Comparative Clustering (CompaCt) of eukaryote complexomes identifies novel interactions and sheds light on protein...

Area of Science:

• Proteomics
• Systems Biology
• Bioinformatics

Background:

• Complexome profiling characterizes protein complexes using native gel electrophoresis and mass spectrometry.
• Existing computational methods are limited to analyzing individual datasets, hindering large-scale cross-species comparisons.
• A need exists for robust methods to integrate and compare complexome data across diverse species.

Purpose of the Study:

• To develop and present Comparative Clustering (CompaCt), a novel computational tool for automated, large-scale integrative analysis of complexome profiling data.
• To enable systematic characterization and comparison of protein complexes across multiple species.
• To leverage orthology for identifying conserved and taxon-specific protein complexes.

Main Methods:

• CompaCt performs fully automated integrative analysis of complexome profiling data from multiple species.
• The method incorporates a novel approach for using orthology in comparative analyses.
• Applied to 53 complexome profiles across eukaryotic branches.

Main Results:

• CompaCt robustly identifies protein complex composition.
• Integrated analysis of multiple datasets enhances complex characterization compared to single-dataset analyses.
• Novel candidate interactors and complexes were identified in various species, including emp24, V-ATPase, and mitochondrial ATP synthase.
• Demonstrated utility in characterizing the Anopheles stephensi complexome and understanding metazoan protein complex evolution.

Conclusions:

• CompaCt provides a powerful, automated solution for large-scale comparative complexome analysis.
• The tool facilitates the systematic identification of conserved and species-specific protein complexes.
• CompaCt advances the study of protein complex evolution and function across eukaryotes.