PilNPilO

PilNPilO is a novel computational method designed for the analysis of protein-protein interaction (PPI) networks. It aims to identify functionally important modules or complexes within these networks by leveraging both topological information and sequence-based features of the proteins involved. The method employs a strategy that iteratively refines candidate modules based on connectivity patterns and evolutionary conservation scores.

The core of PilNPilO's approach involves assigning weights to edges in the PPI network. These weights are derived from a combination of factors, including the number of co-occurring proteins in different species (a proxy for functional association) and the similarity of protein sequences. Proteins with highly conserved sequences are often indicative of essential functions, and this information is incorporated into the module detection process.

PilNPilO then uses a greedy clustering algorithm to identify dense subgraphs within the weighted network. These subgraphs represent potential protein complexes or functional modules. The algorithm starts with small seed modules and iteratively expands them by adding neighboring proteins that contribute significantly to the overall module score, which is calculated based on the sum of edge weights within the module and the density of connections.

The method has been evaluated on several benchmark PPI datasets and has demonstrated its ability to identify known protein complexes and functionally coherent groups of proteins. Its integration of both topological and sequence information provides a more robust approach to PPI network analysis compared to methods relying solely on one type of data. PilNPilO is intended as a tool for researchers to gain deeper insights into the organization and functional architecture of biological systems.