Evidence map›Paper›PMID 40778633›Full record

ArticleChemistry (Weinheim an der Bergstrasse, Germany)2025

Deep Learning for Hyperpolarized NMR of Intrinsically Disordered Proteins without Resolution Loss: Access to Short-Lived Intermediates.

Ertan Turhan, Christopher Pötzl, Dörte Brandis, Federico Faglia, Milan Zachrdla, Dennis Kurzbach

Abstract read
In one paragraph

Article in Chemistry (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Hyperpolarized NMR Reveals Low-Populated Folding Intermediates in DNA.Journal of the American Chemical Society · 2025
    Article
  4. 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

6 authors.

Ertan TurhanFaculty of Chemistry, Institute of Biological Chemistry, University of Vienna, Währinger Str. 38, Vienna, 1090, Austria.
Christopher PötzlFaculty of Chemistry, Institute of Biological Chemistry, University of Vienna, Währinger Str. 38, Vienna, 1090, Austria.
Dörte BrandisFaculty of Chemistry, Institute of Biological Chemistry, University of Vienna, Währinger Str. 38, Vienna, 1090, Austria.
Federico FagliaFaculty of Chemistry, Institute of Biological Chemistry, University of Vienna, Währinger Str. 38, Vienna, 1090, Austria.
Milan ZachrdlaFaculty of Chemistry, Institute of Biological Chemistry, University of Vienna, Währinger Str. 38, Vienna, 1090, Austria.
Dennis KurzbachFaculty of Chemistry, Institute of Biological Chemistry, University of Vienna, Währinger Str. 38, Vienna, 1090, Austria.ORCID https://orcid.org/0000-0001-6455-2136

Funding

Österreichische Forschungsgemeinschaft I-5771N
6 · The paper itself

Abstract

The inherently low sensitivity of solution-state Nuclear Magnetic Resonance (NMR) has long limited its ability to characterize transient biomolecular states at atomic resolution. While dissolution dynamic nuclear polarization (dDNP) offers a compensating signal enhancement, its broader use has been hindered by rapid polarization decay, causing severe spectral distortion. Here, we introduce HyperW-Decon, an approach that enables high-sensitivity, high-resolution NMR of biomolecules in solution. HyperW-Decon combines two key aspects: (i) the use of hyperpolarized water (HyperW) to transfer polarization to proteins through rapid proton exchange; and (ii) a theory-driven, machine learning (ML)-based deconvolution method that corrects polarization-induced artifacts without requiring external reference signals. This approach is based on a first-principles understanding of dDNP line shapes and delivers a scalable solution to spectral distortion. Applied to intrinsically disordered proteins (IDPs) involved in biomineralization, HyperW-Decon reveals previously inaccessible, short-lived ion-peptide encounter complexes with residue resolution.

Indexed as

Deep LearningIntrinsically Disordered ProteinsNuclear Magnetic Resonance, BiomolecularMagnetic Resonance SpectroscopyWaterIntrinsically Disordered ProteinsWaterbiomineralizationdissolution DNPhyperpolarized NMRmachine learningresolution enhancement

Identifiers

PMID40778633
PMCPMC12548504

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.