title: In silico discovery and experimental validation of new protein-protein interactions.
abstract: We introduce a framework for predicting novel protein-protein interactions (PPIs), based on Fisher's method for combining probabilities of predictions that are based on different data sources, such as the biomedical literature, protein domain and mRNA expression information. Our method compares favorably to our previous method based on text-mining alone and other methods such as STRING. We evaluated our algorithms through the prediction of experimentally found protein interactions underlying Muscular Dystrophy, Huntington's Disease and Polycystic Kidney Disease, which had not yet been recorded in protein-protein interaction databases. We found a 1.74-fold increase in the mean average prediction precision for dysferlin and a 3.09-fold for huntingtin when compared to STRING. The top 10 of predicted interaction partners of huntingtin were analysed in depth. Five were identified previously, and the other five were new potential interaction partners. The full matrix of human protein pairs and their prediction scores are available for download. Our framework can be extended to predict other types of relationships such as proteins in a complex, pathway or related disease mechanisms.
authors.affiliation: LUMC, Human Genetics, Leiden, The Netherlands. hvanhaagen@gmail.com
authors.forename: Herman H H B M
authors.initials: HH
authors.lastname: van Haagen
authors.forename: Peter A C
authors.initials: PA
authors.lastname: 't Hoen
authors.forename: Antoine
authors.initials: A
authors.lastname: de Morrée
authors.forename: Willeke M C
authors.initials: WM
authors.lastname: van Roon-Mom
authors.forename: Dorien J M
authors.initials: DJ
authors.lastname: Peters
authors.forename: Marco
authors.initials: M
authors.lastname: Roos
authors.forename: Barend
authors.initials: B
authors.lastname: Mons
authors.forename: Gert-Jan
authors.initials: GJ
authors.lastname: van Ommen
authors.forename: Martijn J
authors.initials: MJ
authors.lastname: Schuemie
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D004330:Drosophila; D000073939:Dysferlin; D015870:Gene Expression; D006801:Humans; D000071058:Huntingtin Protein; D006816:Huntington Disease / Q000235:genetics / Q000378:metabolism*; D008565:Membrane Proteins / Q000235:genetics / Q000378:metabolism*; D051379:Mice; D058990:Molecular Targeted Therapy; D009124:Muscle Proteins / Q000235:genetics / Q000378:metabolism*; D009136:Muscular Dystrophies / Q000235:genetics / Q000378:metabolism*; D009419:Nerve Tissue Proteins / Q000235:genetics / Q000378:metabolism*; D009687:Nuclear Proteins / Q000235:genetics / Q000378:metabolism*; D007690:Polycystic Kidney Diseases / Q000235:genetics / Q000378:metabolism*; D011237:Predictive Value of Tests; D011336:Probability; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D012333:RNA, Messenger
chemical_list: C114839:DYSF protein, human; D000073939:Dysferlin; C086055:HTT protein, human; D000071058:Huntingtin Protein; D008565:Membrane Proteins; D009124:Muscle Proteins; D009419:Nerve Tissue Proteins; D009687:Nuclear Proteins; D012333:RNA, Messenger