Evidence map›Paper›PMID 37159292›Full record

ReviewAnnual review of biophysics2023

Emerging Time-Resolved X-Ray Diffraction Approaches for Protein Dynamics.

Doeke R Hekstra

Open access · hybridAbstract readReview
In one paragraph

Review in Annual review of biophysics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
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

21 citing papers in PubMed, 40 citations in OpenAlex.

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  17. Scaling and Merging Time-Resolved Laue Data with Variational Inference.bioRxiv : the preprint server for biology · 2024
    Article
  18. Article
  19. Design and fabrication of 3D-printedJournal of applied crystallography · 2024
    Article
  20. 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

1 author at 1 institution in 1 country.

Doeke R HekstraDepartment of Molecular and Cellular Biology and School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA; email: doeke_hekstra@harvard.edu.
Harvard University · US

Funding

Identifying Principles of Protein Mechanics by Applying Force and Observing MotionDP2GM141000 · NIGMS · HARVARD UNIVERSITY · PI HEKSTRA, DOEKE ROMKE · 2020 to 2020
$2.4M
NIGMS NIH HHS DP2 GM141000
6 · The paper itself

Abstract

Proteins guide the flows of information, energy, and matter that make life possible by accelerating transport and chemical reactions, by allosterically modulating these reactions, and by forming dynamic supramolecular assemblies. In these roles, conformational change underlies functional transitions. Time-resolved X-ray diffraction methods characterize these transitions either by directly triggering sequences of functionally important motions or, more broadly, by capturing the motions of which proteins are capable. To date, most successful have been experiments in which conformational change is triggered in light-dependent proteins. In this review, I emphasize emerging techniques that probe the dynamic basis of function in proteins lacking natively light-dependent transitions and speculate about extensions and further possibilities. In addition, I review how the weaker and more distributed signals in these data push the limits of the capabilities of analytical methods. Taken together, these new methods are beginning to establish a powerful paradigm for the study of the physics of protein function.

Indexed as

X-Ray DiffractionMotionelectric fieldexcited statesprotein mechanicsserial femtosecond X-ray crystallographytime-resolved X-ray crystallography

Identifiers

PMID37159292
PMCPMC10687665
OpenAlexW4375948637

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.