Nucleic Acids Research Advance Access originally published online on March 9, 2009
Nucleic Acids Research 2009 37(8):2672-2687; doi:10.1093/nar/gkp132
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Nucleic Acids Research, 2009, Vol. 37, No. 8 2672-2687
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
A feature-based approach to modeling protein–protein interaction hot spots
1Department of Bio and Brain Engineering, KAIST, 305-701, Daejeon, South Korea
*To whom correspondence should be addressed. Tel: +82 42 350 5357; Fax: +82 42 350 8680; Email: kds{at}kaist.ac.kr, dhlee{at}biosoft.kaist.ac.kr
Received October 22, 2008. Revised February 2, 2009. Accepted February 17, 2009.
Identifying 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 
–related interactions, especially · · · interactions.
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