Frankenstein: multiple target inverse RNA folding
dc.contributor.author
Lyngso, Rune B.
dc.contributor.author
Anderson, James W.J.
dc.contributor.author
Sizikova, Elena
dc.contributor.author
Badugu, Amarendra
dc.contributor.author
Hyland, Tomas
dc.contributor.author
Hein, Jotun
dc.date.accessioned
2018-09-04T10:50:21Z
dc.date.available
2017-06-10T13:24:03Z
dc.date.available
2018-09-04T10:50:21Z
dc.date.issued
2012-10
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/1471-2105-13-260
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/62743
dc.identifier.doi
10.3929/ethz-b-000062743
dc.description.abstract
Background
RNA 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.
Results
In 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.
Conclusions
Our 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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
RNA
en_US
dc.subject
Inverse folding
en_US
dc.subject
Genetic algorithm
en_US
dc.subject
Riboswitch
en_US
dc.title
Frankenstein: multiple target inverse RNA folding
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
13
en_US
ethz.pages.start
260
en_US
ethz.size
12 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-10T13:26:26Z
ethz.source
ECIT
ethz.identifier.importid
imp5936504a584c032045
ethz.ecitpid
pub:99617
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-14T17:37:33Z
ethz.rosetta.lastUpdated
2024-02-02T05:54:53Z
ethz.rosetta.versionExported
true
ethz.COinS
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