A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)

R. Greg Stacey, Michael A. Skinnider, Nichollas E. Scott, Leonard J. Foster

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Background: An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Results: Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. Conclusions: PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.

Original languageEnglish (US)
Article number457
JournalBMC bioinformatics
Issue number1
StatePublished - Oct 23 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Molecular Biology
  • Structural Biology
  • Biochemistry
  • Computer Science Applications


  • Co-elution
  • Co-fractionation
  • Data analysis
  • Interactome
  • Protein correlation profiling
  • Protein-protein interaction
  • Proteomics
  • Software
  • System biology


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