Mapping shifts in nanopore signal to changes in protein and protein-DNA conformation
Bibliographic record
Abstract
ABSTRACT Solid-state nanopores have been used extensively in biomolecular studies involving DNA and proteins. However, the interpretation of signals generated by the translocation of proteins or protein-DNA complexes remains challenging. Here, we investigate the behavior of monovalent streptavidin and the complex it forms with short biotinylated DNA over a range of nanopore sizes, salts and voltages. We describe a simple geometric model that is broadly applicable and employ it to explain observed variations in conductance blockage and dwell time with experimental conditions. The general approach developed here underscores the value of nanopore-based protein analysis and represents progress toward the interpretation of complex translocation signals. STATEMENT OF SIGNIFICANCE Nanopore sensing allows investigation of biomolecular structure in aqueous solution, including electricfield-induced changes in protein conformation. This nanopore-based study probes: (1) the tetramerdimer transition of streptavidin, observing the effects of increasing voltage with varying salt type and concentration; (2) the possible conformational states of DNA-streptavidin complexes when confined inside a pore. We describe a broadly applicable geometric approach that maps stepwise changes in the nanopore signal to real-time conformational transitions. These results represent progress toward accurate interpretation of nanopore signals generated by molecular complexes.
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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) | 0.000 | 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".