title: Predicting PDZ domain mediated protein interactions from structure.
abstract: BACKGROUND
  PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.
  
  
  RESULTS
  We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training-testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.
  
  
  CONCLUSIONS
  We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training-testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW.
authors: The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
authors: Shirley
authors: S
authors: Hui
authors: Xiang
authors: X
authors: Xing
authors: Gary D
authors: GD
authors: Bader
mesh_terms: D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D054731:PDZ Domains; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D020543:Proteome / Q000737:chemistry / Q000378:metabolism; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D008024:Ligands; D010455:Peptides; D020543:Proteome