A New Algorithm for RNA Secondary Structure Design

Andronescu, Mirela; Fejes, Anthony P.; Hutter, Frank; Hoos, Holger H.; Condon, Anne

doi:10.1016/j.jmb.2003.12.041

Cited by 146 publications

(169 citation statements)

References 27 publications

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“…S34a) to specify a design as a set of one or more static target secondary structures. 8 The sequences of the constituent strands are then typically designed [9][10][11] by optimizing an objective function that captures some combination of affinity and/or specificity for the target structures. 12 By contrast, we design the dynamic function encoded in a self-assembly system by programming the reaction pathway of the system.…”

Section: S71 Leakage and Ligationmentioning

confidence: 99%

Programming biomolecular self-assembly pathways

Yin

Choi

Calvert

et al. 2008

Nature

1,386

1,169

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Section: S71 Leakage and Ligationmentioning

confidence: 99%

Programming biomolecular self-assembly pathways

Yin

Choi

Calvert

et al. 2008

Nature

1,386

1,169

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“…The dataset is referred as "Multi-stable" (DS-MS). For the evaluation, we derived a multi-stable sequence designer named "StochSrchMulti", a variant of RNAdesigner Andronescu et al (2004) which performed the best among existing single-state designers in the design space of interest Ramlan (2009). This variant algorithm is extended with the multi-stable conformation sequence design approached proposed by Flamm et al (2001).…”

Section: Evaluating the Performance Of Multi-stable Sequence Designersmentioning

confidence: 99%

“…Thus, for a particular conformation of an RNA molecule, there are always many sequences that conform to it Schultes et al (1998); Schuster (2006); Schuster et al (1994). Random walks and stochastic approaches have been implemented to solve the sequence design problem as exemplified in RNAinverse from the Vienna RNA package Hofacker et al (1994), RNAdesigner from the RNAsoft suite Andronescu et al (2004) and INFO-RNA Busch and Backofen (2006). Commonly, these approaches rely on the singular folding prediction acting as a reference point during the optimisation process and explicitly avoid the occurrence of alternative conformations.…”

Section: Introductionmentioning

confidence: 99%

Design of interacting multi-stable nucleic acids for molecular information processing

Ramlan

Zauner

2011

Biosystems

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Abstract. Despite an exponential increase in computing power over the past decades, present information technology falls far short of expectations in areas such as cognitive systems and micro robotics. Organisms demonstrate that it is possible to implement information processing in a radically different way from what we have available in present technology, and that there are clear advantages from the perspective of power consumption, integration density, and real-time processing of ambiguous data. Accordingly, the question whether the current silicon substrate and associated computing paradigm is the most suitable approach to all types of computation has come to the fore. Macromolecular materials, so successfully employed by nature, possess uniquely promising properties as an alternate substrate for information processing. The two key features of macromolecules are their conformational dynamics and their self-assembly capabilities. The purposeful design of macromolecules capable of exploiting these features has proven to be a challenge, however, for some groups of molecules it is increasingly practicable. We here introduce an algorithm capable of designing groups self-assembling of nucleic acid molecules with multiple conformational states. Evaluation using natural and artificially designed nucleic acid molecules favours this algorithm significantly, as compared to the probabilistic approach. Furthermore, the thermodynamic properties of the generated candidates are within the same approximation as the customised trans-acting switching molecules reported in the laboratory.

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“…We perform our algorithm on 29 RNA structures extracted from database Rfam, known as a standard benchmark (Taneda, 2011) and 34 RNA structures from RNA-SSD dataset (Andronescu et al, 2004). We compare our algorithm with some well-known algorithms such as INFO-RNA (Busch and Backofen, 2006), ERD (EsmailiTaheri et al, 2014), MODENA (Taneda, 2011) and RNAifold 2.0 (Garcia-Martin et al, 2015) on these datasets.…”

Section: Introductionmentioning

confidence: 99%

RNA design using simulated SHAPE data

2017

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It has long been established that in addition to being involved in protein translation, RNA plays essential roles in numerous other cellular processes, including gene regulation and DNA replication. Such roles are known to be dictated by higher-order structures of RNA molecules. It is therefore of prime importance to find an RNA sequence that can fold to acquire a particular function that is desirable for use in pharmaceuticals and basic research. The challenge of finding an RNA sequence for a given structure is known as the RNA design problem. Although there are several algorithms to solve this problem, they mainly consider hard constraints, such as minimum free energy, to evaluate the predicted sequences. Recently, SHAPE data has emerged as a new soft constraint for RNA secondary structure prediction. To take advantage of this new experimental constraint, we report here a new method for accurate design of RNA sequences based on their secondary structures using SHAPE data as pseudo-free energy. We then compare our algorithm with the four others: INFO-RNA, ERD, MODENA and RNAifold 2.0. Our algorithm precisely predicts 26 out of 29 new sequences for the structures extracted from the Rfam dataset, while the other four algorithms predict no more than 22 out of 29. The proposed algorithm is comparable to the above algorithms on RNA-SSD datasets, where they can predict up to 33 appropriate sequences for RNA secondary structures out of 34.

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A New Algorithm for RNA Secondary Structure Design

Cited by 146 publications

References 27 publications

Programming biomolecular self-assembly pathways

Programming biomolecular self-assembly pathways

Design of interacting multi-stable nucleic acids for molecular information processing

RNA design using simulated SHAPE data

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