Alignment of PPI Networks Using Semantic Similarity for Conserved Protein Complex Prediction

Abstract

Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components between species and predicts unknown protein complexes and signaling pathways. In this article, we present a novel approach of local network alignment by semantic mapping. While most previous methods find protein matches between species by sequence homology, our approach uses semantic similarity. Given Gene Ontology (GO) and its annotation data, we estimate functional closeness between two proteins by measuring their semantic similarity. We adopted a new semantic similarity measure, simVICD, which has the best performance for PPI validation and functional match. We tested alignment between the PPI networks of well-studied yeast protein complexes and the genome-wide PPI network of human in order to predict human protein complexes. The experimental results demonstrate that our approach has higher accuracy in protein complex prediction than graph clustering algorithms, and higher efficiency than previous network alignment algorithms.

Gene network module changes associated with the vertebrate fin to limb transition

“…In network analysis, the degree of a gene (the number of interactions of the gene) is often used as an important metric [10,24]. Genes with higher degrees in a module, i.e., hub genes, are considered more important because they have more interactions with other module genes and removal of such a gene from the module may significantly affect the integrity of the module [23].…”

“…It has been hypothesized that gradual modular changes occur in evolution while maintaining the basic modular structure; this is because dramatic changes in gene interactions may destroy the proper function of an organism [7]. In support of this hypothesis, conserved genes are observed to play an important role in maintaining the stability of the gene modules during evolution [7,8,10]. The recruitment and the removal of other genes and the rewiring of biological pathways are often held together by the conserved genes.…”

“…Gene module analysis has become common in bioinformatics, and the concept of modular evolution has emerged to explain the changes in groups of genes rather than a single gene when studying the evolution of organisms [5][6][7]. However, most of these studies have focused on smaller protein complexes (typically containing less than 20 proteins) that determine molecular functions and biological pathways [8][9][10]. Phenotypes, such as fin and limb development, are resulted by a large number of proteins having diverse molecular functions and belonging to several biological pathways.…”

“…When considering the evolution of functional modules, most studies have focused on identifying the genes that are retained during evolution, i.e., conserved genes, and their organization in the respective modules [8,10]. It has been hypothesized that gradual modular changes occur in evolution while maintaining the basic modular structure; this is because dramatic changes in gene interactions may destroy the proper function of an organism [7].…”

Alignment of Protein Interaction Networks and Disease Prediction: A Survey

“…Similarity between two graph structures can be identified by evaluating their topology [6]. This process can be referred as network alignment.…”