Evidence map›Paper›PMID 27794609›Full record

ArticleJournal of proteome research2016

Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using Limited Proteolysis.

Fang Liu, Michael C Fitzgerald

Abstract read
In one paragraph

Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

23 citing papers in PubMed, 43 citations in OpenAlex.

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  4. Analysis of Limited Proteolysis-Coupled Mass Spectrometry Data.Molecular & cellular proteomics : MCP · 2025
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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

2 authors at 1 institution in 1 country.

Fang LiuDepartment of Chemistry, Duke University , Durham, North Carolina 27708, United States.
Michael C FitzgeraldDepartment of Chemistry, Duke University , Durham, North Carolina 27708, United States.
Duke University · US

Funding

Analysis of Protein-Ligand Binding on the Proteomic ScaleR01GM084174 · NIGMS · DUKE UNIVERSITY · PI FITZGERALD, MICHAEL C · 2009 to 2017
$2.6M
NIGMS NIH HHS R01 GM084174
6 · The paper itself

Abstract

Conformational changes in proteins can lead to disease. Thus, methods for identifying conformational changes in proteins can further improve our understanding and facilitate detection of disease states. Here we combine limited proteolysis (LiP) with Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) to characterize breast cancer-related conformational changes in proteins on the proteomic scale. Studied here are the conformational properties of proteins in two cell culture models of breast cancer, including the MCF-10A and MCF-7 cell lines. The SILAC-LiP approach described here identified ∼200 proteins with cell-line-dependent conformational changes, as determined by their differential susceptibility to proteolytic digestion using the nonspecific protease, proteinase K. The protease susceptibility profiles of the proteins in these cell lines were compared to thermodynamic stability and expression level profiles previously generated for proteins in these same breast cancer cell lines. The comparisons revealed that there was little overlap between the proteins with protease susceptibility changes and the proteins with thermodynamic stability and/or expression level changes. Thus, the large-scale conformational analysis described here provides unique insight into the molecular basis of the breast cancer phenotypes in this study.

Indexed as

Breast NeoplasmsCell Line, TumorFemaleHumansMCF-7 CellsNeoplasm ProteinsPeptide HydrolasesProtein ConformationProtein StabilityProteolysisNeoplasm ProteinsPeptide Hydrolasesbreast cancerlimited proteolysismass spectrometryMCF-10AMCF-7protein foldingproteomicsSILAC

Identifiers

PMID27794609
PMCPMC5270647
OpenAlexW2543245986

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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.