SemiHS: An iterative semi-supervised approach for predicting proteinprotein interaction hot spots

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. © 2011 bentham science publishers ltd.