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Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools
BACKGROUND: Single-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634105/ https://www.ncbi.nlm.nih.gov/pubmed/37940855 http://dx.doi.org/10.1186/s12859-023-05532-5 |
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author | Binet, Thomas Padiolleau-Lefèvre, Séverine Octave, Stéphane Avalle, Bérangère Maffucci, Irene |
author_facet | Binet, Thomas Padiolleau-Lefèvre, Séverine Octave, Stéphane Avalle, Bérangère Maffucci, Irene |
author_sort | Binet, Thomas |
collection | PubMed |
description | BACKGROUND: Single-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure. RESULTS: The minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with [Formula: see text] of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only [Formula: see text] of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5–10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions. CONCLUSIONS: In general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05532-5. |
format | Online Article Text |
id | pubmed-10634105 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106341052023-11-10 Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools Binet, Thomas Padiolleau-Lefèvre, Séverine Octave, Stéphane Avalle, Bérangère Maffucci, Irene BMC Bioinformatics Research BACKGROUND: Single-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure. RESULTS: The minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with [Formula: see text] of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only [Formula: see text] of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5–10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions. CONCLUSIONS: In general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05532-5. BioMed Central 2023-11-08 /pmc/articles/PMC10634105/ /pubmed/37940855 http://dx.doi.org/10.1186/s12859-023-05532-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Binet, Thomas Padiolleau-Lefèvre, Séverine Octave, Stéphane Avalle, Bérangère Maffucci, Irene Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools |
title | Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools |
title_full | Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools |
title_fullStr | Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools |
title_full_unstemmed | Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools |
title_short | Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools |
title_sort | comparative study of single-stranded oligonucleotides secondary structure prediction tools |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634105/ https://www.ncbi.nlm.nih.gov/pubmed/37940855 http://dx.doi.org/10.1186/s12859-023-05532-5 |
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