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Accuracy of RNA Structure Prediction Depends on the Pseudoknot Grammar

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Advances in Bioinformatics and Computational Biology (BSB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13523))

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Abstract

Most models for pseudoknotted RNA structures can be described by multi-context free grammars (MCFGs) and thus are amenable to dynamic programming algorithms. They differ strongly in their definition of admissible structures and thus the search space over which structures are optimized. The accuracy of structure prediction can be expected to depend on choice of the MCFG: models that are too inclusive likely over-predict pseudoknots, while restrictive models by their definition already exclude more complex pseudoknotted structures. A systematic analysis of the impact of the grammar, however, is difficult since available implementations use incomparable energy parameters. We show here that Algebraic Dynamic Programming over MCFGs naturally disentangles energy models (as specified by the evaluation algebra) and the definition of search space defined by a MCFG. Preliminary computational experiments indicate that the choice of the grammar has an important impact already for short RNA sequences.

This work was funded by the German DFG Collaborative Research Centre AquaDiva (CRC 1076 AquaDiva), the German state of Thuringia via the Thüringer Aufbaubank (2021 FGI 0009), the Carl-Zeiss-Stiftung within the program Scientific Breakthroughs in Artificial Intelligence (project “Interactive Inference”), and the German Federal Ministry of Education and Research (BMBF 031L0164C “RNAProNet”).

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Notes

  1. 1.

    Since we use here an energy model that is slightly simplified in the evaluation of certain loop terms compared to the full model implemented in ViennaRNA, occasionally we predict structures that are closer to structure model in the STRAND database and thus accuracy may also be (slightly) better than the ViennaRNA predictions.

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Author information

Authors and Affiliations

  1. Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany

    Dustyn Eggers, Christian Höner zu Siederdissen & Peter F. Stadler

  2. Theoretical Bioinformatics Lab, Bioinformatics/High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany

    Christian Höner zu Siederdissen

  3. Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany

    Peter F. Stadler

  4. Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria

    Peter F. Stadler

  5. Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia

    Peter F. Stadler

  6. Santa Fe Institute, Santa Fe, NM, USA

    Peter F. Stadler

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  1. Dustyn Eggers
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  2. Christian Höner zu Siederdissen
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  3. Peter F. Stadler
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Correspondence to Peter F. Stadler .

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Editors and Affiliations

  1. Instituto Nacional de Câncer, Rio de Janeiro, Brazil

    Nicole M. Scherer

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Raquel C. de Melo-Minardi

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Eggers, D., Höner zu Siederdissen, C., Stadler, P.F. (2022). Accuracy of RNA Structure Prediction Depends on the Pseudoknot Grammar. In: Scherer, N.M., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2022. Lecture Notes in Computer Science(), vol 13523. Springer, Cham. https://doi.org/10.1007/978-3-031-21175-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-21175-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21174-4

  • Online ISBN: 978-3-031-21175-1

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