Predicting the disulfide bonding state of cysteines with combinations of kernel machines

Cysteines may form covalent bonds, known as disulfide bridges, that have an important role in stabilizing the native conformation of proteins. Several methods have been proposed for predicting the bonding state of cysteines, either using local context or using global protein descriptors. In this paper we introduce an SVM based predictor that operates in two stages. The first stage is a multi-class classifier that operates at the protein level, using either standard Gaussian or spectrum kernels. The second stage is a binary classifier that refines the prediction by exploiting local context enriched with evolutionary information in the form of multiple alignment profiles. At both stages, we enriched profile encoding with information about cysteine conservation. The prediction accuracy of the system is 85% measured by 5-fold cross validation, on a set of 716 proteins from the September 2001 PDB Select dataset.