Study of protein aggregation using two‐dimensional correlation infrared spectroscopy and spectral simulations
Bibliographic record
Abstract
Two-dimensional (2D) correlation spectroscopy establishes correlations between intensity variations in a series of spectra obtained by the application of an external perturbation. However, spectral effects (wavenumber shift or bandwidth change) are known to generate apparent asynchronisms in 2D maps. Surprisingly, spectral effects are often neglected in the literature when interpreting experimental maps, which can lead to erroneous conclusions. In an attempt to evaluate the contribution of these effects and that of true asynchronisms on 2D maps, the heat-induced aggregation of glutamyl-tRNA synthetase (GluRS) was studied as a typical example of the application of Fourier transform infrared (FTIR) spectroscopy in the amide I region. The data were compared with those obtained from a mutant protein that differs by one amino acid. To determine whether the aggregation mechanisms are identical for both proteins, the experimental 2D maps were compared to simulations based on curve fitting of the initial and final spectra of the series, which allows change in position and bandwidth of the components to be taken into account. Intermediate spectra were generated using a convenient function that mimics the spectral evolution. The speed and the delay of each component were controlled. Apart from the appearance of turns that occur for the mutant and not for GluRS, the aggregation mechanisms of both proteins seems to be essentially identical. In particular, the loss of alpha-helices seems to be concomitant with the formation of intermolecular beta-sheets, whereas the loss of intramolecular beta-sheets is delayed. Since the experimental maps are satisfactorily simulated when almost all the components are in phase, it appears that many of the asynchronous features are mainly due to spectral effects. Thus, one has to be aware that true asynchronisms are not necessarily at the origin of peaks observed in asynchronous maps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".