Application of High-Throughput Isothermal Denaturation to Assess Protein Stability and Screen for Ligands
Bibliographic record
Abstract
Many diseases in humans are caused by mutations that decrease the stability of specific proteins or increase their susceptibility to aggregation. Consequently, the availability of high-throughput methods for assessing protein stability and aggregation properties under physiological conditions (e.g., 37 degrees C) is necessary to analyze physicochemical properties under conditions that are closer to in vivo models. Therefore, the authors have explored the use of isothermal denaturation (ITD) in a 384-well format to evaluate the reproducibility of the method in assessing the stability of proteins at temperatures below the melting temperature and detecting the binding of ligands. Under the conditions tested, the authors were able to assess the stability of citrate synthase and malate dehydrogenase at different constant temperatures and detect the binding of oxaloacetate and nicotinamide adenine dinucleotide to these 2 enzymes, respectively, using the 384-well format. The ITD experiments detected ligand binding to these proteins at about 4 times lower concentration compared with techniques that measure changes in melting temperature. The data show that ITD can be applied to screen libraries of a relatively large number of compounds or detect small stability differences between protein variants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".