MODENA: a multi-objective RNA inverse folding. Artificially synthesized RNA molecules have recently come under study since such molecules have a potential for creating a variety of novel functional molecules. When designing artificial RNA sequences, secondary structure should be taken into account since functions of noncoding RNAs strongly depend on their structure. RNA inverse folding is a methodology for computationally exploring the RNA sequences folding into a user-given target structure. In the present study, we developed a multi-objective genetic algorithm, MODENA (Multi-Objective DEsign of Nucleic Acids), for RNA inverse folding. MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score. MODENA can simultaneously design multiple different RNA sequences at 1 run, whose lowest free energies range from a very stable value to a higher value near those of natural counterparts. MODENA and previous RNA inverse folding programs were benchmarked with 29 target structures taken from the Rfam database, and we found that MODENA can successfully design 23 RNA sequences folding into the target structures; this result is better than those of the other benchmarked RNA inverse folding programs. The multi-objective genetic algorithm gives a useful framework for a functional biomolecular design. Executable files of MODENA can be obtained at http://rna.eit.hirosaki-u.ac.jp/modena/.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Constantin, Elena: Second-order optimality conditions in locally Lipschitz inequality-constrained multiobjective optimization (2020)
- Jedwab, Jonathan; Petrie, Tara; Simon, Samuel: An infinite class of unsaturated rooted trees corresponding to designable RNA secondary structures (2020)
- Clote, Peter; Bayegan, Amir H.: RNA folding kinetics using Monte Carlo and Gillespie algorithms (2018)
- Haleš, Jozef; Héliou, Alice; Maňuch, Ján; Ponty, Yann; Stacho, Ladislav: Combinatorial RNA design: designability and structure-approximating algorithm in Watson-Crick and Nussinov-Jacobson energy models (2017)
- Gatter, Thomas; Giegerich, Robert; Saule, Cédric: Integrating Pareto optimization into dynamic programming (2016)