“Inverse Drug Discovery” Strategy To Identify Proteins That Are Targeted by Latent Electrophiles As Exemplified by Aryl Fluorosulfates
Bibliographic record
Abstract
Drug candidates are generally discovered using biochemical screens employing an isolated target protein or by utilizing cell-based phenotypic assays. Both noncovalent and covalent hits emerge from such endeavors. Herein, we exemplify an "Inverse Drug Discovery" strategy in which organic compounds of intermediate complexity harboring weak, but activatable, electrophiles are matched with the protein(s) they react with in cells or cell lysate. An alkyne substructure in each candidate small molecule enables affinity chromatography-mass spectrometry, which produces a list of proteins that each distinct compound reacts with. A notable feature of this approach is that it is agnostic with respect to the cellular proteins targeted. To illustrate this strategy, we employed aryl fluorosulfates, an underexplored class of sulfur(VI) halides, that are generally unreactive unless activated by protein binding. Reversible aryl fluorosulfate binding, correct juxtaposition of protein side chain functional groups, and transition-state stabilization of the S(VI) exchange reaction all seem to be critical for conjugate formation. The aryl fluorosulfates studied thus far exhibit chemoselective reactivity toward Lys and, particularly, Tyr side chains, and can be used to target nonenzymes (e.g., a hormone carrier or a small-molecule carrier protein) as well as enzymes. The "Inverse Drug Discovery" strategy should be particularly attractive as a means to explore latent electrophiles not typically used in medicinal chemistry efforts, until one reacts with a protein target of exceptional interest. Structure-activity data can then be used to enhance the selectivity of conjugate formation or the covalent probe can be used as a competitor to develop noncovalent drug candidates. Here we use the "Inverse Drug Discovery" platform to identify and validate covalent ligands for 11 different human proteins. In the case of one of these proteins, we have identified and validated a small-molecule probe for the first time.
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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 | 0.001 |
| 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".