of the simulated maps. Our results suggest that it is effective to use one cryoEM map for learning to detect the SSE in another cryoEM map of similar quality. © 2012 Wiley Periodicals, Inc. Biopolymers 97: 698-708, 2012.
Dong Si; Shuiwang Ji; Kamal Al Nasr; Jing He
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biopolymers     Volume:  97     ISSN:  0006-3525     ISO Abbreviation:  Biopolymers     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-06-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0372525     Medline TA:  Biopolymers     Country:  United States    
Other Details:
Languages:  eng     Pagination:  698-708     Citation Subset:  IM    
Copyright Information:
Copyright © 2012 Wiley Periodicals, Inc.
Department of Computer Science, Old Dominion University, Norfolk, VA 23529.
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