Abstract Background Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential response shown by altered kinases to drug treatment in patients and cell-based assays. However, an incomplete understanding of the relationships connecting genome, proteome and drug sensitivity profiles present a major bottleneck in targeting kinases for personalized medicine. Results In this study, we propose a multi-component Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model and neural networks framework for providing explainable models of protein kinase inhibition and drug response ( $$\hbox {IC}_{50}$$ IC 50 ) profiles in cell lines. Using non-small cell lung cancer as a case study, we show that interaction terms that capture associations between drugs, pathways, and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib). In particular, protein–protein interactions associated with the JNK apoptotic pathway, associations between lung development and axon extension, and interaction terms connecting drug substructures and the volume/charge of mutant residues at specific structural locations contribute significantly to the observed $$\hbox {IC}_{50}$$ IC 50 values in cell-based assays. Conclusions By integrating multi-omics data in the QSMART model, we not only predict drug responses in cancer cell lines with high accuracy but also identify features and explainable interaction terms contributing to the accuracy. Although we have tested our multi-component explainable framework on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles.
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A redox-active switch in fructosamine-3-kinases expands the regulatory repertoire of the protein kinase superfamily
Aberrant regulation of metabolic kinases by altered redox homeostasis substantially contributes to aging and various diseases, such as diabetes. We found that the catalytic activity of a conserved family of fructosamine-3-kinases (FN3Ks), which are evolutionarily related to eukaryotic protein kinases, is regulated by redox-sensitive cysteine residues in the kinase domain. The crystal structure of the FN3K homolog from
- NSF-PAR ID:
- 10169261
- Publisher / Repository:
- American Association for the Advancement of Science (AAAS)
- Date Published:
- Journal Name:
- Science Signaling
- Volume:
- 13
- Issue:
- 639
- ISSN:
- 1945-0877
- Page Range / eLocation ID:
- Article No. eaax6313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Protein phosphorylation is a key signalling mechanism and has myriad effects on protein function. Phosphorylation by protein kinases can be reversed by protein phosphatases, thus allowing dynamic control of protein phosphorylation. Although this may suggest a straightforward kinase–phosphatase relationship, plant genomes contain five times more kinases than phosphatases. Here, we examine phospho‐signalling from a protein phosphatase centred perspective and ask how relatively few phosphatases regulate many phosphorylation sites. The most abundant class of plant phosphatases, the protein phosphatase 2Cs (PP2Cs), is surrounded by a web of regulation including inhibitor and activator proteins as well as posttranslational modifications that regulate phosphatase activity, control phosphatase stability, or determine the subcellular locations where the phosphatase is present and active. These mechanisms are best established for the Clade A PP2Cs, which are key components of stress and abscisic acid signalling. We also describe other PP2C clades and illustrate how these phosphatases are highly regulated and involved in a wide range of physiological functions. Together, these examples of multiple layers of phosphatase regulation help explain the unbalanced kinase–phosphatase ratio. Continued use of phosphoproteomics to examine phosphatase targets and phosphatase–kinase relationships will be important for deeper understanding of phosphoproteome regulation.
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