A novel high-throughput screen for identifying lipids that stabilise membrane proteins in detergent based solution
Bibliographic record
Abstract
Membrane proteins have a range of crucial biological functions and are the target of about 60% of all prescribed drugs. For most studies, they need to be extracted out of the lipid-bilayer, e.g. by detergent solubilisation, leading to the loss of native lipids, which may disturb important protein-lipid/bilayer interactions and thus functional and structural integrity. Relipidation of membrane proteins has proven extremely successful for studying challenging targets, but the identification of suitable lipids can be expensive and laborious. Therefore, we developed a screen to aid the high-throughput identification of beneficial lipids. The screen covers a large lipid space and was designed to be suitable for a range of stability assessment methods. Here, we demonstrate its use as a tool for identifying stabilising lipids for three membrane proteins: a bacterial pyrophosphatase (Tm-PPase), a fungal purine transporter (UapA) and a human GPCR (A2AR). A2AR is stabilised by cholesteryl hemisuccinate, a lipid well known to stabilise GPCRs, validating the approach. Additionally, our screen also identified a range of new lipids which stabilised our test proteins, providing a starting point for further investigation and demonstrating its value as a novel tool for membrane protein research. The pre-dispensed screen will be made commercially available to the scientific community in future and has a number of potential applications in the field.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".