title: SemiHS: an iterative semi-supervised approach for predicting protein-protein interaction hot spots.
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 alanine-mutated 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.
authors.affiliation: Department of Computer Science and Technology, Tongji University, Shanghai, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Deng
authors.forename: Ji-Hong
authors.initials: JH
authors.lastname: Guan
authors.forename: Qi-Wen
authors.initials: QW
authors.lastname: Dong
authors.forename: Shui-Geng
authors.initials: SG
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D030562:Databases, Protein; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins