Multiplexed DNA and Protease Detection with Orthogonal Energy Transfer on a Single Quantum Dot Scaffolded Biosensor
Bibliographic record
Abstract
Almost all pathogens, whether viral or bacterial, utilize key proteolytic steps in their pathogenesis. The ability to detect a pathogen’s genomic material along with its proteolytic activity represents one approach to identifying the pathogen and providing initial evidence of its viability. Here, we report on a prototype biosensor design assembled around a single semiconductor quantum dot (QD) scaffold that is capable of detecting both nucleic acid sequences and proteolytic activity by using orthogonal energy transfer (ET) processes. The sensor consists of a central QD assembled via peptidyl-PNA linkers with multiple DNA sequences that encode complements to genomic sequences originating from the Ebola, Influenza, and COVID-19 viruses, which we use as surrogate targets. These are hybridized to complement strands labeled with a terbium (Tb) chelate, AlexaFluor647 (AF647), and Cy5.5 dyes, giving rise to two potential FRET cascades: the first includes Tb → QD → AF647 → Cy5.5 (→ = ET step), which is detected in a time-gated modality, and QD → AF647 → Cy5.5, which is detected from direct excitation. The labeled DNA-displaying QD construct is then further assembled with a Ru II -modified peptide, which quenches QD photoluminescence by charge transfer and is recognized by a protease to yield the full biosensor. Each of the labeled DNAs and peptides can be ratiometrically assembled to the QD in a controllable manner to tune each of the ET pathways. Addition of a given target DNA displaces its labeled complement on the QD, disrupting that FRET channel, while protease addition disrupts charge transfer quenching of the central QD scaffold and boosts its photoluminescence and FRET relay capabilities. Along with characterizing the ET pathways and verifying biosensing in both individual and multiplexed formats, we also demonstrate the ability of this construct to function in molecular logic and perform Boolean operations; this highlights the construct’s ability to discriminate and transduce signals between different inputs or pathogens. The potential application space for such a sensor device is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".