LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules

Abstract

BackgroundCross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging.ResultsWe present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments.ConclusionThe proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

SAMNA: accurate alignment of multiple biological networks based on simulated annealing

“…As the number of networks has gradually increased, so has the demand for analyzing and discovering hidden information in network data. Modeling known PPIs as network models and analyzing the spatial structure of protein complexes or proteins between different species are important references for inferring the functions of unknown proteins and predicting the evolutionary relationships of species [ 4 , 5 ]. However, PPI networks are large in scale and complex in structure, making them more difficult to study by traditional methods, so that some scholars have proposed using network alignment algorithms to analyze them.…”

SAMNA: Accurate Alignment of Multiple Biological Networks Based on Simulated Annealing

“…As the number of networks has gradually increased, so has the demand for analyzing and discovering hidden information in network data. Modeling known PPIs as network models and analyzing the spatial structure of protein complexes or proteins between different species are important references for inferring the functions of unknown proteins and predicting the evolutionary relationships of species [4,5]. However, PPI networks are large in scale and complex in structure, making them more difficult to research by traditional methods, so some scholars have proposed using network alignment algorithms to analyze PPI networks.…”

Pairwise Biological Network Alignment Based on Discrete Bat Algorithm

“…The purpose of local network alignment is to discover similar local subgraphs among networks [ 18 ]. MaWISH [ 19 ], Graemlin [ 20 ], PathBLAST [ 21 ], NetworkBLAST [ 22 ], and LePrimAlign [ 23 ] are examples of algorithms that can be used to solve the local network alignment problem. However, the disadvantage of local network alignment is that one module may be similar to several modules and a protein may be mapped to dissimilar protein nodes [ 24 ].…”