Alignment of Protein-Protein Interaction Networks

TL;DR

This paper introduces PPINA, an advanced algorithm for aligning protein-protein interaction networks that integrates topological, sequence, and functional similarities, outperforming existing methods on real data.

Contribution

PPINA extends the NETAL algorithm by incorporating functional similarities, providing a more comprehensive and biologically meaningful network alignment approach.

Findings

PPINA outperforms existing algorithms on real PPI networks.

It provides biologically meaningful alignment results.

The method effectively integrates multiple similarity measures.

Abstract

PPI network alignment aims to find topological and functional similarities between networks of different species. Several alignment approaches have been proposed. Each of these approaches relies on a different alignment method and uses different biological information during the alignment process such as the topological structure of the networks and the sequence similarities between the proteins, but less of them integrate the functional similarities between proteins. In this context, we present our algorithm PPINA (Protein-Protein Interaction Network Aligner), which is an extension of the NETAL algorithm. The latter aligns two networks based on the sequence, functional and network topology similarity of the proteins. PPINA has been tested on real PPI networks. The results show that PPINA has outperformed other alignment algorithms where it provides biologically meaningful results.