A web‐tool for visualizing quantitative protein–protein interaction data

Quantitative interaction proteomics data can be a challenge to efficiently analyze and subsequently present to an audience in a simple and easy to understand format that still conveys sufficient levels of information. Here we present freely accessible and open‐source web tools for displaying multiple parameters from quantitative protein–protein interaction data sets in a visually intuitive format. Given a set of “bait” proteins with detected “prey” interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, confidence score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data, and present this information to others in an intuitive way.

[1]  Brian Raught,et al.  A PP2A Phosphatase High Density Interaction Network Identifies a Novel Striatin-interacting Phosphatase and Kinase Complex Linked to the Cerebral Cavernous Malformation 3 (CCM3) Protein*S , 2009, Molecular & Cellular Proteomics.

[2]  Tony Pawson,et al.  Protein Interaction Network of the Mammalian Hippo Pathway Reveals Mechanisms of Kinase-Phosphatase Interactions , 2013, Science Signaling.

[3]  Tony Pawson,et al.  Mapping differential interactomes by affinity purification coupled with data independent mass spectrometry acquisition , 2013, Nature Methods.

[4]  G. Superti-Furga,et al.  Stereospecific targeting of MTH1 by (S)-crizotinib as anticancer strategy , 2014, Nature.

[5]  Hyungwon Choi,et al.  SAINT: Probabilistic Scoring of Affinity Purification - Mass Spectrometry Data , 2010, Nature Methods.

[6]  Hyungwon Choi,et al.  Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data , 2010, Molecular systems biology.

[7]  Guomin Liu,et al.  SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software. , 2014, Journal of proteomics.

[8]  R. Aebersold,et al.  Analysis of protein complexes using mass spectrometry , 2007, Nature Reviews Molecular Cell Biology.

[9]  Brian Burke,et al.  A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells , 2012, The Journal of cell biology.

[10]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[11]  Bonnie Berger,et al.  A Quantitative Chaperone Interaction Network Reveals the Architecture of Cellular Protein Homeostasis Pathways , 2014, Cell.

[12]  Amber L. Couzens,et al.  The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data , 2013, Nature Methods.