Characterizing an engineered release factor capable of reading all three stop codons (569.2) is a research paper published in The FASEB Journal (2014). On theSindex it has a DataRank of 0.141. It has been cited 1 time, with 1 citing works in its 1-hop citation network.
Translation termination is the last step of protein synthesis where the newly synthesized protein is released from the ribosome. For this, a class I release factor (RF1 and RF2) in bacteria binds to one of the three stop codons (UAA, UAG and UGA) and hydrolyze the ester bond between nascent protein and the peptidyl‐tRNA. RF1 and RF2, although similar to each other, read the stop codons in a semi specific manner i.e. RF1 reads UAA and UAG, and RF2 reads UAA and UGA codons. According to current literature [1] these factors discriminate highly against sense codons. Although molecular dynamics simulation experiments [2] have proposed a network of interactions through which RF1 and RF2 recognizes their respective stop codons, there exist very little biochemical experiments to explain how this high specificity is achieved. Using site directed mutagenesis we generated several variants of RF1 and RF2 and characterized those in competition and kinetic assays. One of our engineered release factors showed omnipotent property in stop codon recognition. We have characterized this engineered release factor with respect to other related codons especially in comparison with other release factors. Further we were able to validate our biochemical experiments using computer simulations. Our experiments pinpointed the crucial residues on the class‐I release factors for stop codon specificity and enabled us to engineer a class I release factor capable of reading all three stop codons. Grant Funding Source : Supported by; Swedish Research Council, Sven and Lilly Lawski Foundation
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Base Score Contribution
0.104
From this paper's citation signal
Citation Network Contribution
0.0366
From 1 citing papers with measurable signal
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 74% comes from its base citations and 26% from the citation network (1 citing paper 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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