Covalent Ligand Electrophiles Are Differentially Activated by Proximity Effects Which Govern Latent Protein Reactivity
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Covalent ligands contain an electrophilic moiety that reacts with a nucleophilic residue on a target protein, following an initial reversible binding event. Covalent ligand development typically involves efforts to increase on-target selectivity by maximizing the ligand binding affinity and minimizing intrinsic electrophile reactivity. Problematically, this limits labeling kinetics and requires high affinity ligands. The concept of “latency” describes the potential for “turn-on” activation of electrophiles upon target engagement. Here, we investigate the potential intrinsic latency of covalent electrophiles and test the hypothesis that diverse electrophiles can be differentially activated by proximity effects. We develop a kinetic effective molarity (EM k ) approach to quantitatively characterize kinetics associated with diverse electrophilic reaction mechanisms, both with and without binding proximity effects. We observe that different electrophiles are associated with significantly different EM k parameters, with SuFEx and acrylamide electrophiles associated with the highest intrinsic latency. Eyring transition state analysis revealed that all covalent ligands, independent of electrophile, benefit from significant transition state entropic stabilization. Strikingly, electrophiles associated with the highest latency are associated with greater relative transition state stabilization with different enthalpic and entropic contributions. These findings quantitatively describe electrophile latency and will aid the mechanism-guided development of next-generation covalent ligands associated with “turn-on” reactivity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".