Putting into Practice Domain-Linear Motif Interaction Predictions for Exploration of Protein Networks

PDZ domains recognise short sequence motifs at the extreme C-termini of proteins. A model based on microarray data has been recently published for predicting the binding preferences of PDZ domains to five residue long C-terminal sequences. Here we investigated the potential of this predictor for discovering novel protein interactions that involve PDZ domains. When tested on real negative data assembled from published literature, the predictor displayed a high false positive rate (FPR). We predicted and experimentally validated interactions between four PDZ domains derived from the human proteins MAGI1 and SCRIB and 19 peptides derived from human and viral C-termini of proteins. Measured binding intensities did not correlate with prediction scores, and the high FPR of the predictor was confirmed. Results indicate that limitations of the predictor may arise from an incomplete model definition and improper training of the model. Taking into account these limitations, we identified several novel putative interactions between PDZ domains of MAGI1 and SCRIB and the C-termini of the proteins FZD4, ARHGAP6, NET1, TANC1, GLUT7, MARCH3, MAS, ABC1, DLL1, TMEM215 and CYSLTR2. These proteins are localised to the membrane or suggested to act close to it and are often involved in G protein signalling. Furthermore, we showed that, while extension of minimal interacting domains or peptides toward tandem constructs or longer peptides never suppressed their ability to interact, the measured affinities and inferred specificity patterns often changed significantly. This suggests that if protein fragments interact, the full length proteins are also likely to interact, albeit possibly with altered affinities and specificities. Therefore, predictors dealing with protein fragments are promising tools for discovering protein interaction networks but their application to predict binding preferences within networks may be limited.