Atomic Resolution Map of Hierarchical Self-Assembly for an Amyloidogenic Protein Probed through Thermal <sup>15</sup> N–R <sub>2</sub> Correlation Matrices
Bibliographic record
Abstract
Soluble oligomers formed by amyloidogenic intrinsically disordered proteins are some of the most cytotoxic species linked to neurodegeneration. Due to the transient and heterogeneous nature of such oligomeric intermediates, the underlying self-association events often remain elusive. NMR relaxation measurements sensitive to zero-frequency spectral densities (J(0)), such as the 15 N - R 2 rates, are ideally suited to map sites of self-association at atomic resolution without the need of exogenous labels. Such experiments exploit the dynamic exchange between NMR visible monomers and slowly tumbling oligomers. However, 15 N - R 2 rates are also sensitive to intrinsic monomer dynamics, and it is often difficult to discern these contributions from those arising from exchange with oligomers. Another challenge pertains to defining a hierarchy of self-association. Here, using the archetypical amyloidogenic protein alpha synuclein (αS), we show that the temperature-dependence of 15 N - R 2 effectively identifies self-association sites with reduced bias from internal dynamics. The key signature of the residues involved in self-association is a nonlinear temperature-dependence of 15 N - R 2 with a positive ΔR 2 /ΔT slope. These two hallmarks are systematically probed through a thermal R 2 correlation matrix, from which the network of residues involved in self-association as well as the hierarchy of αS self-association sites is extracted through agglomerative clustering. We find that aggregation is initiated by residues within the NAC region that is solvent inaccessible in αS fibrils and eventually extends to the N-terminal segment harboring familial PD mutations. These hierarchical self-association maps help dissect the essential drivers of oligomerization and reveal how amyloid inhibitors affect oligomer formation.
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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 | 0.000 |
| 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".