Pyrene Excimer Formation (PEF) and Its Application to the Study of Polypeptide Dynamics
Bibliographic record
Abstract
This Perspective describes how the fluorescence blob model (FBM) has been developed and applied over the past 30 years to characterize the long-range backbone dynamics (LRBD) of polymers in solution. In these experiments, the polymers are randomly labeled with the dye pyrene, which forms an excimer upon the encounter between an excited and a ground-state pyrenyl label inside a finite subvolume of the polymer coil referred to as a blob representing the volume probed by the excited pyrene. By compartmentalizing the polymer coil into a cluster of identical blobs, FBM analysis of the fluorescence decays acquired with the polymers yields the number N blob of structural units inside a blob. Since a flexible or rigid backbone will result in an N blob that is either large or small, N blob can be used as a measure of the flexibility of a given polymer. After having established that these experiments based on pyrene excimer formation (PEF) yielded quantitative information about the LRBD of a variety of polymers in solution, control experiments were carried out to characterize the effects that different molecular variables, such as the side-chain size (SCS) of a structural unit or the length of the linker connecting pyrene to the polymeric backbone, had on the parameters retrieved with the FBM. At this point, the FBM was applied to study the LRBD of polypeptides prepared from racemic mixtures of amino acids (aa’s). These studies led to the establishment of simple rules that could be developed into mathematical equations to describe the LRBD of polypeptides. The N blob values retrieved from the FBM analysis of the fluorescence decays acquired with the pyrene-labeled polypeptides could then be employed to predict the total conformational search time (τ tcs ) of any polypeptide based on their sequence. Strong correlations were found between the predicted τ tcs and the experimental folding times of 145 proteins. The good quality of these correlations suggests that the blob-based approach described in this report might represent an interesting mathematical means for studying protein folding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".