Buffer Optimization of Thermal Melt Assays of Plasmodium Proteins for Detection of Small-Molecule Ligands
Bibliographic record
Abstract
In the past decade, thermal melt/thermal shift assays have become a common tool for identifying ligands and other factors that stabilize specific proteins. Increased stability is indicated by an increase in the protein's melting temperature (Tm). In optimizing the assays for subsequent screening of compound libraries, it is important to minimize the variability of Tm measurements so as to maximize the assay's ability to detect potential ligands. The authors present an investigation of Tm variability in recombinant proteins from Plasmodium parasites. Ligands of Plasmodium proteins are particularly interesting as potential starting points for drugs for malaria, and new drugs are urgently needed. A single standard buffer (100 mM HEPES [pH 7.5], 150 mM NaCl) permitted estimation of Tm for 58 of 61 Plasmodium proteins tested. However, with several proteins, Tm could not be measured with a consistency suitable for high-throughput screening unless alternative protein-specific buffers were employed. The authors conclude that buffer optimization to minimize variability in Tm measurements increases the success of thermal melt screens involving proteins for which a standard buffer is suboptimal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".