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- Title
Prediction of Protein Structure Using Surface Accessibility Data.
- Authors
Hartlmüller, Christoph; Göbl, Christoph; Madl, Tobias
- Abstract
An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance-to-surface information encoded in the sPRE data in the chemical shift-based CS-Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach.
- Subjects
PROTEIN structure; NUCLEAR magnetic resonance spectroscopy; MONTE Carlo method; SAMPLE average approximation method; PROTEIN binding
- Publication
Angewandte Chemie, 2016, Vol 128, Issue 39, p12149
- ISSN
0044-8249
- Publication type
Article
- DOI
10.1002/ange.201604788