Theoretical framework and experimental demonstration of sustainable in vitro regeneration of major translation factors EF-Tu and IF3
Shoji, K.;Hagino, K.;Ichihashi, N.
Show abstract
The development of molecular systems capable of self-regeneration, much like living organisms, is a major goal in synthetic biology. Previous efforts to expand the repertoire of regenerating proteins have relied on empirical optimization without a theoretical foundation, fundamentally limiting the scalability of this approach. Here, we developed the first theoretical framework, based on measurable parameters of translational proteins, to rationally predict the dilution rate that enables sustainable regeneration and the steady-state translation level. Using this framework, we successfully demonstrated sustainable regeneration of EF-Tu, the most abundant translation factor, for up to 15 rounds of serial dilution. We further extended this framework to the co-regeneration of multiple translation factors and demonstrated sustainable co-regeneration of EF-Tu and IF3, another major essential translation protein, for up to 20 rounds. These results establish a rational methodology for systematically expanding the number of regenerating proteins, providing a clear path toward the realization of a fully self-regenerating system.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Transcription initiation at a consensus bacterial promoter proceeds via a "bind-unwind-load-and-lock" mechanism 94%
- Pleomorphic effects of three small-molecule inhibitors on transcription elongation by Mycobacterium tuberculosis RNA polymerase 94%
- Ribosome demand links transcriptional bursts to protein expression noise 94%
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.