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SHR Neuro Krebs Kardio Lipid

Hartlmüller, C; Göbl, C; Madl, T.
Prediction of Protein Structure Using Surface Accessibility Data.
Angew Chem Int Ed Engl. 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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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. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
Find related publications in this database (using NLM MeSH Indexing)
Algorithms -
Databases, Protein -
Models, Molecular -
Nuclear Magnetic Resonance, Biomolecular -
Protein Conformation -
Protein Folding -
Proteins - chemistry

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