Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Liang-Chin Huang, Karen E. Ross, Timothy R. Baffi, Harold Drabkin, Krzysztof J. Kochut, Zheng Ruan, Peter D’Eustachio, Daniel McSkimming, Cecilia Arighi, Chuming Chen, Darren A. Natale, Cynthia Smith, Pascale Gaudet, Alexandra C. Newton, Cathy Wu, Natarajan Kannan

ABSTRACT

Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the 'hydrophobic motif' of PKCβII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes. More... »

PAGES

6518

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-24457-1

DOI

http://dx.doi.org/10.1038/s41598-018-24457-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103549281

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29695735


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