Evidence map›Paper›PMID 25975745›Full record

ArticleJournal of biomolecular NMR2015

Sequential backbone assignment based on dipolar amide-to-amide correlation experiments.

ShengQi Xiang, Kristof Grohe, Petra Rovó, Suresh Kumar Vasa, Karin Giller, Stefan Becker, Rasmus Linser

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of biomolecular NMR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Chemical reviews · 2022
    Review
  9. Article
  10. Atomic-resolution chemical characterization of (2x)72-kDa tryptophan synthase via four- and five-dimensionalProceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  11. Article
  12. Solid-state NMR spectroscopy.Nature reviews. Methods primers · 2021
    Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Solid-State NMR H-N-(C)-H and H-N-C-C 3D/4D Correlation Experiments for Resonance Assignment of Large Proteins.Chemphyschem : a European journal of chemical physics and physical chemistry · 2017
    Article
  18. Article
  19. 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

7 authors.

ShengQi XiangDepartment for NMR-Based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
Kristof Grohe
Petra Rovó
Suresh Kumar Vasa
Karin Giller
Stefan Becker
Rasmus Linser

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proton detection in solid-state NMR has seen a tremendous increase in popularity in the last years. New experimental techniques allow to exploit protons as an additional source of information on structure, dynamics, and protein interactions with their surroundings. In addition, sensitivity is mostly improved and ambiguity in assignment experiments reduced. We show here that, in the solid state, sequential amide-to-amide correlations turn out to be an excellent, complementary way to exploit amide shifts for unambiguous backbone assignment. For a general assessment, we compare amide-to-amide experiments with the more common (13)C-shift-based methods. Exploiting efficient CP magnetization transfers rather than less efficient INEPT periods, our results suggest that the approach is very feasible for solid-state NMR.

Indexed as

AmidesAnimalsChickensModels, MolecularNuclear Magnetic Resonance, BiomolecularProtein ConformationProteinsSpectrinAmidesProteinsSpectrin

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.