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

TL;DR

This paper introduces an efficient fixed-parameter tractable algorithm for sampling RNA sequences that can adopt multiple structures, including pseudoknots, improving design capabilities in bioinformatics.

Contribution

The authors present a novel tree decomposition-based algorithm for multi-target RNA design, supporting Boltzmann-weighted sampling and demonstrating significant improvements over previous methods.

Findings

Supports arbitrary additive RNA energy models

Enables generation of sequences with specific properties

Shows empirical improvements over uniform sampling

Abstract

The design of multi-stable RNA molecules has important applications in biology, medicine, and biotechnology. Synthetic design approaches profit strongly from effective in-silico methods, which can tremendously impact their cost and feasibility. We revisit a central ingredient of most in-silico design methods: the sampling of sequences for the design of multi-target structures, possibly including pseudoknots. For this task, we present the efficient, tree decomposition-based algorithm. Our fixed parameter tractable approach is underpinned by establishing the P-hardness of uniform sampling. Modeling the problem as a constraint network, our program supports generic Boltzmann-weighted sampling for arbitrary additive RNA energy models; this enables the generation of RNA sequences meeting specific goals like expected free energies or \GCb-content. Finally, we empirically study general properties of the approach and generate biologically relevant multi-target Boltzmann-weighted designs for a common design benchmark. Generating seed sequences with our program, we demonstrate significant improvements over the previously best multi-target sampling strategy (uniform sampling).Our software is freely available at: https://github.com/yannponty/RNARedPrint .