Classifying covalent protein binders by their targeted binding site
Bibliographic record
Abstract
Covalent protein targeting represents a powerful tool for protein characterization, identification, and activity modulation. The safety of covalent therapeutics was questioned for many years due to the possibility of off-target binding and subsequent potential toxicity. Researchers have recently, however, demonstrated many covalent binders as safe, potent, and long-acting therapeutics. Moreover, they have achieved selective targeting among proteins with high structural similarities, overcome mutation-induced resistance, and obtained higher potency compared to non-covalent binders. In this review, we highlight the different classes of binding sites on a target protein that could be addressed by a covalent binder. Upon folding, proteins generate various concavities available for covalent modifications. Selective targeting to a specific site is driven by differences in the geometry and physicochemical properties of the binding pocket residues as well as the geometry and reactivity of the covalent modifier "warhead". According to the warhead reactivity and the nature of the binding region, covalent binders can alter or lock a targeted protein conformation and inhibit or enhance its activity. We survey these various modification sites using case studies of recently discovered covalent binders, bringing to the fore the versatile application of covalent protein binders with respect to drug discovery approaches.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".