Rapid profiling of protein kinase inhibitors by quantitative proteomics

Golkowski, Martin; Brigham, Jennifer L.; Perera, Buddhini; Romano, Guillermo S.; Maly, Dustin J.; Ong, Shao En

doi:10.1039/c3md00315a

Cited by 21 publications

(29 citation statements)

References 30 publications

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“…1a). 5, 24, 28, 33–35 The inhibitor analogs 1 and 3–5 were described previously and the affinity reagents 2 , 6 and 7 were newly developed in our laboratory. Compounds 1–7 were immobilized on carboxy-derivatized sepharose using amide coupling chemistry to yield the corresponding affinity matrices.…”

Section: Resultsmentioning

confidence: 99%

“…1b). 5, 18 Experiments were done in quadruplicate, with two SILAC label swap experiments in which SILAC labels were switched between control and inhibitor competition experiments. The label swap experiment helps to distinguish true hits from non-selective hits as the former show an inversion of the sign of the log2 SILAC ratios, while contaminants and false positives do not.…”

Section: Resultsmentioning

confidence: 99%

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Kinobead and Single-Shot LC-MS Profiling Identifies Selective PKD Inhibitors

et al. 2017

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ATP-competitive protein kinase inhibitors are important research tools and therapeutic agents. Because there are >500 human kinases that contain highly conserved active sites, the development of selective inhibitors is extremely challenging. Methods to rapidly and efficiently profile kinase inhibitor targets in cell lysates are urgently needed to discover selective compounds and to elucidate the mechanisms of action for polypharmacological inhibitors. Here, we describe a protocol for microgram-scale chemoproteomic profiling of ATP-competitive kinase inhibitors using kinobeads. We employed a gel-free in situ digestion protocol coupled to nanoflow liquid chromatography-mass spectrometry to profile ~200 kinases in single analytical runs using as little as 5 μl of kinobeads and 300 μg of protein. With our kinobead reagents, we obtained broad coverage of the kinome, monitoring the relative expression levels of 312 kinases in a diverse panel of 11 cancer cell lines. Further, we profiled a set of pyrrolopyrimidine- and pyrazolopyrimidine-based kinase inhibitors in competition-binding experiments with label-free quantification, leading to the discovery of a novel selective and potent inhibitor of protein kinase D (PKD) 1, 2 and 3. Our protocol is useful for rapid and sensitive profiling of kinase expression levels and ATP-competitive kinase inhibitor selectivity in native proteomes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Kinobead and Single-Shot LC-MS Profiling Identifies Selective PKD Inhibitors

et al. 2017

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“…This number is in agreement with previously reported dasatinib enrichment experiments. 15 Of the four additional proteins with a Heavy/Light ratio > +1 σ, three (HNRPF, ARPC3, and RL37A) were very close to the cut-off. The remaining protein, NOMO3, is a poorly annotated single-pass membrane protein that has not previously been reported to interact with dasatinib.…”

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confidence: 89%

Direct Proximity Tagging of Small Molecule Protein Targets Using an Engineered NEDD8 Ligase

et al. 2016

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Identifying the protein targets of bioactive small molecules remains a major problem in the discovery of new chemical probes and therapeutics. While activity-based probes and photo-crosslinkers have had success in identifying protein targets of small molecules, each technique has limitations. Here we describe a method for direct proximity tagging of protein targets that bind small molecules. We engineered a promiscuous ligase based on the NEDD8 conjugating enzyme, Ubc12, which can be covalently linked to a small molecule of interest. When target proteins bind the small molecule they are directly labeled on surface lysines with a biotinylated derivative of the small ubiquitin homolog, NEDD8. This unique covalent tag can then be used to identify the small molecule binding proteins. Utilizing the drug dasatinib, we have shown that dasatinib-directed NEDDylation occurs for known endogenous protein binders in complex cell lysates. In addition, we have been able to improve NEDDylation efficiency through rational mutagenesis. Finally, we have shown that affinity-directed NEDDylation can be applied to two other protein-ligand interactions beyond kinases. Proximity tagging using this engineered ligase requires direct binding of the target, and thus provides a useful and orthogonal approach to facilitate small molecule target identification.

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“…The seven kinobead affinity reagents used were synthesized in-house as described previously (56,57). For optimal coverage of the human kinome an optimized mixture of the seven kinobead reagents was prepared as follows: 1 ml of reagent 1, 0.5 ml of reagents 2, 3 and 7, respectively, and 0.25 ml of reagents 4, 5 and 6, respectively, to give 3.25 ml of the complete kinobead mixture.…”

Section: Preparation Of Optimized Kinobead Mixturementioning

confidence: 99%

Kinome-centric pharmacoproteomics identifies signaling pathways underlying cellular responses to targeted cancer drugs

Golkowski

Lau

Chan

et al. 2019

Preprint

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Kinase-dependent signaling networks are frequently dysregulated in cancer, driving disease progression. While kinase inhibition has become an important therapeutic approach many cancers resist drug treatment. Therefore, we need both reliable biomarkers that predict drug responses and new targets to overcome drug resistance. Determining the kinase(s) that control cancer progression in individual cancers can pose a significant challenge. Genomics has identified important, yet limited numbers of kinase driver mutations. Transcriptomics can quantify aberrant gene expression, but it cannot measure the protein phosphorylation that regulates kinase-dependent signaling network activity. Proteomics measures protein expression and phosphorylation and, therefore, quantifies aberrant signaling network activity directly. We developed a kinome-centric pharmacoproteomics platform to study signaling pathways that determine cancer drug response. Using hepatocellular carcinoma (HCC) as our model, we determined kinome activity with kinobead/LC-MS profiling, and screened 299 kinase inhibitors for growth inhibition. Integrating kinome activity with drug responses, we obtained a comprehensive database of predictive biomarkers, and kinase targets that promote drug sensitivity and resistance. Our dataset specified pathway-based biomarkers for the clinical HCC drugs sorafenib, regorafenib and lenvatinib, and we found these biomarkers enriched in human HCC specimens. Strikingly, our database also revealed signaling pathways that promote HCC cell epithelialmesenchymal transition (EMT) and drug resistance, and that NUAK1 and NUAK2 regulate these pathways.Inhibition of these kinases reversed the EMT and sensitized HCC cells to kinase inhibition. These results demonstrate that our kinome pharmacoproteomics platform discovers both predictive biomarkers for personalized oncology and novel cancer drug targets.

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Rapid profiling of protein kinase inhibitors by quantitative proteomics

Cited by 21 publications

References 30 publications

Kinobead and Single-Shot LC-MS Profiling Identifies Selective PKD Inhibitors

Kinobead and Single-Shot LC-MS Profiling Identifies Selective PKD Inhibitors

Direct Proximity Tagging of Small Molecule Protein Targets Using an Engineered NEDD8 Ligase

Kinome-centric pharmacoproteomics identifies signaling pathways underlying cellular responses to targeted cancer drugs

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