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J Mol Biol. 2011 Feb 4;405(5):1295-310. doi: 10.1016/j.jmb.2010.11.025. Epub 2010 Dec 3.

iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions.

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  • 1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.


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 © 2010 Elsevier Ltd. All rights reserved.

[PubMed - indexed for MEDLINE]
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