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

doi:10.1038/nmeth.3178

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 journal › Article › peer-review

120 Scopus citations

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 (US)
Pages (from-to)	79-84
Number of pages	6
Journal	Nature Methods
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/nmeth.3178
State	Published - Jan 1 2014
Externally published	Yes

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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10.1038/nmeth.3178

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Kotlyar, M., Pastrello, C., Pivetta, F., Lo Sardo, A., Cumbaa, C., Li, H., Naranian, T., Niu, Y., Ding, Z., Vafaee, F., Broackes-Carter, F., Petschnigg, J., Mills, G. B., Jurisicova, A., Stagljar, I., Maestro, R., & Jurisica, I. (2014). In silico prediction of physical protein interactions and characterization of interactome orphans. Nature Methods, 12(1), 79-84. https://doi.org/10.1038/nmeth.3178

Kotlyar, M, Pastrello, C, Pivetta, F, Lo Sardo, A, Cumbaa, C, Li, H, Naranian, T, Niu, Y, Ding, Z, Vafaee, F, Broackes-Carter, F, Petschnigg, J, Mills, GB, Jurisicova, A, Stagljar, I, Maestro, R & Jurisica, I 2014, 'In silico prediction of physical protein interactions and characterization of interactome orphans', Nature Methods, vol. 12, no. 1, pp. 79-84. https://doi.org/10.1038/nmeth.3178

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title = "In silico prediction of physical protein interactions and characterization of interactome orphans",

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/).",

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

note = "Funding Information: This research was supported by the grants from Genome Canada via the Ontario Genomics Institute, Ontario Research Fund (GL2-01-030, RE-03-020 to I.J.), the Canadian Institutes for Health Research (#99745, #93579 to I.J., A.J.), the Natural Sciences Research Council (#203475 to I.J.), US Army Department of Defense W81XWH-12-1-0501 (to I.J.), the Italian Association for Cancer Research, the Friuli Venezia-Giulia and CRO 5xmille Intramural Grant (to R.M.), the Friuli Venezia-Giulia Exchange Program (to C.P.), the Ontario Genomics Institute (#303547 to I.S.), the Canadian Institutes of Health Research (Catalyst-NHG99091, PPP-125785 to I.S.), the Canadian Cystic Fibrosis Foundation (#300348 to I.S.), the Canadian Cancer Society (2010-700406 to I.S.), Genentech and University Health Network (GL2-01-018 to I.S.), US National Institutes of Health (NIH) PO1/PPG grant 01CA0099031 (to G.B.M., I.J.) and NCI R21 CA126700 (to Z.D., G.B.M.). Computational resources were supported by grants from the Canada Foundation for Innovation (CFI #12301, #203373, #29272, #22540a, #30865) and IBM (to I.J.). I.J. is supported by the Canada Research Chair program. This research was also supported by the University of Toronto McLaughlin Centre and the Ontario Ministry of Health and Long-Term Care (OMOHLTC). The views expressed do not necessarily reflect those of the OMOHLTC. We thank M. Vidal, D. Hill, F. Roth and the Center for Cancer Systems Biology (Dana-Farber Cancer Institute) for prepublication release of protein interaction data, funded by NIH NHGRI grant R01 HG001715.",

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doi = "10.1038/nmeth.3178",

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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

N1 - Funding Information: This research was supported by the grants from Genome Canada via the Ontario Genomics Institute, Ontario Research Fund (GL2-01-030, RE-03-020 to I.J.), the Canadian Institutes for Health Research (#99745, #93579 to I.J., A.J.), the Natural Sciences Research Council (#203475 to I.J.), US Army Department of Defense W81XWH-12-1-0501 (to I.J.), the Italian Association for Cancer Research, the Friuli Venezia-Giulia and CRO 5xmille Intramural Grant (to R.M.), the Friuli Venezia-Giulia Exchange Program (to C.P.), the Ontario Genomics Institute (#303547 to I.S.), the Canadian Institutes of Health Research (Catalyst-NHG99091, PPP-125785 to I.S.), the Canadian Cystic Fibrosis Foundation (#300348 to I.S.), the Canadian Cancer Society (2010-700406 to I.S.), Genentech and University Health Network (GL2-01-018 to I.S.), US National Institutes of Health (NIH) PO1/PPG grant 01CA0099031 (to G.B.M., I.J.) and NCI R21 CA126700 (to Z.D., G.B.M.). Computational resources were supported by grants from the Canada Foundation for Innovation (CFI #12301, #203373, #29272, #22540a, #30865) and IBM (to I.J.). I.J. is supported by the Canada Research Chair program. This research was also supported by the University of Toronto McLaughlin Centre and the Ontario Ministry of Health and Long-Term Care (OMOHLTC). The views expressed do not necessarily reflect those of the OMOHLTC. We thank M. Vidal, D. Hill, F. Roth and the Center for Cancer Systems Biology (Dana-Farber Cancer Institute) for prepublication release of protein interaction data, funded by NIH NHGRI grant R01 HG001715.

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

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