Conformational Changes Spanning Angstroms to Nanometers via a Combined Protein-Induced Fluorescence Enhancement–Förster Resonance Energy Transfer Method
Bibliographic record
Abstract
Förster resonance energy transfer (FRET)-based single-molecule techniques have revolutionized our understanding of conformational dynamics in biomolecular systems. Recently, a new single-molecule technique based on protein-induced fluorescence enhancement (PIFE) has aided studies in which minimal (<3 nm) displacements occur. Concerns have been raised regarding whether donor fluorophore intensity (and correspondingly fluorescence quantum yield Φ f ) fluctuations, intrinsic to PIFE methods, may adversely affect FRET studies when retrieving the donor–acceptor dye distance. Here, we initially show through revisions of Förster’s original equation that distances may be calculated in FRET experiments regardless of protein-induced intensity (and Φ f ) fluctuations occurring in the donor fluorophore. We additionally demonstrate by an analysis of the recorded emission intensity and competing decay pathways that PIFE and FRET methods may be conveniently combined, providing parallel complementary information in a single experiment. Single-molecule studies conducted with Cy3- and ATTO647N-labeled RNA structures and the HCV-NS5B polymerase protein undergoing binding dynamics along the RNA backbone provide a case study to validate the results. The analysis behind the proposed method enables for PIFE and FRET changes to be disentangled when both FRET and PIFE fluctuate over time following protein arrival and, for example, sliding. A new method, intensity-FRET, is thus proposed to monitor conformational changes spanning from angstroms to nanometers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".