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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

Conference

Date

12/15/2016 - 12/18/2016

Location

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

7822778

Pages 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)

9781509016105

Publication IDs

  • Scopus: 85013236833

Host publication title

Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Host 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

Metrics

Scopus
citations
Fractional count
1
Fractional count
0.25
Fractional count
3
Fractional count
0.75
Fractional count
1
Fractional count
1

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Captures
2
Citation count
2

Funding Details

This work is funded by NSF grant: HBCU-UP RIA 1600919.
FunderFunding number
NSF
HBCU-UP RIA 1600919