MCL-CAw

MCL-CAw refines the Markov Clustering (MCL) algorithm using a core-attachment model to improve detection of protein complexes from yeast protein–protein interaction (PPI) networks.

Key Features:

• Core-Attachment Structure Incorporation: Integrates a core-attachment refinement that distinguishes "core" proteins central to complexes from peripheral "attachment" proteins based on observed modularity in yeast complexes.
• Scalability and Robustness: Retains MCL's scalability and robust performance on weighted networks while reducing noise-induced inaccuracies in cluster predictions.
• Affinity Scoring Integration: Leverages various affinity scoring schemes to mitigate false positives and improve precision and recall of predicted complexes in noisy PPI datasets.
• Performance Evaluation: Demonstrates improved recovery of a larger number of yeast complexes with higher accuracy compared to traditional MCL, particularly under noisy conditions.
• Complex Detection Enhancement: Refines clusters via core-attachment analysis to better align predicted clusters with known biological complexes and to reveal complexes missed due to scoring limitations.
• Comparative Analysis: Is evaluated against other recent complex detection algorithms across unscored and scored networks, showing superior ability to detect accurate protein complexes.
• Protein Essentiality Correlation: Includes analysis of protein essentiality within predicted complexes to explore correlations between essentiality and complex membership.

Scientific Applications:

• Reconstruction of cellular organization: Refines PPI-derived cluster sets to improve reconstruction of cellular organization and higher-order architecture in yeast.
• Protein complex discovery and interpretation: Improves accuracy and reliability of protein complex predictions to facilitate functional interpretation of yeast proteomes and interactions.

Methodology:

MCL-CAw applies a core-attachment refinement to clusters produced by the Markov Clustering algorithm, incorporates affinity scoring schemes for weighted PPI networks, performs comparative evaluations against MCL and other algorithms on unscored and scored networks, and analyzes protein essentiality correlations.