Global alignment of pairwise protein interaction networks for maximal common conserved patterns

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Protein-protein interaction (PPI) network alignment is crucial for understanding conserved biological functions across species.
• Existing alignment tools face challenges in speed, scalability, and accuracy.
• Identifying conserved regions in PPI networks aids in functional inference and pathway discovery.

Purpose of the Study:

• To introduce HopeMap, a novel connected-components based algorithm for fast and accurate PPI network alignment.
• To leverage precompiled lists of homologs identified by KEGG Orthology (KO) terms for improved alignment.
• To evaluate the performance of HopeMap using gene annotations, Gene Ontology (GO), and KO groups.

Main Methods:

• Developed HopeMap, a connected-components based algorithm utilizing a list of homologs identified by KO terms.
• Applied HopeMap to align PPI networks of various species pairs, including yeast-fly and bacteria pairs.
• Evaluated alignment accuracy using gene annotations, GO terms, and KO groups.

Main Results:

• HopeMap demonstrates linear computational cost, offering significant speed improvements.
• The algorithm achieves high accuracy, as validated by specificity and sensitivity metrics for KO and GO terms.
• Analysis of aligned clusters revealed conserved functional regions across species.

Conclusions:

• HopeMap provides a fast, accurate, and scalable solution for PPI network alignment.
• The KO-term-based approach effectively identifies conserved protein interactions.
• HopeMap is easily extendable to multiple network alignments, facilitating broader comparative analyses.