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 language | English |
|---|---|
| Pages (from-to) | 79-84 |
| Number of pages | 6 |
| Journal | Nature Methods |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 1 2014 |
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ASJC Scopus subject areas
- Biotechnology
- Molecular Biology
- Biochemistry
- Cell Biology
- Medicine(all)
Cite this
In silico prediction of physical protein interactions and characterization of interactome orphans. / Kotlyar, Max; Pastrello, Chiara; Pivetta, Flavia; Lo Sardo, Alessandra; Cumbaa, Christian; Li, Han; Naranian, Taline; Niu, Yun; Ding, Zhiyong; Vafaee, Fatemeh; Broackes-Carter, Fiona; Petschnigg, Julia; Mills, Gordon B.; Jurisicova, Andrea; Stagljar, Igor; Maestro, Roberta; Jurisica, Igor.
In: Nature Methods, Vol. 12, No. 1, 01.01.2014, p. 79-84.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - In silico prediction of physical protein interactions and characterization of interactome orphans
AU - Kotlyar, Max
AU - Pastrello, Chiara
AU - Pivetta, Flavia
AU - Lo Sardo, Alessandra
AU - Cumbaa, Christian
AU - Li, Han
AU - Naranian, Taline
AU - Niu, Yun
AU - Ding, Zhiyong
AU - Vafaee, Fatemeh
AU - Broackes-Carter, Fiona
AU - Petschnigg, Julia
AU - Mills, Gordon B.
AU - Jurisicova, Andrea
AU - Stagljar, Igor
AU - Maestro, Roberta
AU - Jurisica, Igor
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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/).
AB - 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/).
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U2 - 10.1038/nmeth.3178
DO - 10.1038/nmeth.3178
M3 - Article
VL - 12
SP - 79
EP - 84
JO - PLoS Medicine
JF - PLoS Medicine
SN - 1549-1277
IS - 1
ER -