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

• Published in: Proteins, structure, function, and bioinformatics Vol. 83; no. 11; pp. 1947 - 1962
• Main Authors: Fischer, Axel W., Alexander, Nathan S., Woetzel, Nils, Karakas, Mert, Weiner, Brian E., Meiler, Jens
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.11.2015; Wiley Subscription Services, Inc; Wiley
• Subjects: Algorithms; Biochemistry & Molecular Biology; Biophysics; Crystallography; de novo folding; Electron Spin Resonance Spectroscopy; limited experimental data; Magnetic Resonance Spectroscopy; membrane proteins; Membrane Proteins - chemistry; Membrane Proteins - metabolism; Membranes; Models, Molecular; NMR; Nuclear magnetic resonance; Protein Conformation; Protein Folding; protein structure determination; Proteins; Spectroscopy; Topology; membrane proteins; de novo folding; proteins; protein structure determination; limited experimental data
• ISSN: 0887-3585; 1097-0134; 1097-0134
• DOI: 10.1002/prot.24801

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