SemiHS: An Iterative Semi-Supervised Approach for Predicting Proteinprotein Interaction Hot Spots

Author(s): Ji-Hong Guan, Qi-Wen Dong, Shui-Geng Zhou, Lei Deng.

Journal Name: Protein & Peptide Letters

Volume 18 , Issue 9 , 2011

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Abstract:

Protein-protein interaction hot spots, as revealed by alanine scanning mutagenesis, make dominant contributions to the free energy of binding. Since mutagenesis experiments are expensive and time-consuming, the development of computational methods to identify hot spots is becoming increasingly important. In this study, by using a new combination of sequence, structure and energy features, we propose an iterative semi-supervised algorithm, SemiHS, to incorporate unlabeled data to improve the accuracy of hot spots prediction when sufficient training data is un-available and to overcome the imbalanced data problem. We evaluate the predictive power of SemiHS on a labeled set of 265 alaninemutated interface residues in 17 complexes and a large unlabeled set of 2465 interface residues with 10-fold cross validation, and get an AUC score of 0.85, with a sensitivity of 0.70 and a specificity of 0.87, which are better than those of the existing methods. Moreover, we validate the proposed method by an independent test and obtain encouraging results.

Keywords: protein-protein interaction, hot spots, semi-supervised, SVM, apoptosis, protein engineering, Systematic mutagenesis, protein interfaces, double water exclusion, desolvation energy, Semi-Supervised Learning, SemiHS, jackknife test, Human Growth Hormone, ML methods, mutagenesis, Bayes networkprotein-protein interaction, hot spots, semi-supervised, SVM, apoptosis, protein engineering, Systematic mutagenesis, protein interfaces, double water exclusion, desolvation energy, Semi-Supervised Learning, SemiHS, jackknife test, Human Growth Hormone, ML methods, mutagenesis, Bayes network

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Article Details

VOLUME: 18
ISSUE: 9
Year: 2011
Page: [896 - 905]
Pages: 10
DOI: 10.2174/092986611796011419
Price: $58

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