In silico prediction of physical protein interactions and characterization of interactome orphans
| Autor: | Andrea Jurisicova, Christian A. Cumbaa, Han Li, Zhiyong Ding, Julia Petschnigg, Igor Jurisica, Gordon B. Mills, Fiona Broackes-Carter, Taline Naranian, Chiara Pastrello, Fatemeh Vafaee, Yun Niu, Igor Stagljar, Roberta Maestro, Alessandra Lo Sardo, Flavia Pivetta, Max Kotlyar |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
False discovery rate
Protein function Proteome In silico Computational Biology Cell Biology Computational biology Biology Biochemistry Interactome Protein–protein interaction Prediction methods Protein Interaction Mapping Data Mining Humans Computer Simulation Tumor Suppressor Protein p53 Molecular Biology Human proteins Software Biotechnology |
| Zdroj: | Nature Methods. 12:79-84 |
| ISSN: | 1548-7105 1548-7091 |
| DOI: | 10.1038/nmeth.3178 |
| Popis: | 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/). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|