Assessing the Stability of Membrane Proteins to Detect Ligand Binding Using Differential Static Light Scattering
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Bibliographic record
Abstract
Protein stabilization upon ligand binding has frequently been used to identify ligands for soluble proteins. Methods such as differential scanning fluorimetry (DSF) and differential static light scattering (DSLS) have been employed in the 384-well format and have been useful in identifying ligands that promote crystallization and 3D structure determination of proteins. However, finding a generic method that is applicable to membrane proteins has been a challenge as the high hydrophobicity of membrane proteins and the presence of detergents essential for their solubilization interfere with fluorescence-based detections. Here the authors used MsbA (an adenosine triphosphate binding cassette transporter), CorA (a Mg++ channel), and CpxA (a histidine kinase) as model proteins and show that DSLS is not sensitive to the presence of detergents or protein hydrophobicity and can be used to monitor thermodenaturation of membrane proteins, assess their stability, and detect ligand binding in a 384-well format. Protein stabilization upon ligand binding has frequently been used to identify ligands for soluble proteins. Methods such as differential scanning fluorimetry (DSF) and differential static light scattering (DSLS) have been employed in the 384-well format and have been useful in identifying ligands that promote crystallization and 3D structure determination of proteins. However, finding a generic method that is applicable to membrane proteins has been a challenge as the high hydrophobicity of membrane proteins and the presence of detergents essential for their solubilization interfere with fluorescence-based detections. Here the authors used MsbA (an adenosine triphosphate binding cassette transporter), CorA (a Mg++ channel), and CpxA (a histidine kinase) as model proteins and show that DSLS is not sensitive to the presence of detergents or protein hydrophobicity and can be used to monitor thermodenaturation of membrane proteins, assess their stability, and detect ligand binding in a 384-well format.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it