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Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools.

Thomas Binet1, Séverine Padiolleau-Lefèvre1, Stéphane Octave1

  • 1Université de technologie de Compiègne, UPJV, CNRS, Enzyme and Cell Engineering, Centre de recherche Royallieu - CS 60 319, 60203, Compiègne Cedex, France.

BMC Bioinformatics
|November 8, 2023
PubMed
Summary

Choosing the right tool for predicting single-stranded nucleic acid (ssNA) secondary structures is crucial. Mfold, RNAfold, and MXfold2 offer the best performance, with AI tools showing promise for complex structures like pseudoknots.

Keywords:
BenchmarkPredictionSecondary structureSingle-stranded oligonucleotides

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Single-stranded nucleic acids (ssNAs) play vital biological roles and have significant biotechnological potential due to their diverse spatial conformations.
  • The secondary structure, or base pairing pattern, is the primary determinant of ssNA spatial organization.
  • Numerous computational tools exist for predicting ssNA secondary structures, creating a need for a comparative guide on their efficacy and limitations.

Purpose of the Study:

  • To evaluate and compare the performance of nine freely available computational tools for predicting ssNA secondary structures.
  • To identify the most accurate and reliable tools for secondary structure prediction based on experimental data.

Main Methods:

  • A comparative analysis of nine ssNA secondary structure prediction tools: mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2.
  • Performance evaluation was conducted on a dataset comprising 538 ssNAs with experimentally determined secondary structures.

Main Results:

  • Minimum free energy (MFE) based tools (mfold, RNAfold) and artificial intelligence (AI) tools (CONTRAfold, MXfold2) demonstrated the highest accuracy in exact predictions.
  • MC-fold exhibited the lowest performance.
  • UFold and SPOT-RNA were the only tools capable of predicting pseudoknots.
  • Considering suboptimal solutions improved the accuracy of mfold and RNAfold.
  • Challenges remain in predicting specific motifs like multi-way junctions and mini-dumbbells, and structures in complex with proteins.

Conclusions:

  • Mfold, RNAfold, and MXfold2 are currently recommended for general ssNA secondary structure prediction, despite limitations with specific motifs.
  • AI-driven tools like UFold and SPOT-RNA show significant potential for overcoming prediction challenges, particularly for pseudoknots, and represent a promising future direction.