Identification of Covalent Ligands – from Single Targets to Whole Proteome
Bibliographic record
Abstract
Abstract There has been a surge of interest and efforts in the discovery of covalent ligands for diverse proteins as tool compounds or therapeutic candidates in recent years. We present two studies that involve applications of a target‐centric approach and a ligand‐centric approach toward covalent ligand discovery. By targeting a rare cysteine residue in a receptor tyrosine kinase EphB3, we were able to rapidly identify potent inhibitors of EphB3 with extraordinary proteomic selectivity supported by activity‐based probe profiling. While characterizing an activity‐based probe intended for EphB3 using ABPP, we made a surprising discovery that its primary cellular target was a catalytic subunit of V‐ATPase through its covalent engagement with a cryptic pocket on V‐ATPase. These two approaches will be increasingly used in combination to develop covalent ligands with high potency and yield comprehensive target profiles to accelerate the rate of therapeutic discovery in the future.
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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) | 0.000 | 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".