Frnakenstein: multiple target inverse RNA folding

Abstract

BackgroundRNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard.ResultsIn this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets.ConclusionsOur method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein.

Principles for Predicting RNA Secondary Structure Design Difficulty

“…Prospective wet-lab tests of these insights, expanding the in silico tests presented here, are possible with Eterna’s current massively parallel experimental pipeline, which tests 10,000 designs per monthly round and will provide the most useful framework for future RNA engineering efforts. Finally, multi-state riboswitch design puzzle creation and solving tools (see also [23–25]) are now available in Eterna, along with large-scale experimental tests. A similar study to the present one detailing features that increase the difficulty of riboswitches and providing a benchmark for switch puzzles is an important next investigation.…”

Computational design of RNAs with complex energy landscapes

“…48 For the same reason, the base pair probabilities in the equilibrium ensemble give no indication of important structural alternatives. Because of its extreme properties, the SV11 structure pair has been used repeatedly as an example, including for design tasks 30 whose goal is to find a sequence that realizes the two conformations with nearly equal energy. In Figure 6, we show that our software readily solves this computational problem.…”

RNA design using simulated SHAPE data

“…MODENA (Taneda, 2011) applies a multi-objective genetic algorithm in which a set of weak Pareto optimal sequences are generated based on the combination of structural stability and similarity scores. Frnakenstein (Lyngsø et al, 2012) used a genetic algorithm based on multiple structural constraints. GGI-Fold (Ganjtabesh et al, 2013) also uses a multi-objective genetic algorithm to design sub-sequences for sub-structures derived from the target structure.…”