PCP

PCP predicts protein complexes from Protein-Protein Interaction (PPI) networks to reconstruct sets of interacting proteins for analysis of cellular functional organization and disease-associated dysfunctional complexes.

Key Features:

• Complex Reconstruction: Reconstructs constituent proteins of complexes from PPI networks.
• Performance Evaluation: Evaluates multiple computational methods on PPI datasets, including yeast, to assess complex identification effectiveness.
• Limitations and Challenges: Addresses detection challenges such as sparse complexes, small or sub-complexes, and overlapping complexes within networks.
• Integration of Diverse Information: Integrates expression profiles and protein 3D structures with PPI networks to inform complex dynamics, including time-based assembly and fuzzy complexes from intrinsically disordered proteins.
• Application in Disease Research: Identifies dysfunctional protein complexes associated with human diseases to support elucidation of disease mechanisms and therapeutic target discovery.

Scientific Applications:

• Functional Organization of Cells: Predicts protein complexes to inform understanding of cellular functions and organization.
• Disease Mechanism Exploration: Identifies dysfunctional complexes to elucidate molecular bases of human diseases and aid therapeutic target discovery.
• Complex Dynamics Understanding: Combines PPI, expression, and structural data to analyze time-dependent assembly and fuzzy complexes involving intrinsically disordered proteins.

Methodology:

Predicts protein complexes from PPI networks, leverages computational methods developed between 2003 and 2015, evaluates methods on PPI datasets (including yeast), integrates expression profiles and protein 3D structures with PPI networks, and addresses detection of sparse/small/sub-complexes, overlapping complexes, time-based assembly, and fuzzy complexes from intrinsically disordered proteins.