Development of an inhibitory TTC7B selective nanobody that blocks EFR3 recruitment of PI4KA
Bibliographic record
Abstract
Phosphatidylinositol 4 kinase IIIα (PI4KIIIα/PI4KA) is an essential lipid kinase that plays a critical role in regulating plasma membrane (PM) identity. PI4KA is primarily recruited to the PM through the targeted recruitment by the proteins, EFR3A and EFR3B, which bind to the PI4KA accessory proteins, TTC7 (TTC7A/B) and FAM126 (FAM126A/B). Here, we characterized how both EFR3 isoforms interact with all possible TTC7-FAM126 combinations and developed a nanobody that specifically blocked EFR3-mediated PI4KA recruitment in TTC7B-containing complexes. Most EFR3-TTC7-FAM126 combinations show similar binding affinities, with the exception of EFR3A-TTC7B-FAM126A, which binds with a ∼10-fold higher affinity. Moreover, we showed that EFR3B phosphorylation markedly decreased binding to TTC7-FAM126. Using a yeast display approach, we isolated a TTC7B selective nanobody that blocked EFR3 binding. Cryo-EM and hydrogen deuterium exchange mass spectrometry showed an extended interface with both PI4KA and TTC7B that sterically blocks EFR3 binding. The nanobody caused decreased membrane recruitment both on lipid bilayers and in cells, with decreased PM production of phosphatidylinositol 4-phosphate. Collectively, these findings provide new insights into PI4KA regulation and provide a tool for manipulating PI4KA complexes, which may be valuable for therapeutic targeting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".