Selection of the 5′‐proximal translation initiation site is influenced by mRNA and eIF‐2 concentrations is a research paper published in European Journal of Biochemistry (1990). On theSindex it has a DataRank of 3.0. It has been cited 55 times, with 52 citing works in its 1-hop citation network.
A cDNA clone of the influenza virus NS (non‐structural protein) gene in a vector carrying a bacteriophage T7 RNA polymerase promoter was manipulated so as to reiterate the initiation site to give two in‐frame AUG codons 57 nucleotide residues apart. Each initiation site was in either a preferred context (…AUAAUGG…) or a less favourable context (…UUUAUGG…) and the four possible permutations were constructed. When capped mRNA transcripts of these clones were translated in the rabbit reticulocyte lysate system, products from initiation at both AUG codons were observed. At low RNA concentrations the frequency of initiation at the 5′‐proximal AUG codon rather than the second was higher when the first AUG codon was in the preferred context, in qualitative agreement with the scanning ribosome model. However, a completely unexpected finding was that the ratio of initiation at the first AUG codon to initiation at the second decreased with increasing mRNA concentration, irrespective of the particular context involved. Several lines of evidence indicated that the increased frequency of initiation at the second AUG codon was not due solely to the lower density of ribosome loading per mRNA at high RNA concentrations, and may therefore be the result of high RNA concentrations out‐titring the capacity of endogenous reticulocyte factors responsible for preferential initiation at the 5′‐proximal AUG codon. The effect of supplementing the system with purified initiation factors was examined. Only eIF‐2 was capable of decreasing the frequency of initiation at the second AUG codon and promoting use of the first AUG at high mRNA concentrations; eIF‐3, 4A, 4B, 4C + 4D, 4F and 5 were inactive.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.604
From this paper's citation signal
Citation Network Contribution
2.3
From 47 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 20% comes from its base citations and 80% from the citation network (47 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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