Template-based prediction of protein 8-state secondary structures

Accurately predicting protein secondary structures is important to many protein structure modeling applications. In this paper, we investigate a template-based approach to enhance 8-state secondary structure prediction accuracy. The rationale is to construct structural templates from known protein structures with certain sequence similarity. The information contained in templates is then incorporated as features with sequence, evolutionary, and heuristic information to train neural networks. Our computational results show that templates containing structural information are effective features to enhance 8-state secondary structure prediction. A 7-fold cross-validated Q8 score of 78.85% is obtained.