Abstract
RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i) a new crossover operator is implemented and (ii) pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/.
Highlights
Evolutionary related non-coding RNAs have their own characteristic secondary structure corresponding to each function, and it is well known that the secondary structures play key roles in the functions of the RNA sequences
BENCHMARK RESULTS We evaluated the pseudoknot design performance of MODENA with the Pseudobase dataset, where IPknot and Hotknots were used as a direct problem solver
Important differences between the current version which can design pseudoknots and the previous pseudoknot-free version are as follows. (i) We utilize a new structural n-point crossover operator in the current version, by which we can generate child solutions without breaking complementary relationships in parent solutions even when pseudoknots are included in the target structure. (ii) We allow MODENA to use pseudoknotted RNA structure prediction methods as direct problem solver
Summary
Evolutionary related non-coding RNAs have their own characteristic secondary structure corresponding to each function, and it is well known that the secondary structures play key roles in the functions of the RNA sequences. This biochemical knowledge accumulated to date indicates that we can generate functional synthetic RNAs if we can control the secondary structure of the RNAs. This biochemical knowledge accumulated to date indicates that we can generate functional synthetic RNAs if we can control the secondary structure of the RNAs In this context, various synthetic RNAs, such as ribozymes (Schultes and Bartel, 2000), micro RNAs (Schwab et al, 2006), riboswitches (Breaker, 2004), and RNA nano structures (Jaeger et al, 2001) have been successfully designed. We can find the following six RNA inverse folding algorithms in literature: local search algorithms [RNAinverse (Hofacker et al, 1994), RNASSD (Andronescu et al, 2004), INFO-RNA (Busch and Backofen, 2006), Inv (Gao et al, 2010), design in NUPACK (Zadeh et al, 2011)] and a genetic algorithm [GA; MODENA (Taneda, 2011)]
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