Evidence map›Paper›PMID 41378110›Full record

ReviewBiophysical reviews2025

Resurgence of magnetic resonance techniques in the era of AlphaFold.

Caitlin E Skinner, Bethany A Haynes, Rivka L Isaacson

Abstract readReview
In one paragraph

Review in Biophysical reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Biophysics in Great Britain and Ireland.Biophysical reviews · 2025
    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.

Caitlin E SkinnerDepartment of Chemistry, King's College London, Britannia House, 7 Trinity Street, London, SE1 1DB UK.
Bethany A HaynesDepartment of Chemistry, King's College London, Britannia House, 7 Trinity Street, London, SE1 1DB UK.
Rivka L IsaacsonDepartment of Chemistry, King's College London, Britannia House, 7 Trinity Street, London, SE1 1DB UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural biology has seen the evolution of multiple pioneering experimental techniques over the last few decades, with leaps in technology and software facilitating rapid solution of crystal structures and the 'resolution revolution' in cryo-electron microscopy. Higher magnetic field strengths have expanded the development of magnetic resonance techniques and their ability to study protein dynamics and conformational diversity. Moreover, decades of experimental data collection and public data deposition combined with modern machine-learning technology have now made it possible to computationally predict three-dimensional protein structures from their amino acid sequence within minutes using AlphaFold (AF), a feat that has inspired a new wave of research. AlphaFold now contributes towards experimental structure solution and provides plausible predictions for structured regions of proteins leaving dynamics and conformational exchange as the next major questions in the field. Nuclear magnetic resonance (NMR) spectroscopy is uniquely placed both to rapidly validate AF predictions and probe protein dynamics at an atomic level in solution. Electron paramagnetic resonance (EPR) spectroscopy can measure distances between specific points in large protein complexes and provide local and global ranges of movement. This review will explore the revival of magnetic resonance techniques in a post-AlphaFold landscape and address their importance in protein research.

Indexed as

AlphaFoldElectron paramagnetic resonanceNuclear magnetic resonance

Identifiers

PMID41378110
PMCPMC12686250

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.