Evidence map›Paper›PMID 36802530›Full record

ArticleJournal of the American Society for Mass Spectrometry2023

Comparative Analysis of Protein Folding Stability-Based Profiling Methods for Characterization of Biological Phenotypes.

Morgan A Bailey, Yun Tang, Hye-Jin Park, Michael C Fitzgerald

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. The Effects and Mechanism of ATM Kinase Inhibitors inInternational journal of molecular sciences · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. 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

4 authors.

Morgan A BaileyDepartment of Chemistry, Duke University, Durham, North Carolina 27708, United States.
Yun TangDepartment of Chemistry, Duke University, Durham, North Carolina 27708, United States.ORCID 0000-0002-7479-8268
Hye-Jin ParkDepartment of Chemistry, Duke University, Durham, North Carolina 27708, United States.
Michael C FitzgeraldDepartment of Chemistry, Duke University, Durham, North Carolina 27708, United States.ORCID 0000-0002-6719-4722

Funding

Global Measurements of Protein Folding Stability for Characterization of Aging and DiseaseR01GM134716 · NIGMS · DUKE UNIVERSITY · PI FITZGERALD, MICHAEL C · 2019 to 2022
$1.2M
NIGMS NIH HHS R01 GM134716
6 · The paper itself

Abstract

Recently, a new suite of mass spectrometry-based proteomic methods has been developed that enables evaluation of protein folding stability on the proteomic scale. These methods utilize chemical and thermal denaturation approaches (SPROX and TPP, respectively) as well as proteolysis strategies (DARTS, LiP, and PP) to assess protein folding stability. The analytical capabilities of these technique have been well-established for protein target discovery applications. However, less is known about the relative advantages and disadvantages of using these different strategies to characterize biological phenotypes. Reported here is a comparative study of SPROX, TPP, LiP, and conventional protein expression level measurements using both a mouse model of aging and a mammalian cell culture model of breast cancer. Analyses on proteins in brain tissue cell lysates derived from 1- and 18-month-old mice (

Indexed as

ProteomeProteomicsAnimalsHumansMammalsMCF-7 CellsMicePhenotypeProtein FoldingProteomeagingcancerchemical denaturationproteomicsthermal denaturationthermodynamics

Identifiers

PMID36802530
PMCPMC10164353

What OpenQuestion holds

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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.