Cysteine-Specific Labeling of Proteins with a Nitroxide Biradical for Dynamic Nuclear Polarization NMR
Bibliographic record
Abstract
Dynamic nuclear polarization (DNP) enhances the signal in solid-state NMR of proteins by transferring polarization from electronic spins to the nuclear spins of interest. Typically, both the protein and an exogenous source of electronic spins, such as a biradical, are either codissolved or suspended and then frozen in a glycerol/water glassy matrix to achieve a homogeneous distribution. While the use of such a matrix protects the protein upon freezing, it also reduces the available sample volume (by ca. a factor of 4 in our experiments) and causes proportional NMR signal loss. Here we demonstrate an alternative approach that does not rely on dispersing the DNP agent in a glassy matrix. We synthesize a new biradical, ToSMTSL, which is based on the known DNP agent TOTAPOL, but also contains a thiol-specific methanethiosulfonate group to allow for incorporating this biradical into a protein in a site-directed manner. ToSMTSL was characterized by EPR and tested for DNP of a heptahelical transmembrane protein, Anabaena sensory rhodopsin (ASR), by covalent modification of solvent-exposed cysteine residues in two (15)N-labeled ASR mutants. DNP enhancements were measured at 400 MHz/263 GHz NMR/EPR frequencies for a series of samples prepared in deuterated and protonated buffers and with varied biradical/protein ratios. While the maximum DNP enhancement of 15 obtained in these samples is comparable to that observed for an ASR sample cosuspended with ~17 mM TOTAPOL in a glycerol-d8/D2O/H2O matrix, the achievable sensitivity would be 4-fold greater due to the gain in the filling factor. We anticipate that the DNP enhancements could be further improved by optimizing the biradical structure. The use of covalently attached biradicals would broaden the applicability of DNP NMR to structural studies of proteins.
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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 | 0.000 | 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 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".