Dynamic Nuclear Polarization-Enabled Quantum Sensing for Investigating Peptide Configurations
Bibliographic record
Abstract
Abstract Introducing persistent free radicals into biochemical systems, a mechanism referred to as Site-Directed Spin Labeling (SDSL), is a biophysical research feat established by Wayne Hubbell. Integrating Double Electron-Electron Resonance (DEER) within SDSL enabled intermolecular distance measurements in the long range (nanometers) that was otherwise unfeasible using Nuclear Magnetic Resonance (NMR) techniques. DEER is commonly used in structural analysis of peptides and polymers to probe distance between electron spin-labels (ree), typically within the range of, 2.0 - 8.0 nanometers. However, this technique is typically limited to low magnetic fields, such as X-Band (0.35 T) or Q-Band ( 1.0 T) due to the instrumental constraints. The low magnetic field restricts both the structural resolution and the ability to probe shorter distance. We propose a novel approach to address the limitations of distance measurement in SDSL peptides or polymers. This approach utilizes Dynamic Nuclear Polarization (DNP) to investigate the distances between them. DNP is a nuclear spin hyperpolarization technique that has revolutionized solid-state NMR by enhancing its sensitivity. It works by transferring high electron spin polarization to coupled nuclear spins under microwave irradiation. The efficiency of Cross Effect DNP (CE DNP) transfer is determined by the magnitude of the dipole-dipole coupling between two electron spins. By exploiting the distance dependence between two electron spins, we can sense the configurations of SDSL peptides. One significant advantage of DNP-enabled quantum sensing is that the method can be applied at higher magnetic fields and under magic-angle spinning conditions using existing instrumentation. The approach enables the observation of shorter distances that are challenging to probe using DEER at low magnetic fields. Overall, our proposed method opens up new possibilities for structural analysis and distance measurements in the study of peptides and polymers, combining with ingenious SDSL technique introduced by the esteemed scientist, Professor Wayne Hubbell
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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 | 0.001 |
| 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".