A semi-supervised boosting SVM for predicting hot spots at protein-protein interfaces.

ABSTRACT:

Background

Hot spots are residues contributing the most of binding free energy yet accounting for a small portion of a protein interface. Experimental approaches to identify hot spots such as alanine scanning mutagenesis are expensive and time-consuming, while computational methods are emerging as effective alternatives to experimental approaches.

Results

In this study, we propose a semi-supervised boosting SVM, which is called sbSVM, to computationally predict hot spots at protein-protein interfaces by combining protein sequence and structure features. Here, feature selection is performed using random forests to avoid over-fitting. Due to the deficiency of positive samples, our approach samples useful unlabeled data iteratively to boost the performance of hot spots predicti

SUBMITTER: Xu B 

PROVIDER: S-EPMC3521187 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature