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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".