IPF Disorder-Weighted PPI Explorer: A Network-Based Computational Framework for Identifying Vulnerable Molecular Targets in Idiopathic Pulmonary Fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a progressive interstitial lung disease characterized by irreversible fibrotic remodeling and high mortality. While numerous molecular factors have been implicated, the systems-level mechanisms that govern network vulnerability in IPF remain poorly understood. In particular, conventional bioinformatics approaches often emphasize gene expression or static protein-protein interaction (PPI) networks, while underrepresenting the role of intrinsic protein disorder and its influence on interaction dynamics. Here, we present the IPF Disorder-Weighted PPI Explorer (IPF-DWPE), a Python-based computational framework that integrates residue-level intrinsic disorder, PPI network topology, and disease-associated variant information to identify vulnerable proteins and interactions in IPF. The framework defines four novel, author-proposed metrics: Sequence-Topology-Knowledge-Driven Disorder (STKDD), Disorder-Flux-Weighted Minimum Interaction (DFWMIN), Protein Vulnerability Index (PVI), and Disorder-Mean Neighborhood Fold Z-score (DMNFZ). Together, these metrics quantify disorder-driven network centrality, interaction fragility, perturbation propagation potential, and local disorder outliers within the interactome. By explicitly combining structural flexibility with network context, IPF-DWPE highlights proteins and interactions that are likely to act as critical conduits for disease-associated perturbations. The framework is designed as a transparent, hypothesis-generating platform for exploratory systems biology analysis rather than clinical prediction. This disorder-weighted network approach provides a complementary perspective to pathway-centric analyses and offers a scalable strategy for prioritizing molecular vulnerabilities in IPF and other complex diseases where intrinsic protein disorder plays a functional role.