Using stacking-energies (INN and INN-HB) for improving the accuracy of RNA secondary structure prediction with an evolutionary algorithm - a comparison to known structures

Abstract

This paper builds on previous research from an EA used to predict secondary structure of RNA molecules. The IEA predicts which specific canonical base pairs will form hydrogen bonds and helices. Three new thermodynamic models were integrated into our EA. The first is based on a modification to our original base pair model. The last two, INN and INN-HB, add stacking-energies using base pair adjacencies. We have tested RNA sequences of lengths 122, 543, and 1494 nucleotides on a wide variety of operators and parameters settings. The accuracy of the predicted structures are compared to the known structures thus demonstrating the benefits of using stacking-energies in structure prediction. Some other improvements to our EA are also discussed. I. INTRODUCTIONRNA is an important biological molecule. It plays key roles in the synthesis of protein from DNA. It is also known for its structural and catalytic roles in the cell [I]. For the purpose of structure prediction, it can be simply described as a flexible single-stranded biopolymer. The biopolymer is made from a sequence of 4 different nucleotides, adenine (A), cytosine (C), guanine (G), and uracil (U). Intra-molecular base pairs can form between different nucleotides, folding the sequence onto itself. The most stable and commvn of these base pairs are GC, AU, and GU, and their mirrors, CG, UA, and UG. These pairs are called canonical base pairs. A more elaborate description of RNA can be found in our previous research (121, [3], 141).A base pair does not form in isolation in our model. We consider stacked pairs, also called a helix, only when three or more adjacent pairs form. Also, the loop connecting a set of stacked pairs must be no shorter than 3 nucleotides in length. By using these simple rules, it is possible to enumerate all the possible helices that can form in a structure. The challenge is in predicting which ones will actually form in nature. The listing of these base pairs in a single structure is what is called the secondary structure of that RNA molecule. Determining the secondary structure of RNA through laboratory methods, such as NMR [SI and XiRay crystallography [6], is challenging and expensive. Hence, computation prediction methods have been proposed as an alternate approach to this problem.Genetic algorithms (GAS) [7] have been applied previously to this problem domain. A serial GA [8] was used to find optimal and sub-optimal structures. A massively parallel GA [9] was introduced in this domain followed by a new mutation operator [IO]. A binary coded GA was also used to show it could approximate the folding pathway of an RNA molecule by adding and deleting helices ([Ill, [12]).These improvements increased the number of true-positive stem loops and base pairs correctly predicted making the GA's prediction even more accurate than that of a dynamic programming algorithm (DPA). DPAs are known for finding th,e single optimal solution within a given thermodynamic model. However, in RNA secondary structure prediction, the optima.1 fold is often a the...

The impact of pseudorandom number quality on P-RnaPredict , a parallel genetic algorithm for RNA secondary structure prediction

“…Extensive testing with the serial GA [5] has shown conclusively that in terms of base-pairs found in known structures the INN and INN-HB stacking-energy thermodynamic models outperformed the simple Major and Matthews basepairing models. Hence, for the experiments presented here, the thermodynamic model chosen was the INN-HB stackingenergy model.…”

“…Later, Deschênes and Wiese [5] have shown that RnaPredict, using two stacking based thermodynamic models, can predict certain RNA secondary structures with very high accuracy and also outperform a dynamic programming algorithm [6]. Our specific work builds on [11,10], in which a serial simulation of a distributed version of RnaPredict was implemented that predicted the secondary structure of RNA molecules from input RNA sequences.…”

A Detailed Analysis of Parallel Speedup in P-RnaPredict - An Evolutionary Algorithm for RNA Secondary Structure Prediction

“…The underlying principle behind INN and INN-HB is that the stabilizing contribution each base pair makes to its helix depends on that base pairs' nearest neighbours. These models are discussed in detail in [12].…”

“…Parameters which varied were based on those determined through prior research [23], [12], and were configured as follows:…”

“…Kay C. Wiese P-RnaPredict is a fully parallel implementation of a coarse-grained distributed EA for RNA secondary structure prediction [10]. It is based on RnaPredict, a serial EA for the same purpose which encodes RNA secondary structures in permutations [11] and includes two stacking-energy based thermodynamic models [12].…”