Predicting PDZ domain–peptide interactions from primary sequences

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Abstract
PDZ domains represent one of the largest families of interaction domains. Chen et al. develop a scoring matrix that enables prediction of peptide–PDZ domain interactions. Unlike previous methods, the model works to some extent for PDZ domains that were not part of the training set. PDZ domains constitute one of the largest families of interaction domains and function by binding the C termini of their target proteins1,2. Using Bayesian estimation, we constructed a three-dimensional extension of a position-specific scoring matrix that predicts to which peptides a PDZ domain will bind, given the primary sequences of the PDZ domain and the peptides. The model, which was trained using interaction data from 82 PDZ domains and 93 peptides encoded in the mouse genome3, successfully predicts interactions involving other mouse PDZ domains, as well as PDZ domains from Drosophila melanogaster and, to a lesser extent, PDZ domains from Caenorhabditis elegans. The model also predicts the differential effects of point mutations in peptide ligands on their PDZ domain–binding affinities. Overall, we show that our approach captures, in a single model, the binding selectivity of the PDZ domain family.