Evidence map›Paper›PMID 22464875›Full record

ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2012

Efficient acquisition of high-resolution 4-D diagonal-suppressed methyl-methyl NOESY for large proteins.

Jie Wen, Pei Zhou, Jihui Wu

Abstract read
In one paragraph

Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. AMIGO - Guided assignment ofJournal of biomolecular NMR · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jie WenHefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, PR China.
Pei Zhou
Jihui Wu

Funding

Structural, Biochemical and Functional Studies of RNAPII CTD Interacting ProteinsR01GM079376 · NIGMS · DUKE UNIVERSITY · PI ZHOU, PEI · 2008 to 2011
$1.2M
NIGMS NIH HHS GM079376NIGMS NIH HHS R01 GM079376
6 · The paper itself

Abstract

The methyl-methyl NOESY experiment plays an important role in determining the global folds of large proteins. Despite the high sensitivity of this experiment, the analysis of methyl-methyl NOEs is frequently hindered by the limited chemical shift dispersion of methyl groups, particularly methyl protons. This makes it difficult to unambiguously assign all of the methyl-methyl NOE crosspeaks using 3-D spectroscopy. The recent development of sparse sampling methods enables highly efficient acquisition of high-resolution 4-D spectra, which provides an excellent solution to resolving the degeneracy of methyl signals. However, many reconstruction algorithms for processing sparsely-sampled NMR data do not provide adequate suppression of aliasing artifacts in the presence of strong NOE diagonal signals. In order to overcome this limitation, we present a 4-D diagonal-suppressed methyl-methyl NOESY experiment specifically optimized for ultrasparse sampling and evaluate it using a deuterated, ILV methyl-protonated sample of the 42 kDa Escherichia coli maltose binding protein (MBP). Suppression of diagonal signals removes the dynamic range barrier of the methyl-methyl NOESY experiment such that residual aliasing artifacts in the CLEAN-reconstructed high-resolution 4-D spectrum can be further reduced. At an ultrasparse sampling rate of less than 1%, we were able to identify and unambiguously assign the vast majority of expected NOE crosspeaks between methyl groups separated by less than 5 Å and to detect very weak NOE crosspeaks from methyl groups that are over 7 Å apart.

Indexed as

AlgorithmsArtifactsbeta-CyclodextrinsEscherichia coliIndicators and ReagentsMagnetic Resonance SpectroscopyMaltose-Binding ProteinsMethylationProteinsProtonsbeta-CyclodextrinsIndicators and ReagentsMaltose-Binding ProteinsProteinsProtons

Identifiers

PMID22464875
PMCPMC3625671

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.