title: Negative protein-protein interaction datasets derived from large-scale two-hybrid experiments.
abstract: Negative protein-protein interaction datasets are needed for training and evaluation of interaction prediction methods, as well as validation of high-throughput interaction discovery experiments. In large-scale two-hybrid assays, the direct interaction of a large number of protein pairs is systematically probed. We present a simple method to harness two-hybrid data to obtain negative protein-protein interaction datasets, which we validated using other available experimental data. The method identifies interactions that were likely tested but not observed in a two-hybrid screen. For each negative interaction, a confidence score is defined as the shortest-path length between the two proteins in the interaction network derived from the two-hybrid experiment. We show that these high-quality negative datasets are particularly important when a specific biological context is considered, such as in the study of protein interaction specificity. We also illustrate the use of a negative dataset in the evaluation of the InterPreTS interaction prediction method.
authors.affiliation: CellNetworks Cluster of Excellence, University of Heidelberg, Germany.
authors.forename: Leonardo G
authors.initials: LG
authors.lastname: Trabuco
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Betts
authors.forename: Robert B
authors.initials: RB
authors.lastname: Russell
mesh_terms: D000818:Animals; D019540:Area Under Curve; D003198:Computer Simulation; D005069:Evaluation Studies as Topic; D006801:Humans; D008954:Models, Biological; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000592:standards; D060066:Protein Interaction Maps; D012372:ROC Curve; D012015:Reference Standards; D020798:Two-Hybrid System Techniques / Q000592:standards*