Premium
Prediction of Protein Structure Using Surface Accessibility Data
Author(s) -
Hartlmüller Christoph,
Göbl Christoph,
Madl Tobias
Publication year - 2016
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.201604788
Subject(s) - folding (dsp implementation) , protein structure prediction , surface (topology) , measure (data warehouse) , convergence (economics) , protein folding , paramagnetism , surface protein , computer science , protein structure , biological system , relaxation (psychology) , chemistry , data mining , physics , biology , mathematics , engineering , biochemistry , economic growth , electrical engineering , economics , geometry , quantum mechanics , virology , neuroscience
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.