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

Hosna Jabbari,Ian Wark,Carlo Montemagno,M Sohel Rahman

doi:10.1371/journal.pone.0194583

Abstract

MotivationRNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method.ResultsThe aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

Highlights

RNA molecules are crucial in different levels of cellular function, and their functions largely depend on their structures
A longstanding question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method
We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is as of great value

Summary

Introduction

RNA molecules are crucial in different levels of cellular function, and their functions largely depend on their structures. The most widely used methods for prediction of RNA secondary structure, can deal only with pseudoknot-free structures (structures with no crossing base pairs), even though pseudoknots (structures with crossing base pairs) are known to be functionally important in many RNAs. several approaches have been proposed that predict pseudoknotted structures, it is still difficult to evaluate merits of each approach as they are different in their hypothesis and their underlying scoring function (i.e. energy model). Several approaches have been proposed that predict pseudoknotted structures, it is still difficult to evaluate merits of each approach as they are different in their hypothesis and their underlying scoring function (i.e. energy model) In particular it is not known how much prediction accuracy depends on folding hypothesis (structure formation hypothesis) versus the underlying energy model.

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