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An accurate classification of native and non-native protein-protein interactions using supervised and semi-supervised learning approaches

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4 Author(s)
Nan Zhao ; Dept. of Comput. Sci., Univ. of Missouri, Columbia, MO, USA ; Bin Pang ; Chi-Ren Shyu ; Korkin, D.

The progress in experimental and computational structural biology has led to a rapid growth of experimentally resolved structures and computational models of protein-protein interactions. However, distinguishing between the physiological and non-physiological interactions remains a challenging problem. In this work, two related problems of interface classification have been addressed. The first problem is concerned with classification of the physiological and crystal-packing interactions. The second problem deals with the classification of the physiological interactions, or their accurate models, and decoys obtained from the inaccurate docking models. We have defined a universal set of interface features and employed supervised and semi-supervised learning approaches to accurately classify the interactions in both problems. Furthermore, we formulated the second problem as a semi-supervised learning problem and employed a transductive SVM to improve the accuracy of classification. Finally, we showed that using the scoring functions from the obtained classifiers, one can improve the accuracy of the docking methods.

Published in:

Bioinformatics and Biomedicine (BIBM), 2010 IEEE International Conference on

Date of Conference:

18-21 Dec. 2010

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