An efficient method for validating protein models using electron microscopy data
- Kamal Al Nasr,
- Christopher Jones,
- Bashar Aboona,
- Abdulrahman Alanazi
- Tennessee State University
Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
Related Event
Title
2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Event type
ConferenceDate
12/15/2016 - 12/18/2016Location
ShenzhenChina
Abstract
Cryo-Electron Microscopy is a powerful biophysical technique that is capable of generating 3-dimensional volume images for macromolecular assemblies and machines. De novo protein modeling uses these images to model the biological molecules. In de novo modeling, many candidate structures are generated at intermediate step. The candidates are evaluated conventionally by time-consuming approaches. We introduce an initial version of a geometrical screening method that uses the skeleton of the cryo-EM images to evaluate the candidate structures. A test of ten (10) proteins shows that our method was able to successfully detect good candidates in an efficient way.
Publication Information
Output type
Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
Original language
English (US)Article number
7822778Pages from-to (Number of pages)
Pages 1726-1731 (6 pages)Publication milestones
- Published - 01/17/2017
Publication status
Published - 01/17/2017
Publisher
Institute of Electrical and Electronics Engineers Inc.Publication series
- Publication series name: Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
ISBN (Electronic)
9781509016105Publication IDs
- Scopus: 85013236833
Host publication title
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016Host publication editors
- Kevin Burrage
- Qian Zhu
- Yunlong Liu
- Tianhai Tian
- Yadong Wang
- Xiaohua Tony Hu
- Qinghua Jiang
- Jiangning Song
- Shinichi Morishita
- Kevin Burrage
- Guohua Wang
Publication metrics
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0.75
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1
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2
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Funding Details
This work is funded by NSF grant: HBCU-UP RIA 1600919.
FunderFunding number
NSF
HBCU-UP RIA 1600919
