Nanopore Analysis of the Effect of Metal Ions on the Folding of Peptides and Proteins
Bibliographic record
Abstract
In this minireview, the nanopore analysis of peptides and proteins in the presence of divalent metal ions will be surveyed. In all cases the binding of the metal ions causes the peptide or protein to adopt a more compact conformation which can no longer enter the α-hemolysin pore. In the absence of Zn(II) the 30-amino acid Zn-finger peptide can readily translocate the pore; but upon addition of Zn(II) the peptide folds and only bumping events are observed. Similarly, the octapeptide repeat from the N-terminus of the prion protein binds Cu(II), which prevents it from translocating. The full-length prion protein also undergoes conformational changes upon binding Cu(II), which results in an increase in the proportion of bumping events. Myelin basic protein of 170 residues is intrinsically disordered and, perhaps surprisingly, for a basic protein of this size, can translocate against the electric field based on the observation that the event time increases with increasing voltage. It, too, folds into a more compact conformation upon binding Cu(II) and Zn(II), which prevents translocation. Finally even proteins such as maltose binding protein which does not contain a formal binding site for metal ions undergoes conformational changes in the presence of the metal chelator, EDTA. Thus, contamination of proteins with trace metal ions should be considered when studying proteins and peptides by nanopore analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".