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The RBS Predictor is the core model of rbsXpress and is based on a deep-learning model (SAPIENs) that was trained on approximately 250,000 5’-UTR sequences experimentally characterized in Escherichia coli. It accepts 5’-UTR sequences as an input and predicts their relative translation rate (rTR) as main output. Furthermore, it can be used to generate input data for the library tool RedLibs (see below). More details on the prediction model and the underlying data generation can be found in the following publication:

Höllerer, S., Papaxanthos, L., Gumpinger, A. C., Fischer, K., Beisel, C., Borgwardt, K., Benenson, Y., & Jeschek, M. (2020). Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nature communications, 11(1), 3551 (https://doi.org/10.1038/s41467-020-17222-4)

RBS Generator

The RBS Generator is the forward design tool of rbsXpress and features custom design of 5’-UTR sequences with a designated rTR defined by the user. The sequence design is enabled by the genetic algorithm GRASP, which efficiently searches the accessible sequence space for 5’-UTRs with maximized, minimized or specific rTR values. More details on 5’-UTR design and the underlying GRASP algorithm can be found in the following publication:

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RedLibs

RedLibs is a tool for the generation of 5’-UTR libraries. It uses prediction data as input and computes degenerate sequences that encode several 5’-UTRs which uniformly span the accessible rTR range (i.e. from low to high strength). The user defines the number of individual 5’-UTRs encoded by the degenerate sequence (i.e. the library size). The resulting “smart” libraries can be simply cloned in bulk and subsequently used to systematically search for optimal expression levels, either for individual proteins or within multi-protein systems. More details on RedLibs as well as a use case in metabolic pathway optimization are described in the following publication:

Jeschek, M., Gerngross, D., & Panke, S. (2016). Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort. Nature communications, 7, 11163 (https://doi.org/10.1038/ncomms11163)

Tips and tricks

For inexperienced users, it is highly recommended to consult the Glossary and different mouseovers to familiarize yourself with 5’-UTR engineering and the provided tools.
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