Altering the targeting specificity of rocaglates: selective eIF4A inhibitors
Bibliographic record
Abstract
DEAD-box RNA helicases are the family of putative RNA helicases with 37 members in mammals and are characterized by the presence of a highly conserved Asp-Glu-Ala-Asp (DEAD) motif.DDX proteins are key players in all facets of RNA biology ranging from transcription to mRNA decay.However, the specific functions of most DDX helicases in various cellular processes are largely unknown.Dysregulation of these helicases has been associated with tumor cell maintenance.In light of this fact, significant efforts have been made over the years in developing small molecules against DEAD-box proteins to understand their role in physiological processes and the possibility of targeting these in various malignancies.DDX helicases have a structurally highly conserved core with various N-terminal and Cterminal flanking ends that determine their substrate specificity and function.Of all RNA helicases, eIF4A (DDX2) is the smallest and best characterized DEAD-box RNA helicase.It is a prototype of DDX helicases that plays a crucial role in translation initiation.Our lab studies three natural small molecule inhibitors targeting eIF4A, of which rocaglates are the best characterized, are potent and well-tolerated in vivo.Rocaglates clamp eIF4A onto the purine rich regions of RNA and inhibit global translation by blocking the ribosomal scanning towards the start codon and inhibiting the recruitment of 40S ribosome onto the RNA.They exert their effect by wedging themselves between two RNA bases and interacting with F163 and Q195 of eIF4A.Only DDX2 paralogs have F163 and Q195 residues at these positions, whereas other DEAD-box proteins have distinct residues at these key interacting positions.Taking this into an account and that the helicase core is highly conserved, we hypothesized that these helicases could be targeted by slightly modifying rocaglates at eIF4A1 interacting sites.Using structure-based drug design and chemical biology approach, we modified rocaglates to explore the possibility of broadening their
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".