RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model

Jabbari, Hosna; Wark, Ian William; Montemagno, Carlo

doi:10.1371/journal.pone.0194583

Cited by 41 publications

(10 citation statements)

References 36 publications

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“…Given the role of the leader-repeat stem-loop in prioritizing immune defense for the S. pyogenes CRISPR-Cas9 system, we hypothesized that this mechanism would exist in many other CRISPR-Cas9 systems. The predicted interactions between the protruding loops of the leaderrepeat stem-loop and the second repeat for the S. pyogenes system are likely weaker, transient, and dependent on co-transcriptional folding 38 . However, the extensive stem formed between the leader RNA and first repeat offers a key feature that could be systematically predicted across CRISPR-Cas9 systems.…”

Section: Leader-repeat Stem-loops Found Across Crispr-cas9 Systemsmentioning

confidence: 99%

Spacer prioritization in CRISPR–Cas9 immunity is enabled by the leader RNA

et al. 2022

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CRISPR-Cas systems store fragments of foreign DNA called spacers as immunological recordings used to combat future infections. Of the many spacers stored in a CRISPR array, the newest spacers are known to be prioritized for immune defense. However, the underlying mechanism remains unclear. Here we show that the leader region upstream of CRISPR arrays in CRISPR-Cas9 systems enhances CRISPR RNA (crRNA) processing from the newest spacer, prioritizing defense against the matching invader. Using the CRISPR-Cas9 system from Streptococcus pyogenes as a model, we found that the transcribed leader interacts with the conserved repeats bordering the newest spacer. The resulting interaction promotes tracrRNA hybridization with the second repeat, accelerating crRNA processing. Accordingly, disrupting this structure reduces the abundance of the associated crRNA and immune defense against targeted plasmids and bacteriophages. Beyond the S. pyogenes system, bioinformatics analyses revealed that leader-repeat structures appear across CRISPR-Cas9 systems. CRISPR-Cas systems thus possess an RNA-based mechanism to prioritize defense against the most recently encountered invaders.

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Section: Leader-repeat Stem-loops Found Across Crispr-cas9 Systemsmentioning

confidence: 99%

Spacer prioritization in CRISPR–Cas9 immunity is enabled by the leader RNA

et al. 2022

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“…CCJ (Chen et al., 2009) and its efficient implementation, Knotty (Jabbari et al., 2018), are among the best‐performing MFE algorithms for prediction of pseudoknotted structures. These methods can handle kissing hairpin structures with arbitrary nested (possibly pseudoknotted) substructures as well as chains of four interleaved bands (or the ABACBDCD motif).…”

Section: Methodsmentioning

confidence: 99%

“…We say an algorithm can handle a structure if it is in the class of structures the algorithm can predict. We note that even if a structure is handled by an algorithm, the algorithm may not predict the structure, as correct structure prediction depends on the underlying energy model as well as the class of structures an algorithm can handle (Jabbari, Wark, & Montemagno, 2018).…”

Section: Methodsmentioning

confidence: 99%

Pseudoknots in RNA Structure Prediction

2023

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RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases.

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“…It has been shown that the accuracy of MFE RNA secondary structure prediction decreases with sequence length both for pseudoknot-free [ 35 ] and pseudoknotted structures [ 36 ]. This has motivated research on incorporating available data (e.g.…”

Section: Rna Secondary Structurementioning

confidence: 99%

KnotAli: informed energy minimization through the use of evolutionary information

2022

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Background Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and by the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters. Results We present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment as input and uses covariation and thermodynamic energy minimization to predict possibly pseudoknotted secondary structures for each individual sequence in the alignment. We compared KnotAli’s performance to that of three other alignment-based programs, two that can handle pseudoknotted structures and one control, on a large data set of 3034 RNA sequences with varying lengths and levels of sequence conservation from 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT). Conclusions We found KnotAli’s performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. While both KnotAli and Cacofold use background noise correction strategies, we found KnotAli’s predictions to be less dependent on the alignment quality. KnotAli can be found online at the Zenodo image: 10.5281/zenodo.5794719

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RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model

Cited by 41 publications

References 36 publications

Spacer prioritization in CRISPR–Cas9 immunity is enabled by the leader RNA

Spacer prioritization in CRISPR–Cas9 immunity is enabled by the leader RNA

Pseudoknots in RNA Structure Prediction

KnotAli: informed energy minimization through the use of evolutionary information

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