Optimization of the prostaglandin F2α receptor for structural biology
Bibliographic record
Abstract
Prostaglandin F2ɑ (PGF2ɑ) is a bioactive lipid derived from arachidonic acid and is involved in many physiological and pathophysiological processes, such as parturition, vascular tone regulation, glaucoma and inflammation. It acts by binding to the Prostaglandin F2ɑ receptor (FP), a G Protein-Coupled Receptor (GPCR) that mediates signaling events by engaging intracellular heterotrimeric G protein effectors. The orthosteric binding site of lipid-binding receptors displays greater efficacy-dependent plasticity that hinders the design of ligands. Solving the structure of FP with ligands of different efficacies at an atomic level is important to fully understand its mechanism of activation and inhibition. Most purified FP-ligand complexes are unstable in vitro. The development of new X-ray crystallography and single particle cryo-electron microscopy (cryoEM) strategies to understand receptors' signal transduction requires improved purification yield and in vitro stability of the receptor. Here, we present a protein engineering effort to optimize the FP protein sequence for use in structural biology. Strategies involve protein insertion sites in the third intracellular loop (ICL3), N-terminal and C-terminal deletions, and single-point mutations that favorably affect receptor purification yield and stability in vitro. The best FP construct displays a yield of 1.5 mg/L and a stability of 59oC, which constitute a threefold improvement in purification yield and 9oC increase in stability over the wild-type receptor. These modifications in the receptor are suitable for pursuing alternative strategies for improving FP purification yield and for studying FP binding efficacy to its ligands through structural biology approaches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.002 |
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".