PuSH - Publication Server of Helmholtz Zentrum München: Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta.

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

Zhang, Z.* ; Porter, J.* ; Tripsianes, K.* ; Lange, O.F.

Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta.

J. Biomol. NMR 59, 135-145 (2014)

DOI

PMC

Open Access Green as soon as Postprint is submitted to ZB.

Abstract
Metrics
Extra information

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.

Altmetric

Additional Metrics?

[➜Log in]

Edit extra informations Login

Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Automatic Data Analysis ; Nuclear Magnetic Resonance ; Structure Determination; Nmr Structure Determination; Torsion Angle Dynamics; Protein Structures; Cs-rosetta; Algorithm; Program; Information; Quality; Dyana

ISSN (print) / ISBN 0925-2738

e-ISSN 1573-5001

Journal Journal of Biomolecular NMR

Quellenangaben Volume: 59, Issue: 3, Pages: 135-145

Publisher Springer

Publishing Place Dordrecht

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Structural Biology (STB)