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@article{1210371,
author = {Zhang, Zaiyong and Porter, Justin and Tripsianes, Konstantinos and Lange, Oliver F.},
article_location = {Dordrecht},
article_number = {3},
doi = {http://dx.doi.org/10.1007/s10858-014-9832-4},
keywords = {Nuclear magnetic resonance; Automatic data analysis; Structure determination},
language = {eng},
issn = {0925-2738},
journal = {Journal of Biomolecular NMR},
title = {Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta},
url = {http://link.springer.com/article/10.1007%2Fs10858-014-9832-4},
volume = {59},
year = {2014}
}
TY - JOUR
ID - 1210371
AU - Zhang, Zaiyong - Porter, Justin - Tripsianes, Konstantinos - Lange, Oliver F.
PY - 2014
TI - Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta
JF - Journal of Biomolecular NMR
VL - 59
IS - 3
SP - 135-145
EP - 135-145
PB - Springer Netherlands
SN - 09252738
KW - Nuclear magnetic resonance
KW - Automatic data analysis
KW - Structure determination
UR - http://link.springer.com/article/10.1007%2Fs10858-014-9832-4
L2 - http://link.springer.com/article/10.1007%2Fs10858-014-9832-4
N2 - We have developed a novel and robust approach for automatic and unsupervised simultaneous nuclear Overhauser effect (NOE) assignment and structure determination within the CS-Rosetta framework. Starting from unassigned peak lists and chemical shift assignments, autoNOE-Rosetta determines NOE cross-peak assignments and generates structural models. The approach tolerates incomplete and raw NOE peak lists as well as incomplete or partially incorrect chemical shift assignments, and its performance has been tested on 50 protein targets ranging from 50 to 200 residues in size. We find a significantly improved performance compared to established programs, particularly for larger proteins and for NOE data obtained on perdeuterated protein samples. X-ray crystallographic structures allowed comparison of Rosetta and conventional, PDB-deposited, NMR models in 20 of 50 test cases. The unsupervised autoNOE-Rosetta models were often of significantly higher accuracy than the corresponding expert-supervised NMR models deposited in the PDB. We also tested the method with unrefined peak lists and found that performance was nearly as good as for refined peak lists. Finally, demonstrating our method's remarkable robustness against problematic input data, we provided correct models for an incorrect PDB-deposited NMR solution structure.
ER -
ZHANG, Zaiyong, Justin PORTER, Konstantinos TRIPSIANES and Oliver F. LANGE. Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta. \textit{Journal of Biomolecular NMR}. Dordrecht: Springer Netherlands, 2014, vol.~59, No~3, p.~135-145. ISSN~0925-2738. Available from: https://dx.doi.org/10.1007/s10858-014-9832-4.
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