AutoCoEv

AutoCoEv predicts novel protein-protein interactions by large-scale co-evolutionary analysis to reveal functional communications within cellular regulatory networks.

Key Features:

• High-throughput co-evolutionary prediction: Performs in silico prediction of protein-protein interactions across large protein arrays using co-evolution signals.
• Co-evolution as interaction marker: Leverages interdependent evolutionary changes between proteins to infer direct or indirect functional interactions.
• Automation and parallelization: Automates and parallelizes the workflow to enable analysis of extensive datasets.
• Integration of tools: Integrates 15 individual programs into a single computational pipeline.
• CAPS2 with statistical enhancement: Culminates in CAPS2 augmented by a custom patch that implements multiple comparisons correction to strengthen statistical robustness.
• Demonstrated scalability: Applied to a dataset of 324 proteins located near lipid rafts in B lymphocytes, identifying numerous strong coevolutionary relationships and predicting novel partners and clusters of functionally related molecules.

Scientific Applications:

• Protein-protein interaction discovery: Predicts novel PPIs from co-evolutionary signals to expand known interaction networks.
• Functional network and cluster identification: Detects clusters of functionally related molecules and novel interaction partners within cellular systems.
• Systems biology analyses: Facilitates large-scale inference of functional communications relevant to cellular regulatory networks, exemplified by analysis of lipid-raft–associated proteins in B lymphocytes.

Methodology:

Leverages co-evolutionary analysis, automates and parallelizes the workflow, integrates 15 individual programs, and uses CAPS2 with a custom patch implementing multiple comparisons correction.