Applications of isothermal titration calorimetry in pure and applied research from 2016 to 2020
Bibliographic record
Abstract
The last 5 years have seen a series of advances in the application of isothermal titration microcalorimetry (ITC) and interpretation of ITC data. ITC has played an invaluable role in understanding multiprotein complex formation including proteolysis-targeting chimeras (PROTACS), and mitochondrial autophagy receptor Nix interaction with LC3 and GABARAP. It has also helped elucidate complex allosteric communication in protein complexes like trp RNA-binding attenuation protein (TRAP) complex. Advances in kinetics analysis have enabled the calculation of kinetic rate constants from pre-existing ITC data sets. Diverse strategies have also been developed to study enzyme kinetics and enzyme-inhibitor interactions. ITC has also been applied to study small molecule solvent and solute interactions involved in extraction, separation, and purification applications including liquid-liquid separation and extractive distillation. Diverse applications of ITC have been developed from the analysis of protein instability at different temperatures, determination of enzyme kinetics in suspensions of living cells to the adsorption of uremic toxins from aqueous streams.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".