Evidence map›Paper›PMID 36697662›Full record

ArticleCommunications chemistry2021

Residue-resolved monitoring of protein hyperpolarization at sub-second time resolution.

Mattia Negroni, Dennis Kurzbach

Abstract read
In one paragraph

Article in Communications chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Hyperpolarized NMR Reveals Low-Populated Folding Intermediates in DNA.Journal of the American Chemical Society · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Nuclear Overhauser spectroscopy in hyperpolarized water - chemicalChemical communications (Cambridge, England) · 2022
    Article
  7. Article
  8. Review
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

2 authors.

Mattia NegroniFaculty of Chemistry, Institute of Biological Chemistry, University Vienna, Währinger Str. 38, 1090, Vienna, Austria.
Dennis KurzbachFaculty of Chemistry, Institute of Biological Chemistry, University Vienna, Währinger Str. 38, 1090, Vienna, Austria. dennis.kurzbach@univie.ac.at.ORCID http://orcid.org/0000-0001-6455-2136

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 801936Österreichische Forschungsförderungsgesellschaft (Austrian Research Promotion Agency) P-33338
6 · The paper itself

Abstract

Signal-enhancement techniques for NMR spectroscopy are important to amplify the weak resonances provided by nuclear spins. Recently, 'hyperpolarization' techniques have been intensively investigated. These provide nuclear spin states far from equilibrium yielding strong signal boosts up to four orders of magnitude. Here we propose a method for real-time NMR of 'hyperpolarized' proteins at residue resolution. The approach is based on dissolution dynamic nuclear polarization (d-DNP), which enables the use of hyperpolarized buffers that selectively boost NMR signals of solvent-exposed protein residues. The resulting spectral sparseness and signal enhancements enable recording of residue-resolved spectra at a 2 Hz sampling rate. Thus, we monitor the hyperpolarization level of different protein residues simultaneously under near-physiological conditions. We aim to address two points: 1) NMR experiments are often performed under conditions that increase sensitivity but are physiologically irrelevant; 2) long signal accumulation impedes fast real-time monitoring. Both limitations are of fundamental relevance to ascertain pharmacological relevance and study protein kinetics.

Identifiers

PMID36697662
PMCPMC9814832

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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.