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Predicting RNA H-type pseudoknots with the massively parallel genetic algorithm

B A Shapiro1, J C Wu

  • 1Image Processing Section, National Cancer Institute. Frederick Cancer Research and Development Center, National Institutes of Health, MD 21702, USA. bshapiro@ncifcrf.gov

Computer Applications in the Biosciences : CABIOS
|August 1, 1997
PubMed
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A genetic algorithm (GA) successfully predicted RNA tertiary structures, including H-type pseudoknots, for viral and bacteriophage sequences. This computational approach validates phylogenetic predictions for complex RNA folding.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • RNA folding is crucial for biological function.
  • Predicting complex RNA structures, like pseudoknots, remains a challenge.
  • Massively parallel computing offers new possibilities for RNA structure prediction.

Purpose of the Study:

  • To employ a genetic algorithm (GA) for predicting RNA tertiary structures.
  • To investigate the GA's capability in identifying H-type pseudoknots.
  • To validate GA predictions against known RNA structures.

Main Methods:

  • Utilized a genetic algorithm (GA) on a MasPar MP-2 supercomputer with 16,384 processors.
  • Applied the GA to diverse RNA sequences: turnip yellow mosaic virus (TYMV), satellite tobacco necrosis virus (STNV)-2 and STNV-I, and bacteriophage T2, T4, T6 gene 32 mRNA.

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  • Compared GA-predicted structures with phylogenetically supported tertiary structures.

    • Main Results:

    • Successfully predicted the existence of H-type pseudoknots in several RNA sequences.
    • The GA accurately folded the tRNA-like 3' end of TYMV RNA (82 nt).
    • GA predictions for STNV-2 (619 nt), STNV-I (622 nt), and bacteriophage gene 32 mRNAs (946-1340 nt) aligned with established tertiary structures.

    Conclusions:

    • The genetic algorithm is a powerful tool for RNA tertiary structure prediction.
    • GA-based prediction successfully identified complex structural motifs like H-type pseudoknots.
    • This computational method provides a reliable means to validate and discover RNA structures.