RNA Secondary Structures

Hofacker, Ivo L.; Stadler, Peter F.

doi:10.1002/9783527619368.ch14

Cited by 8 publications

(5 citation statements)

References 126 publications

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“…At this point, it needs to be mentioned that at room temperature, RNAs exist in an ensemble of structures and the MFE structure is not always the biologically relevant one. 32,33 There are several algorithms to predict these suboptimal secondary structures. [34][35][36] Most of the common secondary structure prediction methods assume that the functional RNA structure depends solely on the thermodynamic equilibrium and does not consider the kinetics of folding.…”

Section: Discussionmentioning

confidence: 99%

Novel Approach to Analyzing MFE of Noncoding RNA Sequences

George

Thomas

2016

Genomics Insights

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Genomic studies have become noncoding RNA (ncRNA) centric after the study of different genomes provided enormous information on ncRNA over the past decades. The function of ncRNA is decided by its secondary structure, and across organisms, the secondary structure is more conserved than the sequence itself. In this study, the optimal secondary structure or the minimum free energy (MFE) structure of ncRNA was found based on the thermodynamic nearest neighbor model. MFE of over 2600 ncRNA sequences was analyzed in view of its signal properties. Mathematical models linking MFE to the signal properties were found for each of the four classes of ncRNA analyzed. MFE values computed with the proposed models were in concordance with those obtained with the standard web servers. A total of 95% of the sequences analyzed had deviation of MFE values within ±15% relative to those obtained from standard web servers.

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Section: Discussionmentioning

confidence: 99%

Novel Approach to Analyzing MFE of Noncoding RNA Sequences

George

Thomas

2016

Genomics Insights

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“…Many programs addressed this by searching for sequences which minimize the distance between the target and the actual MFE structure [37,39,40,41] or the distance to the desired shape [42]. Zadeh et al [43] called this general approach MFE defect optimization and emphasized its limitation: the probability of the target structure, which is proportional to its Boltzmann factor [44], can still be small, due to alternative structures with similar stability in the ensemble of structural conformations. To circumvent this effect, the probability of the target structure in the ensemble can be directly computed and maximized [37,45].…”

Section: Design Goalsmentioning

confidence: 99%

Evolving methods for rational de novo design of functional RNA molecules

Hammer

Günzel

Mörl

et al. 2019

Methods

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Artificial RNA molecules with novel functionality have many applications in synthetic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review, we describe methods common to many such computational approaches, thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system, the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements.

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“…The primary structure folds to itself and leads to the formation of the secondary structure. The secondary structure is defined by weaker bonds (base-pair interactions) between two non-neighbour nucleotides due to the Watson-Crick base-pairs (A-U and G-C) and the wobble base-pair G-U [7].…”

Section: Rna Secondary Structurementioning

confidence: 99%

“…In general, the folding space for the RNA secondary structure starting with a sequence of n nucleotides has approximatively 1.8 n possible states, as defined by Equations (3)(4)(5)(6)(7) in [16]. Different strategies were developed to handle scalability issues.…”

Section: Implementation Issuesmentioning

confidence: 99%

A Graph Grammar for Modelling RNA Folding

Mamuye

Merelli

Tesei

2016

Electron. Proc. Theor. Comput. Sci.

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We propose a new approach for modelling the process of RNA folding as a graph transformation\ud guided by the global value of free energy. Since the folding process evolves towards a configuration\ud in which the free energy is minimal, the global behaviour resembles the one of a self-adaptive system.\ud Each RNA configuration is a graph and the evolution of configurations is constrained by precise rules\ud that can be described by a graph grammar

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RNA Secondary Structures

Cited by 8 publications

References 126 publications

Novel Approach to Analyzing MFE of Noncoding RNA Sequences

Novel Approach to Analyzing MFE of Noncoding RNA Sequences

Evolving methods for rational de novo design of functional RNA molecules

A Graph Grammar for Modelling RNA Folding

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