Pateamine A mediates RNA sequence-selective translation repression by anchoring eIF4A and DDX3 to GNG motifs
Iwasaki, S.; Saito, H.; Handa, Y.; Chen, M.; Schneider-Poetsch, T.; Shichino, Y.; Takahashi, M.; Romo, D.; Yoshida, M.; Furstner, A.; Ito, T.; Fukuzawa, K.
Show abstract
Small-molecule compounds that elicit mRNA-selective translation repression have attracted interest due to their potential for expansion of druggable space. However, only limited examples have been reported to date. Here, we show that pateamine A (PatA) represses translation in an mRNA-selective manner by clamping eIF4A, a DEAD-box RNA-binding protein, on GNG motifs. Through a systematic comparison of multiple eIF4A inhibitors by ribosome profiling, we found that PatA has unique mRNA selectivity in translation repression. Unbiased Bind-n-Seq revealed that PatA-targeted eIF4A exhibits a sequence preference for GNG motifs in an ATP-independent manner. This unusual RNA binding sterically hinders scanning by 40S ribosomes. In silico simulation, combination of classical molecular dynamics simulation and quantum chemical calculation, and the subsequent development of an inactive PatA derivative revealed that the positive charge of the tertiary amine on the trienyl arm induces G selectivity. Moreover, we identified DDX3, another DEAD-box protein, as an alternative target of PatA, showing the same effect as on eIF4A. Our results provide an example of the sequence-selective anchoring of RNA-binding proteins and mRNA-selective inhibition of protein synthesis by small-molecule compounds.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Chemical-guided SHAPE sequencing (cgSHAPE-seq) informs the binding site of RNA-degrading chimeras targeting SARS-CoV-2 5' untranslated region 97%
- Co-transcriptional folding orchestrates sequential multi-effector sensing by a glycine tandem riboswitch 96%
- Chemically modified CRISPR-Cas9 enables targeting of individual G-quadruplex and i-motif structures, revealing ligand-dependent transcriptional perturbation. 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.