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: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
4 August 2012
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| 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 |
| Summary: | 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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| Item Description: | Gesehen am 24.07.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1095-9130 |
| DOI: | 10.1016/j.ymeth.2012.07.028 |