Predicting PDZ domain mediated protein interactions from structure

BackgroundPDZ 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.ResultsWe 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.ConclusionsWe 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 athttp://webservice.baderlab.org/domains/POW.

[1]  Jiunn R Chen,et al.  PDZ Domain Binding Selectivity Is Optimized Across the Mouse Proteome , 2007, Science.

[2]  Katja Luck,et al.  Putting into Practice Domain-Linear Motif Interaction Predictions for Exploration of Protein Networks , 2011, PloS one.

[3]  G. Casey,et al.  Mutated in colorectal cancer, a putative tumor suppressor for serrated colorectal cancer, selectively represses β-catenin-dependent transcription , 2008, Oncogene.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Barry Honig,et al.  Extending the Applicability of the Nonlinear Poisson−Boltzmann Equation: Multiple Dielectric Constants and Multivalent Ions† , 2001 .

[6]  Pascal Benkert,et al.  QMEAN: A comprehensive scoring function for model quality assessment , 2008, Proteins.

[7]  M. Sanner,et al.  Reduced surface: an efficient way to compute molecular surfaces. , 1996, Biopolymers.

[8]  E. Gherardi,et al.  Diverse and potent activities of HGF/SF in skin wound repair , 2004, The Journal of pathology.

[9]  R. Hegde,et al.  Solution structure of the hDlg/SAP97 PDZ2 domain and its mechanism of interaction with HPV-18 papillomavirus E6 protein. , 2007, Biochemistry.

[10]  Sean R. Eddy,et al.  Accelerated Profile HMM Searches , 2011, PLoS Comput. Biol..

[11]  Gavin MacBeath,et al.  Predicting PDZ domain–peptide interactions from primary sequences , 2008, Nature Biotechnology.

[12]  William Stafford Noble,et al.  Large-scale prediction of protein-protein interactions from structures , 2010, BMC Bioinformatics.

[13]  Ian M. Donaldson,et al.  BIND: the Biomolecular Interaction Network Database , 2001, Nucleic Acids Res..

[14]  Alfonso Valencia,et al.  Structure-based prediction of the Saccharomyces cerevisiae SH3-ligand interactions. , 2009, Journal of molecular biology.

[15]  Ignacio E. Sánchez,et al.  Genome-Wide Prediction of SH2 Domain Targets Using Structural Information and the FoldX Algorithm , 2008, PLoS Comput. Biol..

[16]  P. Koolwijk,et al.  Fibrin structure and wound healing , 2006, Journal of thrombosis and haemostasis : JTH.

[17]  Gerhard G. Thallinger,et al.  VASCo: computation and visualization of annotated protein surface contacts , 2009, BMC Bioinformatics.

[18]  Pascale Zimmermann,et al.  Frizzled-PDZ scaffold interactions in the control of Wnt signaling. , 2009, Advances in enzyme regulation.

[19]  Emil Alexov,et al.  Rapid grid‐based construction of the molecular surface and the use of induced surface charge to calculate reaction field energies: Applications to the molecular systems and geometric objects , 2002, J. Comput. Chem..

[20]  Janet M. Thornton,et al.  Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons , 2005, Bioinform..

[21]  B A Stanton,et al.  A PDZ-interacting domain in CFTR is an apical membrane polarization signal. , 1999, The Journal of clinical investigation.

[22]  BMC Bioinformatics , 2005 .

[23]  Livia Perfetto,et al.  MINT, the molecular interaction database: 2012 update , 2011, Nucleic Acids Res..

[24]  Ian M. Donaldson,et al.  iRefIndex: A consolidated protein interaction database with provenance , 2008, BMC Bioinformatics.

[25]  Chris H. Q. Ding,et al.  PSoL: a positive sample only learning algorithm for finding non-coding RNA genes , 2006, Bioinform..

[26]  Gary D Bader,et al.  Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation , 2010, PloS one.

[27]  Chris Sander,et al.  A Specificity Map for the PDZ Domain Family , 2008, PLoS biology.

[28]  T. Pawson,et al.  Assembly of Cell Regulatory Systems Through Protein Interaction Domains , 2003, Science.

[29]  V. Klepeis,et al.  P2Y receptors play a critical role in epithelial cell communication and migration , 2004, Journal of cellular biochemistry.

[30]  Gary D. Bader,et al.  A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain–peptide interaction from primary sequence , 2010, Bioinform..

[31]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[32]  Walter Hunziker,et al.  Convergent and Divergent Ligand Specificity among PDZ Domains of the LAP and Zonula Occludens (ZO) Families* , 2006, Journal of Biological Chemistry.

[33]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[34]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[35]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[36]  Hui Lu,et al.  MeTaDoR: a comprehensive resource for membrane targeting domains and their host proteins , 2007, Bioinform..

[37]  Torsten Schwede,et al.  BIOINFORMATICS Bioinformatics Advance Access published November 12, 2005 The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling , 2022 .

[38]  John D. Westbrook,et al.  The Protein Model Portal , 2008, Journal of Structural and Functional Genomics.

