Dataset Information

Identifying inter-residue resonances in crowded 2D (13)C- (13)C chemical shift correlation spectra of membrane proteins by solid-state MAS NMR difference spectroscopy.

ABSTRACT: The feasibility of using difference spectroscopy, i.e. subtraction of two correlation spectra at different mixing times, for substantially enhanced resolution in crowded two-dimensional (13)C-(13)C chemical shift correlation spectra is presented. With the analyses of (13)C-(13)C spin diffusion in simple spin systems, difference spectroscopy is proposed to partially separate the spin diffusion resonances of relatively short intra-residue distances from the longer inter-residue distances, leading to a better identification of the inter-residue resonances. Here solid-state magic-angle-spinning NMR spectra of the full length M2 protein embedded in synthetic lipid bilayers have been used to illustrate the resolution enhancement in the difference spectra. The integral membrane M2 protein of Influenza A virus assembles as a tetrameric bundle to form a proton-conducting channel that is activated by low pH and is essential for the viral lifecycle. Based on known amino acid resonance assignments from amino acid specific labeled samples of truncated M2 sequences or from time-consuming 3D experiments of uniformly labeled samples, some inter-residue resonances of the full length M2 protein can be identified in the difference spectra of uniformly (13)C labeled protein that are consistent with the high resolution structure of the M2 (22-62) protein (Sharma et al., Science 330(6003):509-512, 2010).

SUBMITTER: Miao Y

PROVIDER: S-EPMC3717563 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Identifying inter-residue resonances in crowded 2D (13)C- (13)C chemical shift correlation spectra of membrane proteins by solid-state MAS NMR difference spectroscopy.

Miao Yimin Y Cross Timothy A TA Fu Riqiang R

Journal of biomolecular NMR 20130525 3

The feasibility of using difference spectroscopy, i.e. subtraction of two correlation spectra at different mixing times, for substantially enhanced resolution in crowded two-dimensional (13)C-(13)C chemical shift correlation spectra is presented. With the analyses of (13)C-(13)C spin diffusion in simple spin systems, difference spectroscopy is proposed to partially separate the spin diffusion resonances of relatively short intra-residue distances from the longer inter-residue distances, leading ...[more]

PMID: 23708936

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Project description:BackgroundProtein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure.In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past.ResultsWe initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å.After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å.Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server.ConclusionsThe methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints.

Project description:Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

Dataset Information

Identifying inter-residue resonances in crowded 2D (13)C- (13)C chemical shift correlation spectra of membrane proteins by solid-state MAS NMR difference spectroscopy.

Publications

Identifying inter-residue resonances in crowded 2D (13)C- (13)C chemical shift correlation spectra of membrane proteins by solid-state MAS NMR difference spectroscopy.

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