Evolutionary Algorithm for RNA Secondary Structure Prediction Based on Simulated SHAPE Data.

Soheila Montaseri,Fatemeh Zare-Mirakabad,Mohammad Ganjtabesh,M Sohel Rahman

doi:10.1371/journal.pone.0166965

Soheila Montaseri, Fatemeh Zare-Mirakabad + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0166965

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Abstract

BackgroundNon-coding RNAs perform a wide range of functions inside the living cells that are related to their structures. Several algorithms have been proposed to predict RNA secondary structure based on minimum free energy. Low prediction accuracy of these algorithms indicates that free energy alone is not sufficient to predict the functional secondary structure. Recently, the obtained information from the SHAPE experiment greatly improves the accuracy of RNA secondary structure prediction by adding this information to the thermodynamic free energy as pseudo-free energy.MethodIn this paper, a new method is proposed to predict RNA secondary structure based on both free energy and SHAPE pseudo-free energy. For each RNA sequence, a population of secondary structures is constructed and their SHAPE data are simulated. Then, an evolutionary algorithm is used to improve each structure based on both free and pseudo-free energies. Finally, a structure with minimum summation of free and pseudo-free energies is considered as the predicted RNA secondary structure.Results and ConclusionsComputationally simulating the SHAPE data for a given RNA sequence requires its secondary structure. Here, we overcome this limitation by employing a population of secondary structures. This helps us to simulate the SHAPE data for any RNA sequence and consequently improves the accuracy of RNA secondary structure prediction as it is confirmed by our experiments. The source code and web server of our proposed method are freely available at http://mostafa.ut.ac.ir/ESD-Fold/.

Highlights

RNA molecules play vital roles in some cellular processes including genetic information carrier, biological catalysis and gene regulation [1]
Simulating the Selective 20-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data for a given RNA sequence requires its secondary structure. We overcome this limitation by employing a population of secondary structures. This helps us to simulate the SHAPE data for any RNA sequence and improves the accuracy of RNA secondary structure prediction as it is confirmed by our experiments
minimum free energy (MFE) is known as a suitable physical measure for RNA secondary structure prediction, the results show that this criterion does not have enough ability to predict the correct structure

Summary

Introduction

RNA molecules play vital roles in some cellular processes including genetic information carrier, biological catalysis and gene regulation [1]. The activity and function of non-coding RNAs are mainly related to their secondary structures formed by Watson-Crick and Wobble base pairs. In the two last decades, many algorithms and computational methods are proposed for the RNA secondary structure prediction based on maximizing the number of base pairs or minimizing the free energy. RNAfold [2, 3] uses a dynamic programming algorithm to predict minimum free energy (MFE) structures as well as to compute partition functions and base pairing probabilities. UNAfold [4] combines free energy minimization, partition function calculation and stochastic sampling to predict RNA secondary structure using dynamic programming algorithm. MFold [5] uses dynamic programming approach to predict RNA secondary structure based on energy minimization method [6].

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