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Title: BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints

Abstract

For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data. Proteins 2015; 83:1947–1962. © 2015 Wiley Periodicals, Inc

Authors:
 [1];  [1];  [1];  [2];  [1];  [1]
  1. Department of Chemistry, Vanderbilt University, Nashville Tennessee 37232; Center for Structural Biology, Vanderbilt University, Nashville Tennessee 37232
  2. Center for Structural Biology, Vanderbilt University, Nashville Tennessee 37232
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); UT-Battelle LLC/ORNL, Oak Ridge, TN (Unted States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1565412
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Proteins
Additional Journal Information:
Journal Volume: 83; Journal Issue: 11; Journal ID: ISSN 0887-3585
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
Biochemistry & Molecular Biology; Biophysics

Citation Formats

Fischer, Axel W., Alexander, Nathan S., Woetzel, Nils, Karakas, Mert, Weiner, Brian E., and Meiler, Jens. BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints. United States: N. p., 2015. Web. doi:10.1002/prot.24801.
Fischer, Axel W., Alexander, Nathan S., Woetzel, Nils, Karakas, Mert, Weiner, Brian E., & Meiler, Jens. BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints. United States. doi:10.1002/prot.24801.
Fischer, Axel W., Alexander, Nathan S., Woetzel, Nils, Karakas, Mert, Weiner, Brian E., and Meiler, Jens. Mon . "BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints". United States. doi:10.1002/prot.24801.
@article{osti_1565412,
title = {BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints},
author = {Fischer, Axel W. and Alexander, Nathan S. and Woetzel, Nils and Karakas, Mert and Weiner, Brian E. and Meiler, Jens},
abstractNote = {For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data. Proteins 2015; 83:1947–1962. © 2015 Wiley Periodicals, Inc},
doi = {10.1002/prot.24801},
journal = {Proteins},
issn = {0887-3585},
number = 11,
volume = 83,
place = {United States},
year = {2015},
month = {9}
}

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