In silico prediction of physical protein interactions and characterization of interactome orphans

Max Kotlyar, Chiara Pastrello, Flavia Pivetta, Alessandra Lo Sardo, Christian Cumbaa, Han Li, Taline Naranian, Yun Niu, Zhiyong Ding, Fatemeh Vafaee, Fiona Broackes-Carter, Julia Petschnigg, Gordon B. Mills, Andrea Jurisicova, Igor Stagljar, Roberta Maestro, Igor Jurisica

Research output: Contribution to journalArticlepeer-review

Abstract

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only a 1/410% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/∼juris/data/fpclass/).

Original languageEnglish
Pages (from-to)79-84
Number of pages6
JournalNature Methods
Volume12
Issue number1
DOIs
Publication statusPublished - Jan 1 2014

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Biochemistry
  • Cell Biology
  • Medicine(all)

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