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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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RNA triplet repeats: improved algorithms for structure prediction and interactions.

Kimon Boehmer1, Sarah J Berkemer1,2, Sebastian Will1

  • 1Laboratoire d'Informatique de l'Ecole Polytechnique (LIX CNRS UMR 7161), France, Institut Polytechnique de Paris, 1 Rue Honoré d'Estienne d'Orves, Palaiseau, 91120, France.

Algorithms for Molecular Biology : AMB
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

Synthetic biology research introduces triplet repeat (TR) RNAs. New algorithms improve minimum free energy (MFE) calculations for RNA structures and interactions, enhancing computational efficiency and enabling orthogonality assessments.

Keywords:
Dynamic programmingNP-hardnessRNA foldingRNA interactionsTriplet repeats

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Area of Science:

  • Computational biology
  • Synthetic biology
  • RNA structure prediction

Background:

  • Triplet repeat (TR) RNAs are gaining attention in synthetic biology.
  • Efficient computation of RNA secondary structures and interactions is crucial.

Purpose of the Study:

  • To develop improved algorithms for minimum free energy (MFE) secondary structure prediction of TR RNAs.
  • To design efficient algorithms for RNA-RNA interaction analysis, including MFE and partition function computation.
  • To investigate the computational complexity and design parameterized algorithms for TR RNA interactions.

Main Methods:

  • Developed improved algorithms for MFE and partition function computation of single RNA strands.
  • Designed a novel algorithm for RNA-RNA interaction analysis, reducing time complexity from factorial to exponential.
  • Established computational hardness for TR RNA interactions but provided a parameterized algorithm.
  • Proposed a polynomial-time algorithm for RNA strand interactions and conducted experimental analysis.

Main Results:

  • Achieved improved computational efficiency for MFE and partition function calculations.
  • Significantly improved the running time for RNA-RNA interaction computations.
  • Demonstrated computational hardness for TR RNA interactions while offering a viable parameterized solution.
  • Enabled experimental assessment of TR RNA interactions and orthogonality through a polynomial-time algorithm.

Conclusions:

  • The developed algorithms offer enhanced computational performance for RNA structure and interaction analysis.
  • The study provides a foundation for understanding and designing complex RNA systems, particularly those involving TR RNAs.
  • The proposed methods facilitate the assessment of orthogonality in TR-based RNA designs for synthetic biology applications.