Construction of Protein PTM Networks by Data Mining, Text Mining, and Ontology Integration: Application to Multi-Faceted Disease Analysis

Perturbations in post-translational modifications (PTMs) and their downstream effects are recognized as key drivers of disease. We have developed iPTMnet, employing an integrative bioinformatics approach—combining text mining, data mining, and ontological representation to capture rich PTM information—for PTM network and disease discovery. Text mining tools are used for full-scale mining of PubMed abstracts and PMC Open Access articles to identify PTM information (kinase, substrate, and site) and phosphorylation-dependent protein-protein interactions (PPIs) in their biological context, including disease consequences. Experimentally observed PTMs, including high-throughput proteomic data from curated PTM databases, are incorporated. Ontologies are used for knowledge representation, particularly the Protein Ontology (PRO) for representation of PTM proteoforms and complexes. The web portal (http://proteininformationresource.org/iPTMnet) supports online search and visual analysis, including multiple-sequence alignment views for comparison of PTM forms across organisms and Cytoscape visualization of PTM enzyme-substrate and PPI relationships in PTM interaction networks. We are conducting use cases for PTM-disease discovery. First, we analyzed phosphorylation-dependent PPIs related to the PI3K/AKT/mTOR pathway, which is deregulated in many cancers and is targeted by therapeutic kinase inhibitors. We classified interactions as pro- and anti-oncogenic to indicate potential mechan...
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