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The other arginine residues p. R 75PKA , p. R PKA , p. R PKA , and p. R PKA , on the other hand, are not part of any known functional site, but are frequently mutated in cancer samples.
Iterative querying and hypothesis generation. A: Plot showing the frequency of each amino acids mutated in cancers in the protein kinase domain.
B: Wordle image showing kinases harboring arginine variants. The text height is proportional to the number of arginine variants observed in the corresponding kinase domain.
C: Locations of EGFR arginine variants in the crystal structure PDB:1XKK. Subdomains are colored and labeled.
D: Western blot results showing constitutive activity of p. E: EGFR auto-phosphorylation and downstream signaling of wild type and p.
RH PKA mutant inhibition with varied concentrations of gefitinib. To understand the functional impact of arginine variants in EGFR, we analyzed the associated pathways and reactions in ProKinO.
EGFR is a receptor tyrosine kinase that controls a diverse array of cellular processes associated with cell migration, adhesion and proliferation.
Its constitutive activity has been correlated with several cancer types and a variety of commercial inhibitors have been developed to abrogate this activity [Lynch et al.
Autophosphorylation of EGFR is one of the well-studied reactions in EGFR signaling, in which binding of EGF to the receptor activates the kinase domain and leads to autophosphorylation of Tyr residues p.
Y PKA , p. Y 0PKA , p. Y 0PKA , and p. Y 0PKA in the C-terminal tail [Helin and Beguinot, ; Margolis et al.
Based on this knowledge, we formulated a testable hypothesis that causative arginine variants will impact EGFR autophosphorylation and Stat3 phosphorylation.
To test this hypothesis, we transfected WT and mutant EGFR in Chinese hamster cells which express very low levels of EGFR and probed for phosphorylation of EGFR C-terminal tail and Stat3 tyrosine residues using western blot analysis, as described in the Methods section.
The substrate binding pocket variant p. RK PKA abrogates EGFR activity to the same extent as the catalytically dead variant p.
DG PKA Fig. In contrast, mutation of p. R PKA , or p. R PKA shows no significant change in C-terminal tail and Stat3 phosphorylation compared to WT.
Interestingly, however, p. The extent of EGFR activation by p. LQ PKA Fig. Cancer cells harboring p. Consistent with previous studies, our experimental results indicate increased sensitivity of the EGFR p.
RH PKA mutant to gefitinib treatment in comparison to WT Fig. To obtain additional insights on the mechanisms by which p.
R PKA mutated in other kinases? R PKA position? Likewise, analysis of kinases naturally conserving mutant types at equivalent positions can provide insights into the impact of variants on kinase structure, function and drug binding.
We queried for variants at position p. Our queries indicate multiple kinases with disease variants at position p. Mechanisms of activation of p.
RH PKA and p. RC PKA variants in EGFR. A: Kinases with variants at position p. R PKA. The text height is proportional to the number of variants.
B: Crystal structures PDBs: 2ITU, 2GS2, and 3GT8 of EGFR showing common p. R PKA orientations. Inactive structure shows a common C-helix capping interaction, whereas active structures instead coordinate with the hinge region and C-terminal tail.
Bottom shows kinase structures with naturally occurring cysteine PDB: 3V5W , histidine PDB: 2REI and glycine PDB: 3HDM at position p.
Based on this knowledge and our query results, we hypothesized that p. R PKA side-chain and C-helix backbone upon C-helix movement Fig.
Furthermore, analysis of kinases that naturally conserve a histidine or cysteine at position p. We also note that in EGFR, p.
R PKA is within hydrogen bonding proximity to the C-terminal auto-inhibitory AP2-helix, suggesting that both auto-inhibitory C-helix hinge and C-terminal tail interactions may be relieved by the p.
Further studies are needed to fully understand the mechanisms by which the p. We have demonstrated that ProKinO is a valuable resource for mining and annotating the cancer kinome.
In particular, the conceptual representation of knowledge related to structural and functional motifs allows effective mining of cancer variants while facilitating hypothesis generation and testing.
Our ontological approach is conceptually different from previous structure and machine learning based approaches to predict variant impact [Capriotti and Altman, ; Shi and Moult, ; Hashimoto et al.
Computational approaches, like those available in the Cancer Related Analysis of VAriance Toolkit CRAVAT [Douville et al. However, by making predictions on all proteins, they necessarily miss the wealth of knowledge stored in domain specific and single locus databases, and further don't provide the structural context necessary to frame a testable hypothesis.
The results presented here will serve as a conceptual starting point for experimental studies and help prioritize key variants and mutated kinases for functional characterization and drug discovery [Simpson et al.
While ProKinO offers several utilities for integrative analysis of protein kinase data, it needs to be further developed to fully realize its impact in kinase research.
For example, sequence and structural motifs that contribute to the functional specificity of major kinase groups and families can be added in the Sequence and Structural Motif classes to explore how variants impact family or group specific functions.
Network analysis on missense variants has revealed their preponderance in protein—protein, protein—nucleic acid, and protein—ion interfaces and validated that proteins involved in signal transduction are more frequently mutated in cancer [Nishi et al.
These interaction interfaces can provide the context crucial to predict variant impact. Likewise, incorporating information on kinase substrates and phosphorylation patterns can provide additional functional context for predicting variant impact [Hanks and Hunter, ; Ashburner et al.
Finally, user-friendly interfaces need to be incorporated to facilitate integrative analysis of ProKinO data by a wide range of scientific users.
In particular, SPARQL query construction requires both an in-depth knowledge of the SPARQL query language and a semantic understanding of the ontology.
We are working on a graphical query builder, which will allow the formulation of queries by visually inspecting the classes and relations in the ontology schema.
This will allow biologists who are not familiar with the SPARQL query language to formulate integrative, hypothesis-oriented queries on ProKinO data.
As part of future development, we also plan to incorporate structural visualization tools such as Mutation Position Imaging Toolbox MuPIT [Niknafs et al.
These tools are expected to enhance the usability of ProKinO and, consequently, accelerate the functional characterization of the cancer kinome.
Disclaimer: Supplementary materials have been peer-reviewed but not copyedited. National Center for Biotechnology Information , U.
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