Evidence map›Paper›PMID 26966684›Full record

ArticleBioMed research international2016

PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data.

Stefan Kalkhof, Stefan Schildbach, Conny Blumert, Friedemann Horn, Martin von Bergen, Dirk Labudde

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 3 institutions in 2 countries.

Stefan KalkhofDepartment of Proteomics, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; Department of Bioanalytics, University of Applied Sciences and Arts of Coburg, 96450 Coburg, Germany.ORCID 0000-0001-6121-7105
Stefan SchildbachDepartment of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.
Conny BlumertInstitute of Clinical Immunology, Medical Faculty, University of Leipzig, 04103 Leipzig, Germany; Fraunhofer Institute for Cell Therapy and Immunology, 04103 Leipzig, Germany.
Friedemann HornInstitute of Clinical Immunology, Medical Faculty, University of Leipzig, 04103 Leipzig, Germany; Fraunhofer Institute for Cell Therapy and Immunology, 04103 Leipzig, Germany.
Martin von BergenDepartment of Proteomics, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; Department of Metabolomics, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany; Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark.
Dirk LabuddeDepartment of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.
Fraunhofer Institute for Cell Therapy and Immunology · DEHelmholtz Centre for Environmental Research · DEHochschule Mittweida · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

Indexed as

SoftwareAmino AcidsHEK293 CellsHumansIsotope LabelingMass SpectrometryPhosphorylationProtein BindingProtein Interaction MappingProteinsProteomicsSTAT1 Transcription FactorAmino AcidsProteinsSTAT1 protein, humanSTAT1 Transcription Factor

Identifiers

PMID26966684
PMCPMC4761381
OpenAlexW2265745671

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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.