Evidence for a Functionally Relevant Rocaglamide Binding Site on the eIF4A–RNA Complex
Bibliographic record
Abstract
Translation initiation is an emerging target in oncology and neurobiology indications. Naturally derived and synthetic rocaglamide scaffolds have been used to interrogate this pathway; however, there is uncertainty regarding their precise mechanism(s) of action. We exploited the genetic tractability of yeast to define the primary effect of both a natural and a synthetic rocaglamide in a cellular context and characterized the molecular target using biochemical studies and in silico modeling. Chemogenomic profiling and mutagenesis in yeast identified the eIF (eukaryotic Initiation Factor) 4A helicase homologue as the primary molecular target of rocaglamides and defined a discrete set of residues near the RNA binding motif that confer resistance to both compounds. Three of the eIF4A mutations were characterized regarding their functional consequences on activity and response to rocaglamide inhibition. These data support a model whereby rocaglamides stabilize an eIF4A-RNA interaction to either alter the level and/or impair the activity of the eIF4F complex. Furthermore, in silico modeling supports the annotation of a binding pocket delineated by the RNA substrate and the residues identified from our mutagenesis screen. As expected from the high degree of conservation of the eukaryotic translation pathway, these observations are consistent with previous observations in mammalian model systems. Importantly, we demonstrate that the chemically distinct silvestrol and synthetic rocaglamides share a common mechanism of action, which will be critical for optimization of physiologically stable derivatives. Finally, these data confirm the value of the rocaglamide scaffold for exploring the impact of translational modulation on disease.
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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 | 0.000 | 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) | 0.002 | 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".