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The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data

Author

Listed:
  • Marc Parisien

    (Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, PO Box 6128, Downtown Station, Montréal, Québec H3C 3J7, Canada)

  • François Major

    (Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, PO Box 6128, Downtown Station, Montréal, Québec H3C 3J7, Canada)

Abstract

The classical RNA secondary structure model considers A·U and G·C Watson–Crick as well as G·U wobble base pairs. Here we substitute it for a new one, in which sets of nucleotide cyclic motifs define RNA structures. This model allows us to unify all base pairing energetic contributions in an effective scoring function to tackle the problem of RNA folding. We show how pipelining two computer algorithms based on nucleotide cyclic motifs, MC-Fold and MC-Sym, reproduces a series of experimentally determined RNA three-dimensional structures from the sequence. This demonstrates how crucial the consideration of all base-pairing interactions is in filling the gap between sequence and structure. We use the pipeline to define rules of precursor microRNA folding in double helices, despite the presence of a number of presumed mismatches and bulges, and to propose a new model of the human immunodeficiency virus-1 -1 frame-shifting element.

Suggested Citation

  • Marc Parisien & François Major, 2008. "The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data," Nature, Nature, vol. 452(7183), pages 51-55, March.
    • Handle: RePEc:nat:nature:v:452:y:2008:i:7183:d:10.1038_nature06684
    DOI: 10.1038/nature06684
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    Citations

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    Cited by:

    1. Jun Li & Wei Zhu & Jun Wang & Wenfei Li & Sheng Gong & Jian Zhang & Wei Wang, 2018. "RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-18, November.
    2. Michael F Sloma & David H Mathews, 2017. "Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-23, November.
    3. Antoine Soulé & Vladimir Reinharz & Roman Sarrazin-Gendron & Alain Denise & Jérôme Waldispühl, 2021. "Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-28, May.
    4. Wenkai Wang & Chenjie Feng & Renmin Han & Ziyi Wang & Lisha Ye & Zongyang Du & Hong Wei & Fa Zhang & Zhenling Peng & Jianyi Yang, 2023. "trRosettaRNA: automated prediction of RNA 3D structure with transformer network," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Jiaqiang Zhu & Wei Huang & Jing Zhao & Loc Huynh & Derek J. Taylor & Michael E. Harris, 2022. "Structural and mechanistic basis for recognition of alternative tRNA precursor substrates by bacterial ribonuclease P," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Carla L. Esposito & Ida Autiero & Annamaria Sandomenico & H. Li & Mahmoud A. Bassal & Maria L. Ibba & Dongfang Wang & Lucrezia Rinaldi & Simone Ummarino & Giulia Gaggi & Marta Borchiellini & Piotr Swi, 2023. "Targeted systematic evolution of an RNA platform neutralizing DNMT1 function and controlling DNA methylation," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    7. Ge Han & Yi Xue, 2022. "Rational design of hairpin RNA excited states reveals multi-step transitions," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Jun Li & Jian Zhang & Jun Wang & Wenfei Li & Wei Wang, 2016. "Structure Prediction of RNA Loops with a Probabilistic Approach," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-17, August.
    9. Jes Frellsen & Ida Moltke & Martin Thiim & Kanti V Mardia & Jesper Ferkinghoff-Borg & Thomas Hamelryck, 2009. "A Probabilistic Model of RNA Conformational Space," PLOS Computational Biology, Public Library of Science, vol. 5(6), pages 1-11, June.
    10. Mélanie Boudard & Julie Bernauer & Dominique Barth & Johanne Cohen & Alain Denise, 2015. "GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    11. Liang Liu & Shi-Jie Chen, 2012. "Coarse-Grained Prediction of RNA Loop Structures," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-15, November.

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