Current homology modeling methods for predicting protein-protein inter- actions (PPIs) have difficulty in the "twilight zone" (<40%) of sequence iden- tities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template com- plex to predict an entire three-dimensional structure. We introduce a thread- ing approach, iWRAP, which focuses on only the protein interface. Our ap- proach combines a novel linear programming formulation for interface align- ment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Further- more, by combining our predictions with a full-complex threader, we achieve coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.CI - Copyright (c) 2010 Elsevier Ltd. All rights reserved.
|Evidence ID||Analyze ID||Interactor||Interactor Systematic Name||Interactor||Interactor Systematic Name||Type||Assay||Annotation||Action||Modification||Phenotype||Source||Reference||Note|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Gene Ontology Term||Gene Ontology Term ID||Qualifier||Aspect||Method||Evidence||Source||Assigned On||Reference||Annotation Extension|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Phenotype||Experiment Type||Experiment Type Category||Mutant Information||Strain Background||Chemical||Details||Reference|
|Evidence ID||Analyze ID||Regulator||Regulator Systematic Name||Target||Target Systematic Name||Experiment||Conditions||Strain||Source||Reference|