Single-molecule DNA detection with an engineered MspA protein nanopore
Bibliographic record
Abstract
Nanopores hold great promise as single-molecule analytical devices and biophysical model systems because the ionic current blockades they produce contain information about the identity, concentration, structure, and dynamics of target molecules. The porin MspA of Mycobacterium smegmatis has remarkable stability against environmental stresses and can be rationally modified based on its crystal structure. Further, MspA has a short and narrow channel constriction that is promising for DNA sequencing because it may enable improved characterization of short segments of a ssDNA molecule that is threaded through the pore. By eliminating the negative charge in the channel constriction, we designed and constructed an MspA mutant capable of electronically detecting and characterizing single molecules of ssDNA as they are electrophoretically driven through the pore. A second mutant with additional exchanges of negatively-charged residues for positively-charged residues in the vestibule region exhibited a factor of approximately 20 higher interaction rates, required only half as much voltage to observe interaction, and allowed ssDNA to reside in the vestibule approximately 100 times longer than the first mutant. Our results introduce MspA as a nanopore for nucleic acid analysis and highlight its potential as an engineerable platform for single-molecule detection and characterization applications.
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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) | 0.000 | 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 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".