Evidence map›Paper›PMID 20053573›Full record

ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2010

A practical implementation of cross-spectrum in protein backbone resonance assignment.

Kang Chen, Frank Delaglio, Nico Tjandra

Abstract read
In one paragraph

Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

6 citing papers in PubMed.

  1. Article
  2. Covariance nuclear magnetic resonance methods for obtaining protein assignments and novel correlations.Concepts in magnetic resonance. Part A, Bridging education and research · 2017
    Article
  3. Article
  4. Article
  5. Article
  6. Article
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.

Kang ChenLaboratory of Molecular Biophysics, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Frank Delaglio
Nico Tjandra

Funding

Regulation of mitochondria morphologyZIAHL006064 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI TJANDRA, NICO · 2010 to 2025
$20.9M
Nmr Studies Of The Regulation Of Cell SignalingZIAHL001048 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI TJANDRA, NICO · 2009 to 2025
$20.1M
Intramural NIH HHS Z99 HL999999Intramural NIH HHS ZIA HL001048
6 · The paper itself

Abstract

The concept of cross-spectrum is applied in protein NMR spectroscopy to assist in the backbone sequential resonance assignment. Cross-spectrum analysis is used routinely to reveal correlations in frequency domains as a means to reveal common features contained in multiple time series. Here the cross-spectrum between related NMR spectra, for example HNCO and HN(CA)CO, can be calculated with point-by-point multiplications along their common C' carbon axis. In the resulting higher order cross-spectrum, an enhanced correlation signal occurs at every common i-1 carbon frequency allowing the amide proton H(N) (and nitrogen N) resonances from residues i and i-1 to be identified. The cross-spectrum approach is demonstrated using 2D spectra H(N)CO, H(NCA)CO, H(NCO)CACB, and H(N)CACB measured on a 15N/13C double-labeled Ubiquitin sample. These 2D spectra are used to calculate two pseudo-3D cross-spectra, H(i)-H(i)(-1)-C'(i)(-1) and H(i)-H(i)(-1)-CA(i)(-1)CB(i)(-1). We show using this approach, backbone resonances of H, C', CA, and CB can be fully assigned without ambiguity. The cross-spectrum principle is expected to offer an easy, practical, and more quantitative approach for heteronuclear backbone resonance assignment.

Indexed as

AlgorithmsAmino Acid SequenceMagnetic Resonance SpectroscopyMolecular Sequence DataPeptide MappingProteinsProteins

Identifiers

PMID20053573
PMCPMC2843802

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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.