Gene architectures that minimize cost of gene expression

Frumkin, Idan; Schirman, Dvir; Rotman, Aviv; Li, Fangfei; Zahavi, Liron; Mordret, Ernest; Asraf, Omer; Wu, Song; Levy, Sasha F.; Pilpel, Yitzhak

doi:10.1016/j.molcel.2021.05.007

Cited by 8 publications

(17 citation statements)

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“…Frumkin et al (2017) measured the fitness effects of these random peptide-tagged GFPs in E. coli using FitSeq (Li, et al 2018). For 89 of them, Frumkin et al (2017) were able to calculate a “fitness residual” based on the deviation from the fitness expected from the level of GFP expression. Note that while this fitness residual controls for expression level, it still contains the cost of inefficient expression in addition to the fitness effect of the peptide itself.…”

Section: Resultsmentioning

confidence: 99%

“…Note that while this fitness residual controls for expression level, it still contains the cost of inefficient expression in addition to the fitness effect of the peptide itself. Frumkin et al (2017) found that low fitness residuals were associated with hydrophobic and expensive-to-synthesize amino acids. These findings are consistent with our own estimates of direct peptide effects, as hydrophobic amino acids tend to be order-prone (Linding, et al 2004; Angyan, et al 2012), and amino acid volume is highly correlated with synthesis cost in E. coli (Pearson’s correlation coefficient = 0.85, P = 2 × 10 −6 , cost for amino acid synthesis in E. coli taken from (Akashi and Gojobori 2002)).…”

Section: Resultsmentioning

confidence: 99%

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Random peptides rich in small and disorder-promoting amino acids are less likely to be harmful

Kosinski

Aviles

Gomez

et al. 2020

Preprint

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Proteins' great potential for harm, via misfolding and aggregation, does not prevent them from being born de novo from non-coding DNA. To investigate how newborn proteins might "first, do no harm", we estimate fitnesses from an experiment that competed Escherichia coli lineages that each expressed a unique random peptide. A variety of peptide metrics significantly predict lineage fitness, but almost all this predictive power stems from simple amino acid composition. Low fitness is predicted by hydrophobic and positively charged residues, positive net charge, aggregation prone regions, and underdispersed hydrophobic residues, while high fitness is predicted by disorder-promoting, negatively charged, small residues, and negative net charge. The same amino acids that predict high fitness in E. coli are enriched in young Pfams in animals, but not in plants. To modify Jacques Monod's famous quote, what makes peptides benign in E.coli also makes them benign in elephants, but not in eucalyptus.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Random peptides rich in small and disorder-promoting amino acids are less likely to be harmful

Kosinski

Aviles

Gomez

et al. 2020

Preprint

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“…This allowed us to identify two key parameters of the system, namely, the smoothed elongation rate λ 0 immediately following initiation and the minimal smoothed elongation rate λ min . Previous studies have established some association between the sequence context in the early 5 coding region and protein production levels (Frumkin et al, 2017;Boël et al, 2016;Ben-Yehezkel et al, 2015). For example, it has been shown that mRNA secondary structure in the first ∼ 16 codons (which locally decreases the elongation rate) negatively affects the translation rate in E. Coli, while no significant contribution of mRNA folding in other regions was found (Frumkin et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have established some association between the sequence context in the early 5 coding region and protein production levels (Frumkin et al, 2017;Boël et al, 2016;Ben-Yehezkel et al, 2015). For example, it has been shown that mRNA secondary structure in the first ∼ 16 codons (which locally decreases the elongation rate) negatively affects the translation rate in E. Coli, while no significant contribution of mRNA folding in other regions was found (Frumkin et al, 2017). By exposing α and λ 0 as the only parameters that currents in LD depend on, our analysis suggests a direct explanation for such contrast.…”

Section: Discussionmentioning

confidence: 99%

The Key Parameters that Govern Translation Efficiency

2020

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Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. Here, we study a stochastic model describing the traffic flow of ribosomes along the mRNA (namely, the inhomogeneous -TASEP), and identify the key parameters that govern the overall rate of protein synthesis, sensitivity to initiation rate changes, and efficiency of ribosome usage. By analyzing a continuum limit of the model, we obtain closed-form expressions for stationary currents and ribosomal densities, which agree well with Monte Carlo simulations. Furthermore, we completely characterize the phase transitions in the system, and by applying our theoretical results, we formulate design principles that detail how to tune the key parameters we identified to optimize translation efficiency. Using ribosome profiling data from S. cerevisiae, we shows that its translation system is generally consistent with these principles. Our theoretical results have implications for evolutionary biology, as well as synthetic biology.

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“…Recent investigations on NCS toolbox development have mainly been conducted in prokaryotes, such as E. coli and B. subtilis , where high‐throughput datasets and statistical analysis were applied. [ 3–8,37–40 ] Abundant evidence shows that both N‐terminal fragments from endogenous genes and randomly synthesized NCSs regulate gene translation levels efficiently and effectively. [ 3,4,6,8 ] However, an NCS toolbox for S. cerevisiae has not yet been developed.…”

Section: Discussionmentioning

confidence: 99%

Model‐driven design of synthetic N‐terminal coding sequences for regulating gene expression in yeast and bacteria

Wang

Zhang

Tian

et al. 2022

Biotechnology Journal

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N‐terminal coding sequences (NCSs) are key regulatory elements for fine‐tuning gene expression during translation initiation—the rate‐limiting step of translation. However, owing to the complex combinatory effects of NCS biophysical factors and endogenous regulation, designing NCSs remains challenging. In this study, a multi‐view learning strategy for model‐driven generation of synthetic NCSs for Saccharomyces cerevisiae and Bacillus subtilis are implemented, which are widely used in laboratories and industries. NCS libraries for S. cerevisiae and B. subtilis with nearly 150,000 cells were sorted. Next, model training was performed with NCS deep features extracted from DNA, codon, and amino acid sequences, as well as calculated features from the minimum free energy (MFE) and tRNA adaption index. Two models were separately developed for generating synthetic NCSs for both up‐ and down‐regulating gene expression with accuracies higher than 65% for S. cerevisiae and B. subtilis. Synthetic NCSs were then applied to enhance bioproduction, yielding 1.48‐ and 1.71‐fold production improvements of D‐limonene by S. cerevisiae and ovalbumin by B. subtilis, respectively. This work provides model‐driven design of synthetic NCSs as a toolbox for regulating gene expression in S. cerevisiae and B. subtilis. The machine learning‐based modeling approach can be used for NCS design in other microorganisms.

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Gene architectures that minimize cost of gene expression

Abstract: In our manuscript, we characterized various mechanisms by which bacterial cells express similar levels of proteins in an efficient manner that reduces production costs in terms of energy and building block usage.

Cited by 8 publications

References 0 publications

Random peptides rich in small and disorder-promoting amino acids are less likely to be harmful

Random peptides rich in small and disorder-promoting amino acids are less likely to be harmful

The Key Parameters that Govern Translation Efficiency

Model‐driven design of synthetic N‐terminal coding sequences for regulating gene expression in yeast and bacteria

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