Automated design of synthetic ribosome binding sites to control protein expression
Open accessAbstract
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.
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A fully rational approach to designing functional 5′ UTRs has not yet been implemented.
“While there has been some success in this effort in prokaryotes, yeast and even mammalian cells27,34–36, a fully rational approach to designing functional 5′ UTRs has not yet been implemented.”
From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [36]
The Materials and Methods section (Software Implementation; page unavailable) says the authors implemented an RBS Calculator that optimizes ribosome-binding-site sequences to rationally control protein expression. This contradicts the unqualified assertion that a rational design approach had not been implemented, although the paper addresses bacterial ribosome-binding sites rather than every feature of a 5′ UTR. (LLM, conf=0.77)
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Experimental — accuracy not yet measuredEvo's zero-shot correlation on promoter-RBS sequences exceeded GC content (r = 0.47), GenSLM (r = 0.11), and RBS Calculator (r = 0.39).
“Evo’s zero-shot correlation on promoter-RBS sequences is also higher than the GC content of the promoter-RBS sequences (Spearman r = 0.47), zero-shot GenSLM likelihoods (Spearman r = 0.11), and RBS Calculator (Spearman r = 0.39)”
From Sequence modeling and design from molecular to genome scale with Evo (2024) · cites this paper as [57]
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- Authors
- Salis, H. M., Mirsky, E. A., & Voigt, C. A.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Voigt, Christopher A
- Venue
- Nature Biotechnology
- Published
- 2009-10
- Volume
- 27
- Issue
- 10
- Pages
- 946-950
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC2782888
- OpenAlex
- W2047360542
- Cited by
- 1,844
- References
- 35
- Citations by year
- 2026: 94 · 2025: 94 · 2024: 116 · 2023: 94 · 2022: 112 · 2021: 114 · 2020: 124 · 2019: 144 · 2018: 155 · 2017: 129 · 2016: 129 · 2015: 108 · 2014: 113 · 2013: 113 · 2012: 103
- FWCI
- 27.22
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Eye Institute (EY016546, PN2EY016546, PN1EY016546)
- National Institute of Allergy and Infectious Diseases (R56AI067699, R01AI067699, AI067699)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- translation rate, design automation, synthetic gene circuits, metabolic pathway engineering, Escherichia coli, forward engineering, synthetic biology
- MeSH terms
- Algorithms, Binding Sites, Cloning, Molecular, DNA, Recombinant, Escherichia coli, Genetic Engineering, Ribosomal Proteins, Ribosomes, RNA, Messenger, Thermodynamics, Protein Biosynthesis, Reproducibility of Results, Escherichia coli Proteins
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