Fourier Transform Infrared Spectroscopy of Calcium-Binding Proteins
Bibliographic record
Abstract
Infrared spectroscopy measures absorptions of vibrating molecules and yields information about molecular structure and structural interactions. Over the last two decades, the infrared technique has emerged as a very useful tool for examining protein conformation as a result of the increase in energy throughput, achievable signal-to-noise ratio, wavenumber accuracy, and data aquisition rates that came with the development of Fourier transform infrared (FTIR) spectrometers. High-quality infrared spectra can now rapidly be aquired and require only relatively small amounts of protein. The size of the protein or the nature of the environment does not limit the application of FTIR spectroscopy. Importantly, measurements of proteins in aqueous solution are almost routine now. Furthermore, the process of obtaining structural information is not restricted to a static picture, but can also be achieved in real time by applying time-resolved infrared techniques. The effects of environmental factors, point mutations, or ligand binding on the structure of the proteins can be examined with high sensitivity by using peptide backbone and side-chain infrared bands as conformation-sensitive monitors. In combination with isotope labeling, the technique also permits the study of protein-protein or protein-peptide interactions. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".