Publication details
Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Biomolecular NMR |
| MU Faculty or unit | |
| Citation | |
| web | http://link.springer.com/article/10.1007%2Fs10858-014-9832-4 |
| Doi | http://dx.doi.org/10.1007/s10858-014-9832-4 |
| Field | Biochemistry |
| Keywords | Nuclear magnetic resonance; Automatic data analysis; Structure determination |
| Description | 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. |