Label-free free-solution nanoaperture optical tweezers for single molecule protein studies
Bibliographic record
Abstract
Nanoaperture optical tweezers are emerging as useful label-free, free-solution tools for the detection and identification of biological molecules and their interactions at the single molecule level. Nanoaperture optical tweezers provide a low-cost, scalable, straight-forward, high-speed and highly sensitive (SNR ∼ 33) platform to observe real-time dynamics and to quantify binding kinetics of protein-small molecule interactions without the need to use tethers or labeling. Such nanoaperture-based optical tweezers, which are 1000 times more efficient than conventional optical tweezers, have been used to trap and isolate single DNA molecules and to study proteins like p53, which has been claimed to be in mutant form for 75% of human cancers. More recently, nanoaperture optical tweezers have been used to probe the low-frequency (in the single digit wavenumber range) Raman active modes of single nanoparticles and proteins. Here we review recent developments in the field of nanoaperture optical tweezers and how they have been applied to protein-antibody interactions, protein-small molecule interactions including single molecule binding kinetics, and protein-DNA interactions. In addition, recent works on the integration of nanoaperture optical tweezers at the tip of optical fiber and in microfluidic environments are presented.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".