[39]  Ian M. Donaldson,et al.  iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence , 2010, Database J. Biol. Databases Curation.

[40]  M. Goebeler,et al.  Chemokines in cutaneous wound healing , 2001, Journal of leukocyte biology.

[41]  Martin Kuiper,et al.  BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks , 2005, Bioinform..

[42]  Jie Li,et al.  Regenerative phenotype in mice with a point mutation in transforming growth factor β type I receptor (TGFBR1) , 2011, Proceedings of the National Academy of Sciences.

[43]  Jens Meiler,et al.  A physical model for PDZ-domain/peptide interactions , 2010, Journal of molecular modeling.

[44]  Daisuke Kihara,et al.  3D-SURFER: software for high-throughput protein surface comparison and analysis , 2009, Bioinform..

[45]  Charlotte M. Deane,et al.  JOY: protein sequence-structure representation and analysis , 1998, Bioinform..

[46]  Adam J. Smith,et al.  The Database of Interacting Proteins: 2004 update , 2004, Nucleic Acids Res..

[47]  Damian Szklarczyk,et al.  The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored , 2010, Nucleic Acids Res..

[48]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[49]  Jihui Wu,et al.  Solution structure and backbone dynamics of the AF‐6 PDZ domain/Bcr peptide complex , 2007, Protein science : a publication of the Protein Society.

[50]  Olivera Stojadinovic,et al.  PERSPECTIVE ARTICLE: Growth factors and cytokines in wound healing , 2008, Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society.

[51]  R. Hannoush,et al.  Inhibition of Wnt signaling by Dishevelled PDZ peptides. , 2009, Nature chemical biology.

[52]  Kara Dolinski,et al.  The BioGRID Interaction Database: 2011 update , 2010, Nucleic Acids Res..

[53]  Hans-Werner Mewes,et al.  CORUM: the comprehensive resource of mammalian protein complexes , 2007, Nucleic Acids Res..

[54]  Ernest F. Talarico,et al.  Plasma membrane calcium-ATPase isoform four distribution changes during corneal epithelial wound healing , 2010, Molecular vision.

[55]  Sachdev S Sidhu,et al.  Origins of PDZ Domain Ligand Specificity , 2003, The Journal of Biological Chemistry.

[56]  Won Kim,et al.  A machine learning based method for the prediction of G protein-coupled receptor-binding PDZ domain proteins , 2009, Molecules and cells.

[57]  Walter Hunziker,et al.  Comparative Structural Analysis of the Erbin PDZ Domain and the First PDZ Domain of ZO-1 , 2006, Journal of Biological Chemistry.

[58]  R. Dardik,et al.  Role of Coagulation Factor XIII (FXIII) in Angiogenesis and Tissue Repair , 2006, Pathophysiology of Haemostasis and Thrombosis.

[59]  Yang Zhang.  Protein structure prediction: when is it useful? , 2009, Current opinion in structural biology.

[60]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[61]  Jeffrey M Peters,et al.  Xenobiotic metabolism, disposition, and regulation by receptors: from biochemical phenomenon to predictors of major toxicities. , 2011, Toxicological sciences : an official journal of the Society of Toxicology.

[62]  K. Dev,et al.  Making protein interactions druggable: targeting PDZ domains , 2004, Nature Reviews Drug Discovery.

[63]  G. Lagna,et al.  Negative regulation of axis formation and Wnt signaling in Xenopus embryos by the F-box/WD40 protein βTrCP , 1999, Mechanisms of Development.

[64]  Gary D. Bader,et al.  Proteome scanning to predict PDZ domain interactions using support vector machines , 2010, BMC Bioinformatics.

[65]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[66]  Tanja Kortemme,et al.  Structure-based prediction of the peptide sequence space recognized by natural and synthetic PDZ domains. , 2010, Journal of molecular biology.

[67]  L. Cantley,et al.  Recognition of Unique Carboxyl-Terminal Motifs by Distinct PDZ Domains , 1997, Science.

[68]  Wenming Xu,et al.  CD9 is critical for cutaneous wound healing through JNK signaling. , 2012, The Journal of investigative dermatology.

[69]  Andrea Becchetti,et al.  Integrins and ion channels in cell migration: implications for neuronal development, wound healing and metastatic spread. , 2010, Advances in experimental medicine and biology.

[70]  Daniel Fischer,et al.  Servers for protein structure prediction. , 2006, Current opinion in structural biology.

[71]  J. Doorbar,et al.  Molecular biology of human papillomavirus infection and cervical cancer. , 2006, Clinical science.

[72]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[73]  T. Eling,et al.  Xenobiotic metabolism by prostaglandin H synthase. , 1992, Pharmacology & therapeutics.

[74]  R. Colvin,et al.  Role of platelet-derived growth factor in wound healing: synergistic effects with other growth factors. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[75]  angesichts der Corona-Pandemie,et al.  UPDATE , 1973, The Lancet.

[76]  Andrew M. Jenkinson,et al.  Ensembl 2009 , 2008, Nucleic Acids Res..