Seed selection strategy in global network alignment without destroying the entire structures of functional modules

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Network alignment, particularly of protein-protein interaction (PPI) networks, is crucial for identifying conserved pathways and improving biological system insights.
• Existing global network alignment (GNA) methods often rely on heuristic seed-and-extend approaches, which can be suboptimal due to localized seed selection and disregard for original functional module structures.
• The NP-complete nature of GNA necessitates efficient heuristic solutions that can overcome these limitations.

Purpose of the Study:

• To develop a novel seed selection strategy for global network alignment that preserves functional modules.
• To improve the accuracy and biological relevance of network alignments by addressing limitations of conventional methods.

Main Methods:

• Proposed a Multiple Hubs-based Alignment (MHA) strategy using pairs of hub nodes as multiple seeds for network alignment.
• Employed node membership similarity (including sequence, centrality, and dynamic membership similarity) to quantify functional module participation.
• Aligned networks based on module preservation, ensuring functional modules remain intact during the heuristic alignment process.

Main Results:

• The MHA method effectively balances the extension of conserved interactions with the preservation of functional modules.
• A case study aligning yeast and fly PPI networks demonstrated MHA's superior performance over state-of-the-art algorithms.
• MHA identified conserved functional modules more effectively and retrieved 86% more conserved interactions compared to IsoRank.

Conclusions:

• The novel seed selection strategy yields topologically and biologically more similar network alignment results.
• MHA can serve as a valuable reference and complement to existing heuristic methods for more meaningful biological network alignments.