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

Abstract

Abstract Background: Protein is an important part of biological tissue and contains a lot of biological information. Protein-protein interaction network alignment is a method for analyzing proteins that helps discover conserved functions between organisms and predict unknown functions. In particular, multi-network alignment aims to find the mapping relationship among multiple network nodes, so as to transfer the knowledge of species. However, with the increasing complexity of PPI networks, how to perform network alignment more accurately and efficiently is a new challenge. Results: This paper proposes a new global network alignment algorithm called SAMNA (Simulated Annealing Multiple Network Alignment), using both network topology and sequence homology information. To generate the alignment, SAMNA first generates cross-network candidate clusters by a clustering algorithm on a k-partite similarity graph constructed with sequence similarity information, and then selects candidate cluster nodes as alignment results and optimizes them using an improved simulated annealing algorithm. Conclusion: The SAMNA algorithm was experimented on synthetic and real-world network datasets, and the results showed that SAMNA outperformed the state-of-the-art algorithm in biological performance.