ArticleProteins2015
BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints.
Article in Proteins, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Modeling of Cu(II)-based protein spin labels using rotamer libraries.Physical chemistry chemical physics : PCCP · 2024Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
- Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction.Biophysical journal · 2020Article
- Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures.Structure (London, England : 1993) · 2019Article
- Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data.ACS central science · 2019Article
- Utility of Covalent Labeling Mass Spectrometry Data in Protein Structure Prediction with Rosetta.Journal of chemical theory and computation · 2019Article
- Use of an Isotope-Coded Mass Tag (ICMT) Method To Determine the Orientation of Cholesterol Oxidase on Model Membranes.Biochemistry · 2018Article
- Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.Analytical chemistry · 2018Article
- Integrated Structural Biology for α-Helical Membrane Protein Structure Determination.Structure (London, England : 1993) · 2018Article
- Finding the needle in the haystack: towards solving the protein-folding problem computationally.Critical reviews in biochemistry and molecular biology · 2018Review
- Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities.Journal of chemical theory and computation · 2017Article
- Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.Proteins · 2017Article
- Article
- Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.PloS one · 2017Article
- Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX.Journal of structural biology · 2016Article
- Relative Orientation of POTRA Domains from Cyanobacterial Omp85 Studied by Pulsed EPR Spectroscopy.Biophysical journal · 2016Article
- Protonation-dependent conformational dynamics of the multidrug transporter EmrE.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline.PloS one · 2016Article
- Protein structure prediction guided by crosslinking restraints--A systematic evaluation of the impact of the crosslinking spacer length.Methods (San Diego, Calif.) · 2015Article
- Article
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6 authors.
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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.
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