Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

Background: Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results: In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracte...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Alvaro J. González, Li Liao
• Keywords: BMCBI 2010, Computational Method, Singular Value Decomposition, Support Vector Machines