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Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Hartlmuller, C; Gobl, C; Madl, T.
Prediction of Protein Structure Using Surface Accessibility Data
ANGEW CHEM INT EDIT. 2016; 55(39): 11970-11974. [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Madl Tobias
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Number of Figures: 3
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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.

Find related publications in this database (Keywords)
CS-Rosetta
NMR spectroscopy
paramagnetic relaxation
protein structure prediction
structural biology
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