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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
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Optical Tweezers to Study RNA-Protein Interactions in Translation Regulation
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Frnakenstein: multiple target inverse RNA folding.

Rune B Lyngsø1, James W J Anderson, Elena Sizikova

  • 1Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.

BMC Bioinformatics
|October 10, 2012
PubMed
Summary

This study introduces Frnakenstein, a genetic algorithm for the inverse RNA folding problem. It efficiently designs RNA sequences for specific structures, including multi-target designs, outperforming existing methods without CG bias.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • RNA secondary structure prediction is a classic bioinformatics problem.
  • The inverse RNA folding problem, designing sequences for target structures, is gaining interest.
  • This inverse problem is computationally challenging.

Purpose of the Study:

  • To develop a genetic algorithm for the inverse RNA folding problem.
  • To address the multi-target inverse RNA folding problem.
  • To design sequences with superior quality for target structures.

Main Methods:

  • A genetic algorithm approach was implemented as a Python program named Frnakenstein.
  • The method was benchmarked against four existing methods.
  • Evaluated on 769 single structure targets and 292 two-structure targets.

Main Results:

  • Frnakenstein performed as well as or better than existing methods in finding sequences that fold into target structures.
  • The method avoided the heavy bias towards CG base pairs observed in other top-performing methods.
  • Achieved a perfect design for about 80% of two-structure targets.

Conclusions:

  • Successful inverse RNA folding designs do not require heavy biases in base pair distributions.
  • Design difficulty increases with larger real structures but not predicted structures.
  • Automated design of artificial riboswitches is feasible, even for complex two-structure targets.