FRET Imaging of Nonuniformly Distributed DNA SAMs on Gold Reveals the Role Played by the Donor/Acceptor Ratio and the Local Environment in Measuring the Rate of Hybridization
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mixed DNA SAMs labeled with a fluorophore (either AlexaFluor488 or AlexaFluor647) were prepared on a single crystal gold bead electrode using potential-assisted thiol exchange and studied using Förster resonance energy transfer (FRET). A measure of the local environment of the DNA SAM (e.g., crowding) was possible using FRET imaging on these surfaces since electrodes prepared this way have a range of surface densities (Γ DNA ). The FRET signal was strongly dependent on Γ DNA and on the ratio of AlexaFluor488 to AlexaFluor647 used to make the DNA SAM, which were consistent with a model of FRET in 2D systems. FRET was shown to provide a direct measure of the local DNA SAM arrangement on each crystallographic region of interest providing a direct assessment of the probe environment and its influence on the rate of hybridization. The kinetics of duplex formation for these DNA SAMs was also studied using FRET imaging over a range of coverages and DNA SAM compositions. Hybridization of the surface-bound DNA increased the average distance between the fluorophore label and the gold electrode surface and decreased the distance between the donor (D) and acceptor (A), both of which result in an increase in FRET intensity. This increase in FRET was modeled using a second order Langmuir adsorption rate equation, reflecting the fact that both D and A labeled DNA are required to become hybridized to observe a FRET signal. The self-consistent analysis of the hybridization rates on low and high coverage regions on the same electrode showed that the low coverage regions achieved full hybridization 5× faster than the higher coverage regions, approaching rates typically found in solution. The relative increase in FRET intensity from each region of interest was controlled by manipulating the donor to acceptor composition of the DNA SAM without changing the rate of hybridization. The FRET response can be optimized by controlling the coverage and the composition of the DNA SAM sensor surface and could be further improved with the use of a FRET pair with a larger (e.g., > 5 nm) Förster radius.
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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 | 0.001 |
| 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 | 0.001 | 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".