Trapping, unfolding, identifying, and binding single proteins using the double-nanohole optical trap
Bibliographic record
Abstract
In this paper we describe the double nanohole laser tweezer system used to trap single nanoparticles. We cover the basic theory behind the DNH and what makes it more powerful than traditional laser tweezers commonly used for larger particles. We outline the basic setup used to reliably trap several different types of particles ranging in size from 1 nm to 40 nm. Data from several experiments is shown which displays exactly how a particle is confirmed to be trapped. We will discuss the use of autocorrelation as well as other information that can be extracted from the optical transmission in our setup and how it has been applied to the identification of protein small molecule interactions and protein binding. Other uses of the data collected from our setup will be discussed including the observation of protein folding. Finally we discuss the current developments of the process and its possible uses as a drug discovery tool, a new type of single particle nanopipette and new bio-sensors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".