Computational Approaches to Elucidating Transient Protein-Protein Interactions, Predicting Receptor-Ligand Pairings

ORIGINAL: IntechOpen

Computational Approaches to Elucidating Transient Protein-Protein Interactions, Predicting Receptor-Ligand Pairings

By Ernesto Iacucci, Samuel Xavier De Souza and Yves Moreau

1. Introduction   

Protein-protein interactions (PPI) are one of the most important biological events which occur in the cell. As PPIs regulate almost all biological processes in the cell, aberrations in PPI may cause severe health problems. One specific area of PPI is receptor-ligand interactions. These interactions are transient yet account for a large part of cell-to-cell communication. As PPI is an important area of research, many groups have proposed methods to make computational predictions of PPI.  The basis of the majority of these methods rely largely on the phylogenetic profile analysis of candidate interactors. These methods determine the similarity of the phylogenetic history of a protein A and its putative protein partner B, examining the most accurate measure of similarity between the phylogenetic histories of A and B in order to predict interaction. As interacting proteins should co-adapt as they are under the same evolutionary pressures, it is self-evident that interacting receptors and ligands should be identifiable by application of the same methodology.   While several methods, described below, make use of phylogenetic information to predict protein-protein interaction (PPI), more contemporary work has been conducted in the area of data fusion and kernel learning. We describe one method [Iacucci et al. 2011] in detail which does both. In this work, the existing line of phylogenetic research is extended by using phylogenetic data to construct a kernel to train a least square support vector machines (LS-SVM) in order to classify candidate receptors and ligands as interacting or noninteracting.    

In this chapter, we discuss the plethora of various methods for determining protein-protein interactions. In addition, we evaluate the application of LS-SVMs to the sub-problem of receptor-ligand interaction prediction.

Fig. 1. The Receptor Ligand Schematic. Schematic of receptor-ligand and protein-protein interaction model. Top image is a representation of in-vivo interaction of proteins, receptors, and ligands while bottom image is the graph representation from which a PPI adjacency matrix may be derived. (Figure published in Iacucci et al. 2010)   

Fig. 2. Phylogenetic Analysis of Proteins

Fig. 3. Work flow of the combined kernel classifier