DomSVR: Domain Boundary Prediction with Support Vector Regression and Evolutionary Information

Protein domains are autonomous folding units and are fundamental structural and functional units of proteins. Protein domain boundaries are helpful to the classification of proteins and understanding the evolutions, structures and functions of proteins. In this paper, we propose a support vector regression based method to locate residues at protein domain boundaries with a combination of evolutionary information including sequence profiles, predicted secondary structures, predicted relative solvent accessibility, and profiles from HSSP items. Our proposed model achieved an average sensitivity of ~37% and an average specificity of ~77% on domain boundary identification on our dataset of multi-domain proteins and showed better predictive performance than previous domain identification models.