Protein Bioinformatics

Qinghua Wang, Karen E. Ross, Hongzhan Huang, Jia Ren, Gang Li, K. Vijay-Shanker, Cathy H. Wu, Cecilia N. Arighi, Wang, Qinghua, Ross, Karen E., Huang, Hongzhan, Ren, Jia, Li, Gang, Vijay-Shanker, K., Wu, Cathy H., Arighi, Cecilia N.

Cathy H. Wu, Cecilia N. Arighi, Karen E. Ross

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.