Proteome scanning of PDZ domain interactions using support vector machines

Background

An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will help to increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes.

Results

We built an SVM using mouse and human experimental training data to predict PDZ domain interactions. We showed that it correctly predicts known interactions from proteomes of different organisms and compared to published state of art predictors, is more accurate and precise.

SVM Predictions

SVM predictions were validated using known interactions from PDZBase, a domain peptide interaction database. The number of interactions which corresponded to known protein protein interactions (PPIs) from iRefIndex (BIND, BioGRID, CORUM, DIP, HPRD, IntAct, MINT) was also calculated.

The following are SVM proteome scanning predictions for 13 human, 6 fly and 6 worm PDZ domains with domain-peptide interactions in PDZBase.

The following are SVM proteome scanning predictions for 192 human PDZ domains for which the SVM predicted at least one binder. From this set 75 PDZ domains had prediction which corresponded to PPIs iRefIndex as indicated. Please Supplemental Information for the identities of these domains.

The format of the output is:

Supplementary

Source Code

Team


CategoryProject

Data/PDZProteomeScanning (last edited 2010-06-29 21:07:18 by ShirleyHui)

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