Joint Alignment of Multiple Protein-Protein Interaction Networks via Convex Optimization

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• High-throughput experiments generate vast amounts of protein-protein interaction (PPI) data.
• PPI network alignment is crucial for understanding evolutionary relationships, identifying conserved subnetworks, and functional annotation.
• Existing multiple PPI network alignment methods have limitations in alignment quality.

Purpose of the Study:

• To introduce ConvexAlign, a novel method for joint alignment of multiple PPI networks.
• To improve the quality and functional coherence of multiple PPI network alignments.
• To identify conserved biological complexes that may be missed by other methods.

Main Methods:

• Developed ConvexAlign, a method utilizing convex optimization.
• The scoring function integrates sequence similarity, topological score, and interaction conservation.
• Optimizes alignments globally and enforces consistency across all pairwise alignments.

Main Results:

• ConvexAlign outperforms popular methods in producing functionally coherent alignments on synthetic and real data.
• Demonstrated a significant advantage over existing methods when aligning real PPI networks.
• Successfully identified conserved complexes not detectable by other alignment methods.

Conclusions:

• ConvexAlign offers a superior approach to multiple PPI network alignment compared to existing methods.
• The global optimization strategy and consistency enforcement lead to enhanced alignment quality.
• This method provides valuable insights into conserved biological structures and functions across species.