GPPIAL: A New Global PPI Network Aligner Based on Orthologs

Protein-protein interaction (PPI) network alignment aims to find a node mapping between nodes across networks for different specifies such that the mapped nodes are topologically and biologically similar. Such alignment can help to reveal functionally similar subnetworks and predict previously undiscovered orthologs of different species. Although many aligners have been proposed, few can find global alignments with both high topological and biological quality. In this paper, we propose a new pairwise global PPI network aligner (GPPIAL) based on orthologs. We first construct a score matrix to evaluate the matching possibility of node pairs by combining multiple sources of information, including the biological (sequence and functional), topological (degree and neighborhood), and interaction information. Then we select orthologs with high similarity scores in the Inparanoid database as anchor pairs, and gradually build a global alignment. We compare GPPIAL with the stateof-the-art pairwise global network aligners on multiple PPI network pairs from the BioGRID dataset. The results show that GPPIAL significantly outperforms existing global aligners in terms of biological quality without losing the topological quality. Moreover, GPPIAL can also detect more orthologous proteins than existing global aligners, and thus can be applied to predicting the function of unannotated proteins. Our code and datasets are available at https://github.com/whuyrc/GPPIAL.