Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.
Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.
Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA.
Tianchuan
T
Du
Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA.
Li
0000-0002-1197-1879
L
Liao
Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA.
Cathy H
CH
Wu
Contact matrix prediction; Interaction site prediction; Machine learning; Protein-protein interaction