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Structure and Interaction Prediction in Prokaryotic RNA Biology

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  • Authors: Patrick R. Wright*1, Martin Mann*2, Rolf Backofen*3
  • Editors: Gisela Storz5, Kai Papenfort6
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Bioinformatics Group; 2: Bioinformatics Group; 3: Bioinformatics Group; 4: Center for Biological Signaling Studies (BIOSS), University of Freiburg, Freiburg, Germany; 5: Division of Molecular and Cellular Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD; 6: Department of Biology I, Microbiology, LMU Munich, Martinsried, Germany
  • Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
  • Received 27 June 2017 Accepted 02 January 2018 Published 20 April 2018
  • Rolf Backofen, backofen@informatik.uni-freiburg.de
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  • Abstract:

    Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in “wet” laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.

  • Citation: Wright* P, Mann* M, Backofen* R. 2018. Structure and Interaction Prediction in Prokaryotic RNA Biology. Microbiol Spectrum 6(2):RWR-0001-2017. doi:10.1128/microbiolspec.RWR-0001-2017.

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/content/journal/microbiolspec/10.1128/microbiolspec.RWR-0001-2017
2018-04-20
2018-05-26

Abstract:

Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in “wet” laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.

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Figures

Image of FIGURE 1
FIGURE 1

Loop decomposition of a nested RNA structure into hairpin loops (H; dark gray), multiloops (M; light gray), stackings (S; light blue), bulges (B; dark blue), and interior loops (I; dark blue). Initials of the loop types are placed in white next to the enclosing base pair (black).

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
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Image of FIGURE 2
FIGURE 2

Graphical depiction of the Nussinov-like recursion from equation 1 .

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
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Image of FIGURE 3
FIGURE 3

Potential interactions for two identical RNA molecules (blue and green) as predicted by the RNAhybrid web server (A) ( 52 ) and IntaRNA web server (B) ( 53 ) (intramolecular base pairs subsequently added, which form a kissing hairpin interaction). Inter- and intramolecular base pairs are indicated by vertical pipe symbols and arches, respectively.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
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FIGURE 4

Recursion depiction of interaction prediction via (A) and (B).

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
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FIGURE 5

(A) Intramolecular structure-enclosing interaction predictable by concatenation-based approaches. (B) Double kissing hairpin interaction that be predicted since it forms a pseudoknot when linked. The red dotted line denotes the linker, and blue and green denote the first and second sequences, respectively. Base pairs are indicated by pipe or dash symbols.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
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FIGURE 6

The ensemble-based approach for interaction prediction. Instead of considering only a single individual structure for the RNAs and , RNAup and IntaRNA introduce a sequence-specific accessibility term, which represents all structures with an accessible (i.e., not covered by intramolecular base pairs) interaction site .. and ... These are incorporated into a modified duplex calculation.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
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FIGURE 7

(Left) accessibility scenario without consideration of RNA-binding factors (A, B). Here, region .. is accessible, while ′..′ is blocked by intramolecular base pairs. (Right) Putative situation with bound factors A and B. The accessible site .. is blocked by A, while ′..′ becomes accessible due to structural reconfiguration upon binding of A and B.

Source: microbiolspec April 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.RWR-0001-2017
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