Abstract
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.
Highlights
Protein-protein interactions (PPIs) play crucial roles in many biological processes in living organisms, such as immune response, enzyme catalysis, and signal transduction
Previous research on PPI site prediction and analysis has been summarized in some recent reviews [3,4,5,6,7,8,9,10]
We developed a novel machine learning approach (contact matrix-interaction profile hidden Markov model (CM-ipHMM)) to predicting interacting residues with the integration of predicted contact matrix prediction for better accuracy
Summary
Protein-protein interactions (PPIs) play crucial roles in many biological processes in living organisms, such as immune response, enzyme catalysis, and signal transduction. Previous research on PPI site prediction and analysis has been summarized in some recent reviews [3,4,5,6,7,8,9,10]. While just knowing the interaction site is good enough for many applications, we further want to know how those interacting residues across the interface between two interacting proteins are paired up because the residue-residue contact information of two interacting proteins can provide further insights into interactions and specific target candidates for mutagenesis. Computational methods that can predict the detailed residue-residue contact information from pure protein sequences have been reported [11, 12]. Fisher scores extracted from ipHMMs of interacting domains were used with support vector machine (SVMs) to predict the contact points in previous research [11]. In Ovchinnikov et al, covariance between residues across the interface is used to predict the residue contact by assigning a so-called GREMLIN score for each residue pairs
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