Proteome scanning of PDZ domain interactions using support vector machines

Motivation

PDZ domains mediate important biological processes through the recognition of short linear motifs. Two recent independent high through put protein microarray and phage display experiments have been used to detect PDZ domain interactions. Several computational predictors of PDZ domain interactions have also been developed, however they are trained using only protein microarray data or focus on limited subsets of PDZ domains. 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 not only an accurate but precise predictor due to the thousands of possible interactors in a given proteome. However, once validated these predictions would increase the coverage of current PDZ domain interaction networks and further our understanding of the biologically processes they mediate.

Results

We developed a PDZ domain interaction predictor using SVMs trained with both protein microarray and phage display data. In order to use the phage display data for training, we developed a method to deterministically generate artificial negative interactions for the phage display data since it consisted of positive interactions only. Through extensive blind testing we showed that the SVM could predict interactions in different organisms. We then used the SVM to scan the proteomes of different organisms to predict binders for several PDZ domains. Predictions were validated using PDZBase or protein microarray data and a comparison of F1 measures and FPRs between the SVM and published or commonly used predictors demonstrated the SVM’s improved accuracy and precision.

Availability and Implementation

Source code is freely available at URL, implemented in Java.

Team

• Shirley Hui
• Gary Bader

CategoryProject