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The design of multi-stable RNA molecules has important applications inbiology, medicine, and biotechnology. Synthetic design approaches profitstrongly from effective in-silico methods, which can tremendously impact theircost and feasibility. We revisit a central ingredient of most in-silico designmethods: the sampling of sequences for the design of multi-target structures,possibly including pseudoknots. For this task, we present the efficient, treedecomposition-based algorithm. Our fixed parameter tractable approach isunderpinned by establishing the P-hardness of uniform sampling. Modeling theproblem as a constraint network, our program supports genericBoltzmann-weighted sampling for arbitrary additive RNA energy models; thisenables the generation of RNA sequences meeting specific goals like expectedfree energies or \GCb-content. Finally, we empirically study general propertiesof the approach and generate biologically relevant multi-targetBoltzmann-weighted designs for a common design benchmark. Generating seedsequences with our program, we demonstrate significant improvements over thepreviously best multi-target sampling strategy (uniform sampling).Our softwareis freely available at: https://github.com/yannponty/RNARedPrint .

Fixed-Parameter Tractable Sampling for RNA Design with Multiple Target Structures