Analysis and Optimization of Saturation Transfer Difference NMR Experiments Designed to Map Early Self-Association Events in Amyloidogenic Peptides
Bibliographic record
Abstract
Saturation transfer difference (STD) methods recently have been proposed to be a promising tool for self-recognition mapping at residue and atomic resolution in amyloidogenic peptides. Despite the significant potential of the STD approach for systems undergoing oligomer/monomer (O/M) equilibria, a systematic analysis of the possible artifacts arising in this novel application of STD experiments is still lacking. Here, we have analyzed the STD method as applied to O/M peptides, and we have identified three major sources of possible biases: offset effects, intramonomer cross-relaxation, and partial spin-diffusion within the oligomers. For the purpose of quantitatively assessing these artifacts, we employed a comparative approach that relies on 1-D and 2-D STD data acquired at different saturation frequencies on samples with different peptide concentrations and filtration states. This artifact evaluation protocol was applied to the Abeta(12-28) model system, and all three types of artifacts appear to affect the measured STD spectra. In addition, we propose a method to minimize the biases introduced by these artifacts in the Halpha STD distributions used to obtain peptide self-recognition maps at residue resolution. This method relies on the averaging of STD data sets acquired at different saturation frequencies and provides results comparable to those independently obtained through other NMR pulse sequences that probe oligomerization, such as nonselective off-resonance relaxation experiments. The artifact evaluation protocol and the multiple frequencies averaging strategy proposed here are of general utility for the growing family of amyloidogenic peptides, as they provide a reliable analysis of STD spectra in terms of polypeptide self-recognition epitopes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".