How Reactive are Druggable Cysteines in Protein Kinases?
Bibliographic record
Abstract
Targeted covalent inhibitors (TCIs) have been successfully developed as high-affinity and selective inhibitors of enzymes of the protein kinase family. These drugs typically act by undergoing an electrophilic addition with an active-site cysteine residue, so design of a TCI begins with the identification of a “druggable” cysteine. These electrophilic additions generally require deprotonation of the thiol to form a reactive anionic thiolate, so the acidity of the residue is a critical factor. Few experimental measurements of the p K a ’s of druggable cysteines have been reported, so computational prediction could prove to be very important in selecting reactive cysteine targets. Here we report the computed p K a ’s of druggable cysteines in selected protein kinases that are of clinical relevance for targeted therapies. The p K a ’s of the cysteines were calculated using advanced computational methods based on all-atom replica-exchange thermodynamic integration molecular dynamics simulations in explicit solvent. We found that the acidities of druggable cysteines within protein kinases are diverse and elevated, indicating enormous differences in their reactivity. Constant-pH molecular dynamics simulations were also performed on selected protein kinases, and the results confirmed this varied range in the acidities of druggable cysteines. Many of these active-site cysteines have low exposure to solvent molecules, elevating their p K a values. Electrostatic interactions with nearby anionic residues also elevate the p K a ’s of cysteine residues in the active site. The results suggest that some cysteine residues within kinase binding sites will be slow to react with a TCI because of their low acidity. Several oncogenic kinase mutations were also modeled and found to have p K a ’s similar to that of the wild-type kinase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".