Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen

PLoS Comput Biol. 2014 Sep 25;10(9):e1003814. doi: 10.1371/journal.pcbi.1003814. eCollection 2014 Sep.

Abstract

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cluster Analysis
  • Databases, Protein
  • Gene Knockdown Techniques
  • Genomics / methods*
  • HeLa Cells
  • Humans
  • Phenotype
  • Protein Interaction Maps
  • Proteins / chemistry
  • Proteins / genetics*
  • Proteins / metabolism*
  • RNA, Small Interfering / chemistry*
  • RNA, Small Interfering / genetics*
  • RNA, Small Interfering / metabolism*
  • ROC Curve

Substances

  • Proteins
  • RNA, Small Interfering

Grant support

We acknowledge funding from the Landesstiftung Baden- Württemberg (RNS/RNAi #P-LS-RNS30); the BMBF (www.bmbf.de) #0313923; #01EO1002; #0316168D, #0316076C, BMBF/NGFN+ NeuroNet 01GS8169-73, ENGINE #01GS0898, the Helmholtz Alliance for the project Systems Biology of Signaling in Cancer (www.helmholtz.de); the Deutscher Akademischer Auslandsdienst (www.daad.de) for funding of AS, DFG (http://www.dfg.de;/SFB740/2-11 and SFB618/3-9), EU (http://europa.eu/index_de.htm; EuroSpin Health-F2-2009-241498 and SynSys Health-F2-2009-242167). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.