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Multiple native states reveal persistent ruggedness of an RNA folding landscape.

ABSTRACT: According to the 'thermodynamic hypothesis', the sequence of a biological macromolecule defines its folded, active (or 'native') structure as a global energy minimum in the folding landscape. However, the enormous complexity of folding landscapes of large macromolecules raises the question of whether there is in fact a unique global minimum corresponding to a unique native conformation or whether there are deep local minima corresponding to alternative active conformations. The folding of many proteins is well described by two-state models, leading to highly simplified representations of protein folding landscapes with a single native conformation. Nevertheless, accumulating experimental evidence suggests a more complex topology of folding landscapes with multiple active conformations that can take seconds or longer to interconvert. Here we demonstrate, using single-molecule experiments, that an RNA enzyme folds into multiple distinct native states that interconvert on a timescale much longer than that of catalysis. These data demonstrate that severe ruggedness of RNA folding landscapes extends into conformational space occupied by native conformations.

SUBMITTER: Solomatin SV

PROVIDER: S-EPMC2818749 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Multiple native states reveal persistent ruggedness of an RNA folding landscape.

Solomatin Sergey V SV Greenfeld Max M Chu Steven S Herschlag Daniel D

Nature 20100201 7281

According to the 'thermodynamic hypothesis', the sequence of a biological macromolecule defines its folded, active (or 'native') structure as a global energy minimum in the folding landscape. However, the enormous complexity of folding landscapes of large macromolecules raises the question of whether there is in fact a unique global minimum corresponding to a unique native conformation or whether there are deep local minima corresponding to alternative active conformations. The folding of many p ...[more]

PMID: 20130651

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Project description:BackgroundRNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard.ResultsIn this paper we present a genetic algorithm approach to solve the inverse folding problem. The main aims of the development was to address the hitherto mostly ignored extension of solving the inverse folding problem, the multi-target inverse folding problem, while simultaneously designing a method with superior performance when measured on the quality of designed sequences. The genetic algorithm has been implemented as a Python program called Frnakenstein. It was benchmarked against four existing methods and several data sets totalling 769 real and predicted single structure targets, and on 292 two structure targets. It performed as well as or better at finding sequences which folded in silico into the target structure than all existing methods, without the heavy bias towards CG base pairs that was observed for all other top performing methods. On the two structure targets it also performed well, generating a perfect design for about 80% of the targets.ConclusionsOur method illustrates that successful designs for the inverse RNA folding problem does not necessarily have to rely on heavy biases in base pair and unpaired base distributions. The design problem seems to become more difficult on larger structures when the target structures are real structures, while no deterioration was observed for predicted structures. Design for two structure targets is considerably more difficult, but far from impossible, demonstrating the feasibility of automated design of artificial riboswitches. The Python implementation is available at http://www.stats.ox.ac.uk/research/genome/software/frnakenstein.

Dataset Information

Multiple native states reveal persistent ruggedness of an RNA folding landscape.

Publications

Multiple native states reveal persistent ruggedness of an RNA folding landscape.

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