Evidence map›Paper›PMID 35957530›Full record

ArticleBiophysical journal2022

Comparative evaluation of spin-label modeling methods for protein structural studies.

Maxx H Tessmer, Elizabeth R Canarie, Stefan Stoll

Abstract read
In one paragraph

Article in Biophysical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Protein Modeling with DEER Spectroscopy.Annual review of biophysics · 2025
    Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Modeling of Cu(II)-based protein spin labels using rotamer libraries.Physical chemistry chemical physics : PCCP · 2024
    Article
  11. Long-distance tmFRET using bipyridyl- and phenanthroline-based ligands.bioRxiv : the preprint server for biology · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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.

Maxx H TessmerDepartment of Chemistry, University of Washington, Seattle, Washington.
Elizabeth R CanarieDepartment of Chemistry, University of Washington, Seattle, Washington.
Stefan StollDepartment of Chemistry, University of Washington, Seattle, Washington. Electronic address: stst@uw.edu.

Funding

TRAINING IN MOLECULAR BIOPHYSICST32GM008268 · NIGMS · UNIVERSITY OF WASHINGTON · PI KOLLMAN, JUSTIN M, ZHENG, NING · 1988 to 2023
$8.6M
Equipment Supplement: Analysis tools for quantifying protein conformational landscapes using DEER spectroscopyR01GM125753 · NIGMS · UNIVERSITY OF WASHINGTON · PI STOLL, STEFAN · 2019 to 2022
$1.3M
High-sensitivity upgrade of DEER/EPR spectrometerS10OD021557 · OD · UNIVERSITY OF WASHINGTON · PI STOLL, STEFAN · 2017 to 2017
$495k
NIGMS NIH HHS R01 GM125753NIGMS NIH HHS T32 GM008268NIH HHS S10 OD021557
6 · The paper itself

Abstract

Site-directed spin-labeling electron paramagnetic resonance spectroscopy is a powerful technique for the investigation of protein structure and dynamics. Accurate spin-label modeling methods are essential to make full quantitative use of site-directed spin-labeling electron paramagnetic resonance data for protein modeling and model validation. Using a set of double electron-electron resonance data from seven different site pairs on maltodextrin/maltose-binding protein under two different conditions using five different spin labels, we compare the ability of two widely used spin-label modeling methods, based on accessible volume sampling and rotamer libraries, to predict experimental distance distributions. We present a spin-label modeling approach inspired by canonical side-chain modeling methods and compare modeling accuracy with the established methods.

Indexed as

Spin LabelsElectron Spin Resonance SpectroscopyMaltose-Binding ProteinsMaltose-Binding ProteinsSpin Labels

Identifiers

PMID35957530
PMCPMC9515001

What OpenQuestion holds

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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.