Format

Send to

Choose Destination
See comment in PubMed Commons below
Nat Methods. 2015 Jan;12(1):79-84. doi: 10.1038/nmeth.3178. Epub 2014 Nov 17.

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

Author information

1
Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.
2
1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Centro Riferimento Oncologico, Istituto Nazionale Tumori, Aviano, Italy.
3
Centro Riferimento Oncologico, Istituto Nazionale Tumori, Aviano, Italy.
4
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.
5
1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Nanjing University of Aeronautics and Astronautics, Nanjing, China.
6
Department of Systems Biology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
7
1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
8
Donnelly Centre, Departments of Molecular Genetics and Biochemistry, University of Toronto, Toronto, Ontario, Canada.
9
1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. [3] Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. [4] TECHNA Institute for the Advancement of Technology for Health, Toronto, Ontario, Canada.

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 ∼ 10% 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/).

PMID:
25402006
DOI:
10.1038/nmeth.3178
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Nature Publishing Group
    Loading ...
    Support Center