Probing the Protein Kinases′ Cysteinome by Covalent Fragments
Bibliographic record
Abstract
Protein kinases are important drug targets, yet specific inhibitors have been developed for only a fraction of the more than 500 human kinases. A major challenge in designing inhibitors for highly related kinases is selectivity. Unlike their non-covalent counterparts, covalent inhibitors offer the advantage of selectively targeting structurally similar kinases by modifying specific protein side chains, particularly non-conserved cysteines. Previously, covalent fragment screens yielded potent and selective inhibitors for individual kinases such as ERK1/2 but have not been applied to the broader kinome. Furthermore, many of the accessible cysteine positions have not been addressed so far. Here, we outline a generalizable approach to sample ATP-site cysteines with fragment-like covalent inhibitors. We present the development of a kinase-focused covalent fragment library and its systematic screening against a curated selection of 47 kinases, with 60 active site-proximal cysteines using LC/MS and differential scanning fluorimetry (DSF) assays, followed by hit validation through various complementary techniques. Our findings expand the repertoire of targetable cysteines within protein kinases, provide insight into unique binding modes identified from crystal structures and deliver isoform-specific hits with promising profiles as starting points for the development of highly potent and selective covalent inhibitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".