Abstract
Prediction of RNA structure is a useful process for creating new drugs and understanding genetic diseases. In this paper, we proposed a particle swarm optimization (PSO) and ant colony optimization (ACO) based framework (PAF) for RNA secondary structure prediction. PAF consists of crucial stem searching (CSS) and global sequence building (GSB). In CSS, a modified ACO (MACO) is used to search the crucial stems, and then a set of stems are generated. In GSB, we used a modified PSO (MPSO) to construct all the stems in one sequence. We evaluated the performance of PAF on ten sequences, which have length from 122 to 1494. We also compared the performance of PAF with the results obtained from six existing well-known methods, SARNA-Predict, RnaPredict, ACRNA, PSOfold, IPSO, and mfold. The comparison results show that PAF could not only predict structures with higher accuracy rate but also find crucial stems.
Highlights
RNA functions as an information carrier, catalyst, and regulatory element, perhaps reflecting its importance in the earliest stages of evolution
Where a, b, c is the weight; Ei is the free energy for the secondary structure in the ith particle; δ is the number of pairs of the jth stem; P is the length of possible pairs; φ is the size of stem k which is higher than 4; M is the number of selected stems; L is the total number of stems which are higher than 4; S is the size of pseudoknots; W is the size of possible pairs
One noteworthy setting is the percentage of suboptimality
Summary
RNA functions as an information carrier, catalyst, and regulatory element, perhaps reflecting its importance in the earliest stages of evolution. The metaheuristic methods are widely used to predict RNA secondary structure. These methods generally include genetic algorithm (GA) [3], particle swarm optimization (PSO) [4], ant colony optimization (ACO) [5], and simulated annealing (SA) [6]. Yu et al [13] put forward ACRNA based on ACO for RNA secondary structure prediction. Neethling and Engelbrecht [15] proposed a set-based Particle Swarm Optimization algorithm to optimize the structure of an RNA molecule, using an advanced thermodynamic model. (1) A framework, namely, PAF, was proposed for RNA secondary structure prediction, which includes CSS and GSB.
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