Frnakenstein: multiple target inverse RNA folding

BMC Bioinformatics

Research Article

Amarendra Badugu¹  Elena Sizikova²  Jotun Hein³  Rune B Lyngsø³  James WJ Anderson³  Tomas Hyland⁴ 

[1] Department of Biosystems Science and Engineering, ETH Zürich, 4058, Basel, Switzerland;Department of Computer Science, University of Oxford, OX1 3QD, Oxford, UK;Department of Statistics, University of Oxford, OX1 3TG, Oxford, UK;Mathematics Institute, University of Oxford, OX1 3LB, Oxford, UK;

关键词: RNA;    Inverse folding;    Genetic algorithm;    Riboswitch;   

DOI  :  10.1186/1471-2105-13-260

 received in 2012-07-07, accepted in 2012-09-23,  发布年份 2012

来源: Springer

【 摘 要 】

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 athttp://www.stats.ox.ac.uk/research/genome/software/frnakenstein.

【 授权许可】

Unknown   
© Lyngsø et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.