Developing Complex RNA Design Applications in the Infrared Framework.

Methods Mol Biol

LIX, CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France.

Published: May 2024

AI Article Synopsis

  • The text discusses the need for effective strategies to design novel RNAs in biotechnology and biomedical research, emphasizing the role of computational tools for flexibility and specificity.
  • The computational framework Infrared is introduced, which allows users to develop customized RNA design tools efficiently using simple Python code.
  • The framework is explained in a tutorial format, guiding users from basic RNA sequence generation to complex designs, including a practical application where Infrared is used to create an artificial "AND" riboswitch for gene expression activation based on metabolite presence.

Article Abstract

Applications in biotechnology and bio-medical research call for effective strategies to design novel RNAs with very specific properties. Such advanced design tasks require support by computational tools but at the same time put high demands on their flexibility and expressivity to model the application-specific requirements. To address such demands, we present the computational framework Infrared. It supports developing advanced customized design tools, which generate RNA sequences with specific properties, often in a few lines of Python code. This text guides the reader in tutorial format through the development of complex design applications. Thanks to the declarative, compositional approach of Infrared, we can describe this development as a step-by-step extension of an elementary design task. Thus, we start with generating sequences that are compatible with a single RNA structure and go all the way to RNA design targeting complex positive and negative design objectives with respect to single or even multiple target structures. Finally, we present a "real-world" application of computational design to create an RNA device for biotechnology: we use Infrared to generate design candidates of an artificial "AND" riboswitch, which activates gene expression in the simultaneous presence of two different small metabolites. In these applications, we exploit that the system can generate, in an efficient (fixed-parameter tractable) way, multiple diverse designs that satisfy a number of constraints and have high quality w.r.t. to an objective (by sampling from a Boltzmann distribution).

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Source
http://dx.doi.org/10.1007/978-1-0716-3519-3_12DOI Listing

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