Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016, Article ID 2891918, 13 pages
Research Article

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

1Department of Proteomics, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany
2Department of Bioanalytics, University of Applied Sciences and Arts of Coburg, 96450 Coburg, Germany
3Department of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany
4Institute of Clinical Immunology, Medical Faculty, University of Leipzig, 04103 Leipzig, Germany
5Fraunhofer Institute for Cell Therapy and Immunology, 04103 Leipzig, Germany
6Department of Metabolomics, Helmholtz Centre for Environmental Research-UFZ, 04318 Leipzig, Germany
7Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark

Received 30 September 2015; Revised 28 November 2015; Accepted 29 November 2015

Academic Editor: Yudong Cai

Copyright © 2016 Stefan Kalkhof et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.