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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.

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