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Prediction of Protein Structure Using Surface Accessibility Data
Author(s) -
Hartlmüller Christoph,
Göbl Christoph,
Madl Tobias
Publication year - 2016
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201604788
Subject(s) - folding (dsp implementation) , protein structure prediction , surface (topology) , convergence (economics) , measure (data warehouse) , protein folding , paramagnetism , surface protein , protein structure , computer science , relaxation (psychology) , biological system , data mining , chemistry , physics , biology , mathematics , biochemistry , engineering , condensed matter physics , geometry , virology , neuroscience , economic growth , electrical engineering , economics
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.