Automated Design of Synthetic Ribosome Binding Sites to Precisely Control Protein Expression

By Howard M. Salis, E. Mirsky, Christopher A. Voigt. Nature Biotechnology, 2009

1,087 citations recorded by the source corpus.

Microbial engineering often requires fine control over protein expression—for example, to connect genetic circuits or control flux through a metabolic pathway. To circumvent the need for trial and error optimization, we developed a predictive method for designing synthetic ribosome binding sites, enabling a rational control over the protein expression level. Experimental validation of >100 predictions in Escherichia coli showed that the method is accurate to within a factor of 2.3 over a range of 100,000-fold. The design method also correctly predicted that reusing identical ribosome binding site sequences in different genetic contexts can result in different protein expression levels. We demonstrate the method's utility by rationally optimizing protein expression to connect a genetic sensor to a synthetic circuit. The proposed forward engineering approach should accelerate the construction and systematic optimization of large genetic systems.

Subfields: Cardiology and Cardiovascular Medicine, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d1408d57d5b7c338d523a048d5e3569f6f6c5825

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Citations in the corpus

Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. (2014, methodology): “Our experimental results are shown in red, and the predicted values based on a thermodynamic model considering the sequence surrounding translation initiation sites are shown in blue (Salis et al., 2009).”
Synthetically-primed adaptation of Pseudomonas putida to a non-native substrate D-xylose (2024, neutral): “S1 shows the patient/sample ﬂ ow diagram.”
PromoterPredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between promoter strength and sequence (2018, background): “Such an understanding could be vital in the prediction of promoter strengths in different contexts, as well as the forward design of promoters in ﬁ nely-tuned genetic circuits (see Endy, 2005 ; De Mey et al., 2007 ; Salis, Mirsky & Voigt, 2009 ; Li & Zhang, 2014 ).”
Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences (2017, neutral): “coli the impact of a ribosome binding site on translation (Salis et al. 2009) or to understand how combinations of promoters and ribosome binding sites affect RNA and protein expression (Kosuri et al.”