Single-Molecule Force-Clamp Spectroscopy: Dwell Time Analysis and Practical Considerations
Bibliographic record
Abstract
Single-molecule force-clamp spectroscopy has become a powerful tool for studying protein folding/unfolding, bond rupture, and enzymatic reactions. Different methods have been developed to analyze force-clamp spectroscopy data on polyproteins to obtain kinetic parameters characterizing the mechanical unfolding of proteins, which are often modeled as a two-state process (a Poisson process). However, because of the finite number of domains in polyproteins, the statistical analysis of the force-clamp spectroscopy data is different from that of a classical Poisson process, and the equivalency of different analysis methods remains to be proven. In this article, we show that these methods are equivalent and lead to accurate measurements of the unfolding rate constant. We also demonstrate that distinct from the constant-pulling-velocity experiments, in which the unfolding rate extracted from the data is dependent on the number of protein domains in the polyproteins (the N effect), force-clamp experiments do not show any N effect. Using a simulated data set, we also highlighted important practical considerations that one needs to take into account when using the single-molecule force-clamp spectroscopy technique to characterize the unfolding energy landscape of proteins.
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 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".