A game changer in cancer kinase target profiling
Kinases are enzymes that regulate cell signaling by adding phosphate groups in cell signaling, and their dysregulation is linked to cancer progression. Targeting kinases with small-molecule inhibitors is a promising therapeutic strategy but developing selective inhibitors to prevent unintended off-target effects remains challenging due to structural similarities among kinases. In a recent study, published in Molecular & Cellular Proteomics, Wouter van Bergen of the University of Utrecht, The Netherlands, introduces a novel technique that improves kinase target identification, to help to enhance drug specificity and reduce unintended interactions.

Unlike traditional methods, this study used phosphonate affinity tags, which are chemical probes that mimic phosphate groups, for monitoring site-specific drug binding. These tags facilitate the distinction between closely related kinases, helping to reveal off-target effects. Using a combination of cell biology, biochemical reactions and proteomics, the group demonstrated that phosphonate affinity tags are a useful tool for high-specificity kinase inhibitor profiling. In human lung carcinoma cells treated with a tyrosine kinase inhibitor, they used covalent linkage formation between a broad-spectrum kinase targeting activity-based probe and the phosphonate tag, followed by proteomic analysis, to identify effective competition between the inhibitor, a key indicator of target engagement. This approach also uncovered previously unknown off-target interactions, confirming its sensitivity and accuracy.
By refining kinase inhibitor profiling, this technique opens the door to more precise cancer therapies. It could support personalized medicine approaches by tailoring treatments to individual patients, improving both safety and efficacy.
Enjoy reading ASBMB Today?
Become a member to receive the print edition four times a year and the digital edition monthly.
Learn moreGet the latest from ASBMB Today
Enter your email address, and we’ll send you a weekly email with recent articles, interviews and more.
Latest in Science
Science highlights or most popular articles

AI unlocks the hidden grammar of gene regulation
Using fruit flies and artificial intelligence, Julia Zeitlinger’s lab is decoding genome patterns — revealing how transcription factors and nucleosomes control gene expression, pushing biology toward faster, more precise discoveries.

Zebrafish model links low omega-3s to eye abnormalities
Researchers at the University of Colorado Anschutz developed a zebrafish model to show that low maternal docosahexaenoic acid can disrupt embryo eye development and immune gene expression, offering a tool to study nutrition in neurodevelopment.

Top reviewers at ASBMB journals
Editors recognize the heavy-lifters and rising stars during Peer Review Week.

Teaching AI to listen
A computational medicine graduate student reflects on building natural language processing tools that extract meaning from messy clinical notes — transforming how we identify genetic risk while redefining what it means to listen in science.

Early lipid changes drive retinal degeneration in Zellweger spectrum disorder
Lipid profiling in a rare disease mouse model reveals metabolic shifts and inflammation in the retinal pigment epithelium — offering promising biomarker leads to combat blindness.

How sugars shape Marfan syndrome
Research from the University of Georgia shows that Marfan syndrome–associated fibrillin-1 mutations disrupt O glycosylation, revealing unexpected changes that may alter the protein's function in the extracellular matrix.