The process of resonance assignment represents a time-consuming and potentially error-prone bottleneck in structural studies of proteins by solid-state NMR (ssNMR). Software for the automation of this process is therefore of high interest. Procedures developed through the last decades for solution-state NMR are not directly applicable for ssNMR due to the inherently lower data quality caused by lower sensitivity and broader lines, leading to overlap between peaks. Recently, the first efforts towards procedures specifically aimed for ssNMR have been realized (Schmidt et al. in J Biomol NMR 56(3):243â??254, 2013). Here we present a robust automatic method, which can accurately assign protein resonances using peak lists from a small set of simple 2D and 3D ssNMR experiments, applicable in cases with low sensitivity. The method is demonstrated on three uniformly 13C, 15N labeled biomolecules with different challenges on the assignments. In particular, for the immunoglobulin binding domain B1 of streptococcal protein G automatic assignment shows 100Â*% accuracy for the backbone resonances and 91.8Â*% when including all side chain carbons. It is demonstrated, by using a procedure for generating artificial spectra with increasing line widths, that our method, GAMES_ASSIGN can handle a significant amount of overlapping peaks in the assignment. The impact of including different ssNMR experiments is evaluated as well.
Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta
Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta
Abstract
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...
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06-19-2014 10:21 PM
[NMR paper] Automated robust and accurate assignment of protein resonances for solid state NMR.
Automated robust and accurate assignment of protein resonances for solid state NMR.
Automated robust and accurate assignment of protein resonances for solid state NMR.
J Biomol NMR. 2014 May 10;
Authors: Nielsen JT, Kulminskaya N, Bjerring M, Nielsen NC
Abstract
The process of resonance assignment represents a time-consuming and potentially error-prone bottleneck in structural studies of proteins by solid-state NMR (ssNMR). Software for the automation of this process is therefore of high interest. Procedures developed through...
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05-13-2014 03:11 PM
[NMR paper] Automated solid-state NMR resonance assignment of protein microcrystals and amyloids.
Automated solid-state NMR resonance assignment of protein microcrystals and amyloids.
Related Articles Automated solid-state NMR resonance assignment of protein microcrystals and amyloids.
J Biomol NMR. 2013 May 21;
Authors: Schmidt E, Gath J, Habenstein B, Ravotti F, Székely K, Huber M, Buchner L, Böckmann A, Meier BH, Güntert P
Abstract
Solid-state NMR is an emerging structure determination technique for crystalline and non-crystalline protein assemblies, e.g., amyloids. Resonance assignment constitutes the first and often very...
Automated protein resonance assignments of magic angle spinning solid-state NMR spect
Automated protein resonance assignments of magic angle spinning solid-state NMR spectra of β1 immunoglobulin binding domain of protein G (GB1)
Abstract Magic-angle spinning solid-state NMR (MAS SSNMR) represents a fast developing experimental technique with great potential to provide structural and dynamics information for proteins not amenable to other methods. However, few automated analysis tools are currently available for MAS SSNMR. We present a methodology for automating protein resonance assignments of MAS SSNMR spectral data and its application to experimental peak lists of the...
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10-15-2010 05:16 PM
Robust structure-based resonance assignment for functional protein studies by NMR
Abstract High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment...
Automated robust and accurate assignment of protein resonances for solid state NMR
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