Domain information based prediction of protein-protein interactions of glucosinolate biosynthesis

Protein-protein interactions (PPIs) are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more and removing PPIs falsely predicted PPIs involved in glucosinolate biosynthesis in Arabidopsis. A symmetric kernel function is proposed according to the approach of feature representation which combines the domain and domain-domain interaction (DDI) information in this paper. The performance of this kernel indicates SVM based PPIs predictor trained with this kernel is highly effective. According to the prediction result, proteins with Arabidopsis Genome Initiative (AGI) numbers AT4G14800 and AT5G54810, AT5G05730 and AT4G18040, AT1G04510 and AT5G05260 are affirmed as interactive among the 237 low level of confidence PPIs pairs. Furthermore, the SVM-based PPIs predictor is used to explore PPIs of AT1G74090 and AT5G07690 both of which are members of the four glucosinolate biosynthesis pathway proteins absent from AtPIN.