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Methods Mol Biol. 2017;1558:213-232. doi: 10.1007/978-1-4939-6783-4_10.

Analysis of Protein Phosphorylation and Its Functional Impact on Protein-Protein Interactions via Text Mining of the Scientific Literature.

Author information

1
Center for Bioinformatics and Computational Biology, Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way, Suite 205, Newark, DE, 19711, USA.
2
Department of Computer & Information Sciences, University of Delaware, Newark, DE, 19711, USA.
3
Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, 20057, USA.
4
Center for Bioinformatics and Computational Biology, Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way, Suite 205, Newark, DE, 19711, USA. arighi@dbi.udel.edu.
5
Department of Computer & Information Sciences, University of Delaware, Newark, DE, 19711, USA. arighi@dbi.udel.edu.

Abstract

Post-translational modifications (PTMs) are one of the main contributors to the diversity of proteoforms in the proteomic landscape. In particular, protein phosphorylation represents an essential regulatory mechanism that plays a role in many biological processes. Protein kinases, the enzymes catalyzing this reaction, are key participants in metabolic and signaling pathways. Their activation or inactivation dictate downstream events: what substrates are modified and their subsequent impact (e.g., activation state, localization, protein-protein interactions (PPIs)). The biomedical literature continues to be the main source of evidence for experimental information about protein phosphorylation. Automatic methods to bring together phosphorylation events and phosphorylation-dependent PPIs can help to summarize the current knowledge and to expose hidden connections. In this chapter, we demonstrate two text mining tools, RLIMS-P and eFIP, for the retrieval and extraction of kinase-substrate-site data and phosphorylation-dependent PPIs from the literature. These tools offer several advantages over a literature search in PubMed as their results are specific for phosphorylation. RLIMS-P and eFIP results can be sorted, organized, and viewed in multiple ways to answer relevant biological questions, and the protein mentions are linked to UniProt identifiers.

KEYWORDS:

Bioinformatics; Phosphorylation; Post-translational modification; Protein–protein interaction; Text mining

PMID:
28150240
PMCID:
PMC5446092
DOI:
10.1007/978-1-4939-6783-4_10
[Indexed for MEDLINE]
Free PMC Article

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