Biochemistry of fluoroprolines: the prospect of making fluorine a bioelement
Bibliographic record
Abstract
Due to the heterocyclic structure and distinct conformational profile, proline is unique in the repertoire of the 20 amino acids coded into proteins. Here, we summarize the biochemical work on the replacement of proline with (4R)- and (4S)-fluoroproline as well as 4,4-difluoroproline in proteins done mainly in the last two decades. We first recapitulate the complex position and biochemical fate of proline in the biochemistry of a cell, discuss the physicochemical properties of fluoroprolines, and overview the attempts to use these amino acids as proline replacements in studies of protein production and folding. Fluorinated proline replacements are able to elevate the protein expression speed and yields and improve the thermodynamic and kinetic folding profiles of individual proteins. In this context, fluoroprolines can be viewed as useful tools in the biotechnological toolbox. As a prospect, we envision that proteome-wide proline-to-fluoroproline substitutions could be possible. We suggest a hypothetical scenario for the use of laboratory evolutionary methods with fluoroprolines as a suitable vehicle to introduce fluorine into living cells. This approach may enable creation of synthetic cells endowed with artificial biodiversity, containing fluorine as a bioelement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".