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Mol Cell Proteomics. 2015 Dec;14(12):3274-83. doi: 10.1074/mcp.M115.050773. Epub 2015 Oct 23.

Relevance Rank Platform (RRP) for Functional Filtering of High Content Protein-Protein Interaction Data.

Author information

  • 1From the ‡Institute for Molecular Medicine Finland FIMM, University of Helsinki, PO Box 20, FIN-00014 Helsinki, Finland; §Centre for Biotechnology, ‖Faculty of Life Science and Biotechnology, South Asian University, New Delhi 110021, India;
  • 2From the ‡Institute for Molecular Medicine Finland FIMM, University of Helsinki, PO Box 20, FIN-00014 Helsinki, Finland;
  • 3§Centre for Biotechnology.
  • 4**Institute of Biosciences and Medical Technology (BioMediTech), University of Tampere and Tampere University Hospital, FIN-33014, Tampere, Finland.
  • 5From the ‡Institute for Molecular Medicine Finland FIMM, University of Helsinki, PO Box 20, FIN-00014 Helsinki, Finland; §Centre for Biotechnology, ¶Department of Pathology,University of Turku and Åbo Akademi, Turku, Finland, PO Box 123, FIN-20521 Turku, Finland.; jukwes@utu.fi.

Abstract

High content protein interaction screens have revolutionized our understanding of protein complex assembly. However, one of the major challenges in translation of high content protein interaction data is identification of those interactions that are functionally relevant for a particular biological question. To address this challenge, we developed a relevance ranking platform (RRP), which consist of modular functional and bioinformatic filters to provide relevance rank among the interactome proteins. We demonstrate the versatility of RRP to enable a systematic prioritization of the most relevant interaction partners from high content data, highlighted by the analysis of cancer relevant protein interactions for oncoproteins Pin1 and PME-1. We validated the importance of selected interactions by demonstration of PTOV1 and CSKN2B as novel regulators of Pin1 target c-Jun phosphorylation and reveal previously unknown interacting proteins that may mediate PME-1 effects via PP2A-inhibition. The RRP framework is modular and can be modified to answer versatile research problems depending on the nature of the biological question under study. Based on comparison of RRP to other existing filtering tools, the presented data indicate that RRP offers added value especially for the analysis of interacting proteins for which there is no sufficient prior knowledge available. Finally, we encourage the use of RRP in combination with either SAINT or CRAPome computational tools for selecting the candidate interactors that fulfill the both important requirements, functional relevance, and high confidence interaction detection.

© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

PMID:
26499835
[PubMed - in process]
PMCID:
PMC4762622
[Available on 2016-12-01]
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