title: Predicting PDZ domain-peptide interactions from primary sequences.
abstract: PDZ domains constitute one of the largest families of interaction domains and function by binding the C termini of their target proteins. 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 genome, 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.
authors: Department of Molecular and Cellular Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA.
authors: Jiunn R
authors: JR
authors: Chen
authors: Bryan H
authors: BH
authors: Chang
authors: John E
authors: JE
authors: Allen
authors: Michael A
authors: MA
authors: Stiffler
authors: Gavin
authors: G
authors: MacBeath
mesh_terms: D000818:Animals; D001483:Base Sequence; D017173:Caenorhabditis elegans / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D004331:Drosophila melanogaster / Q000378:metabolism*; D007700:Kinetics; D008024:Ligands; D051379:Mice; D008856:Microscopy, Fluorescence / Q000379:methods; D008968:Molecular Conformation; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry*; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D012372:ROC Curve
chemical_list: D008024:Ligands; D010455:Peptides