Bibliographic record
Abstract
The application of nanopore sensing utilizing the α-hemolysin pore to probe proteins at single-molecule resolution has expanded rapidly. In some studies protein translocation through the α-hemolysin has been reported. However, there is no direct evidence, as yet, that proteins can translocate the α-hemolysin pore. The biggest challenge to obtaining direct evidence is the lack of a highly sensitive assay to detect very low numbers of protein molecules. Furthermore, if an activity based assay is applied then the proteins translocating by unfolding should refold back to an active confirmation for the assay technique to work. To overcome these challenges we selected a model enzyme, ribonuclease A, that readily refolds to an active conformation even after unfolding it with denaturants. In addition we have developed a highly sensitive reverse transcription polymerase chain reaction based activity assay for ribonuclease A. Initially, ribonuclease A, a protein with a positive net charge and dimensions larger than the smallest diameter of the pore, was subjected to nanopore analysis under different experimental conditions. Surprisingly, although the protein was added to the cis chamber (grounded) and a positive potential was applied, the interaction of ribonuclease A with α-hemolysin pore induced small and large blockade events in the presence and the absence of a reducing and/or denaturing agent. Upon measuring the zeta potential, it was found that the protein undergoes a charge reversal under the experimental conditions used for nanopore sensing. From the investigation of the effect of voltage on the interaction of ribonuclease A with the α-hemolysin pore, it was impossible to conclude if the events observed were translocations. However, upon testing for ribonuclease A activity on the trans chamber it was found that ribonuclease A does not translocate the α-hemolysin pore.
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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 | 0.000 | 0.000 |
| 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".