ArticleProtein science : a publication of the Protein Society2004
Optimal bundling of transmembrane helices using sparse distance constraints.
Article in Protein science : a publication of the Protein Society, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction.Biophysical journal · 2020Article
- A photon-free approach to transmembrane protein structure determination.Journal of molecular biology · 2011Article
- Simplified modeling approach suggests structural mechanisms for constitutive activation of the C5a receptor.Proteins · 2011Article
- Elucidating the higher-order structure of biopolymers by structural probing and mass spectrometry: MS3D.Journal of mass spectrometry : JMS · 2010Article
- Alpha-helical topology prediction and generation of distance restraints in membrane proteins.Biophysical journal · 2008Article
- Structural refinement of membrane proteins by restrained molecular dynamics and solvent accessibility data.Biophysical journal · 2008Article
- Computational prediction of atomic structures of helical membrane proteins aided by EM maps.Biophysical journal · 2007Article
- Structure modeling of all identified G protein-coupled receptors in the human genome.PLoS computational biology · 2006Article
- Dipolar coupling between nitroxide spin labels: the development and application of a tether-in-a-cone model.Biophysical journal · 2006Article
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5 authors.
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Abstract
We present a two-step approach to modeling the transmembrane spanning helical bundles of integral membrane proteins using only sparse distance constraints, such as those derived from chemical cross-linking, dipolar EPR and FRET experiments. In Step 1, using an algorithm, we developed, the conformational space of membrane protein folds matching a set of distance constraints is explored to provide initial structures for local conformational searches. In Step 2, these structures refined against a custom penalty function that incorporates both measures derived from statistical analysis of solved membrane protein structures and distance constraints obtained from experiments. We begin by describing the statistical analysis of the solved membrane protein structures from which the theoretical portion of the penalty function was derived. We then describe the penalty function, and, using a set of six test cases, demonstrate that it is capable of distinguishing helical bundles that are close to the native bundle from those that are far from the native bundle. Finally, using a set of only 27 distance constraints extracted from the literature, we show that our method successfully recovers the structure of dark-adapted rhodopsin to within 3.2 A of the crystal structure.
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