A feature-based approach to modeling protein–protein interaction hot spots

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dc.contributor.authorCho, Kyu-il-
dc.contributor.authorKim, Dongsup-
dc.contributor.authorLee, Doheon-
dc.date.accessioned2011-09-09T01:04:17Z-
dc.date.available2011-09-09T01:04:17Z-
dc.date.issued2009-05-09-
dc.identifier.citationNucleic Acids Research, Vol.37, No.8en
dc.identifier.issn1362-4962-
dc.identifier.urihttp://hdl.handle.net/10203/25147-
dc.description.abstractIdentifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to n–related interactions, especially n    n interactions.en
dc.description.sponsorshipThis work was supported by the Korean System Biology Program (No. M10309020000-03B5002-00000), the National Research Lab. Program (No. 2006-01508), and the Pioneer Research Program for Converging Technology from MEST through KOSEF. Funding for open access charge: Pioneer Research Program for Converging Technology.en
dc.language.isoen_USen
dc.publisherOxford University Pressen
dc.titleA feature-based approach to modeling protein–protein interaction hot spotsen
dc.typeArticleen
dc.identifier.doi10.1093/nar/gkp132-
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