3-Fluoro-4-hydroxyprolines: Synthesis, Conformational Analysis, and Stereoselective Recognition by the VHL E3 Ubiquitin Ligase for Targeted Protein Degradation
Bibliographic record
Abstract
Abstract Hydroxylation and fluorination of proline alters the pyrrolidine ring pucker and the trans:cis amide bond ratio in a stereochemistry-dependent fashion, affecting molecular recognition of proline-containing molecules by biological systems. While hydroxyprolines and fluoroprolines are common motifs in medicinal and biological chemistry, the synthesis and molecular properties of prolines containing both modifications, i.e., fluoro-hydroxyprolines, have not been described. Here we present a practical and facile synthesis of all four diastereoisomers of 3-fluoro-4-hydroxyprolines (F-Hyps), starting from readily available 4-oxo-l-proline derivatives. Small-molecule X-ray crystallography, NMR spectroscopy, and quantum mechanical calculations are consistent with fluorination at C3 having negligible effects on the hydrogen bond donor capacity of the C4 hydroxyl, but inverting the natural preference of Hyp from C4-exo to C4-endo pucker. In spite of this, F-Hyps still bind to the von Hippel–Lindau (VHL) E3 ligase, which naturally recognizes C4-exo Hyp in a stereoselective fashion. Co-crystal structures and electrostatic potential calculations support and rationalize the observed preferential recognition for (3R,4S)-F-Hyp over the corresponding (3S,4S) epimer by VHL. We show that (3R,4S)-F-Hyp provides bioisosteric Hyp substitution in both hypoxia-inducible factor 1 alpha (HIF-1α) substrate peptides and peptidomimetic ligands that form part of PROTAC (proteolysis targeting chimera) conjugates for targeted protein degradation. Despite a weakened affinity, Hyp substitution with (3S,4S)-F-Hyp within the PROTAC MZ1 led to Brd4-selective cellular degradation at concentrations >100-fold lower than the binary Kd for VHL. We anticipate that the disclosed chemistry of 3-fluoro-4-hydroxyprolines and their application as VHL ligands for targeted protein degradation will be of wide interest to medicinal organic chemists, chemical biologists, and drug discoverers alike.
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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) | 0.000 | 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 | 0.001 |
| 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".