Investigating Protein Binding with the Isothermal Ligand‐induced Resolubilization Assay
Bibliographic record
Abstract
Target engagement assays typically detect and quantify the direct physical interaction of a protein of interest and its ligand through stability changes upon ligand binding. Commonly used target engagement methods detect ligand-induced stability by subjecting samples to thermal or proteolytic stress. Here we describe a new variation to these approaches called Isothermal Ligand-induced Resolubilization Assay (ILIRA), which utilizes lyotropic solubility stress to measure ligand binding through changes in target protein solubility. We identified distinct buffer systems and salt concentrations that compromised protein solubility for four diverse proteins: dihydrofolate reductase (DHFR), nucleoside diphosphate-linked moiety X motif 5 (NUDT5), poly [ADP-ribose] polymerase 1 (PARP1), and protein arginine N-methyltransferase 1 (PRMT1). Ligand-induced solubility rescue was demonstrated for these proteins, suggesting that ILIRA can be used as an additional target engagement technique. Differences in ligand-induced protein solubility were assessed by Coomassie blue staining for SDS-PAGE and dot blot, as well as by NanoOrange, Thioflavin T, and Proteostat fluorescence, thus offering flexibility for readout and assay throughput.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".