Negative protein-protein interaction datasets derived from large-scale two-hybrid experiments

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 systemati...

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Main Authors: Trabuco, Leonardo G. (Author) , Betts, Matthew J. (Author) , Russell, Robert B. (Author)
Format: Article (Journal)
Language:English
Published: 4 August 2012
In: Methods
Year: 2012, Volume: 58, Issue: 4, Pages: 343-348
ISSN:1095-9130
DOI:10.1016/j.ymeth.2012.07.028
Online Access:Verlag, Pay-per-use, Volltext: http://dx.doi.org/10.1016/j.ymeth.2012.07.028
Verlag, Pay-per-use, Volltext: http://www.sciencedirect.com/science/article/pii/S1046202312001843
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Author Notes:Leonardo G. Trabuco, Matthew J. Betts, Robert B. Russell

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520 |a 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. 
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