RNA inverse folding using Monte Carlo tree search

Xiufeng Yang, Kazuki Yoshizoe, Akito Taneda, Koji Tsuda

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Background: Artificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases. Result: We present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding. Conclusion: MCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA.

Original languageEnglish
Article number468
JournalBMC bioinformatics
Issue number1
Publication statusPublished - Nov 6 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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