Author(s): Hosur R, Xu J, Bienkowska J, Berger B
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Abstract Current homology modeling methods for predicting protein-protein interactions (PPIs) have difficulty in the "twilight zone" (<40\%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses only on the protein interface. Our approach combines a novel linear programming formulation for interface alignment 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. Furthermore, by combining our predictions with a full-complex threader, we achieve a 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. Copyright Â© 2010 Elsevier Ltd. All rights reserved.
This article was published in J Mol Biol
and referenced in Journal of Bioequivalence & Bioavailability