title: Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.
abstract: Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases.
authors.affiliation: Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA oanat@udel.edu.
authors.forename: Catalina O
authors.initials: CO
authors.lastname: Tudor
authors.affiliation: Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
authors.forename: Karen E
authors.initials: KE
authors.lastname: Ross
authors.affiliation: Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.affiliation: Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
authors.forename: K
authors.initials: K
authors.lastname: Vijay-Shanker
authors.affiliation: Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
authors.forename: Cathy H
authors.initials: CH
authors.lastname: Wu
authors.affiliation: Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA.
authors.forename: Cecilia N
authors.initials: CN
authors.lastname: Arighi
mesh_terms: D057225:Data Mining; D030562:Databases, Protein; D003920:Diabetes Mellitus / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D010750:Phosphoproteins / Q000235:genetics / Q000378:metabolism; D010766:Phosphorylation; D011494:Protein Kinases / Q000235:genetics / Q000378:metabolism
chemical_list: D009363:Neoplasm Proteins; D010750:Phosphoproteins; D011494:Protein Kinases
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