科研之友
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摘要
Motivation: Proteinprotein interactions have proved to be a valuable starting point for understanding the inner workings of the cell. Computational methodologies have been built which both predict interactions and use interaction datasets in order to predict other protein features. Such methods require gold standard positive (GSP) and negative (GSN) interaction sets. Here we examine and demonstrate the usefulness of homologous interactions in predicting good quality positive and negative interaction datasets.
Results: We generate GSP interaction sets as subsets from experimental data using only interaction and sequence information. We can therefore produce sets for several species (many of which at present have no identified GSPs). Comprehensive error rate testing demonstrates the power of the method. We also show how the use of our datasets significantly improves the predictive power of algorithms for interaction prediction and function prediction.
Furthermore, we generate GSN interaction sets for yeast and examine the use of homology along with other protein properties such as localization, expression and function. Using a novel method to assess the accuracy of a negative interaction set, we find that the best single selector for negative interactions is a lack of co-function. However, an integrated method using all the characteristics shows significant improvement over any current method for identifying GSN interactions. The nature of homologous interactions is also examined and we demonstrate that interologs are found more commonly within species than across species.
Conclusion: GSP sets built using our homologous verification method are demonstrably better than standard sets in terms of predictive ability. We can build such GSP sets for several species. When generating GSNs we show a combination of protein features and lack of homologous interactions gives the highest quality interaction sets.
- 出版日期2008-3-1
