This paper presents two in-depth studies on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction. The first study is an analysis of the performance of two thermodynamic models, Individual Nearest Neighbor (INN) and Individual Nearest Neighbor Hydrogen Bond (INN-HB). The correlation between the free energy of predicted structures and the sensitivity is analyzed for 19 RNA sequences. Although some variance is shown, there is a clear trend between a lower free energy and an increase in true positive base pairs. With increasing sequence length, this correlation generally decreases. In the second experiment, the accuracy of the predicted structures for these 19 sequences are compared against the accuracy of the structures generated by the mfold dynamic programming algorithm (DPA) and also to known structures. RnaPredict is shown to outperform the minimum free energy structures produced by mfold and has comparable performance when compared to sub-optimal structures produced by mfold.

P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

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RNA is central in several stages of protein synthesis, and also has structural, functional, and regulatory roles in the cell. The shape of organic molecules such as RNA largely determines their function within an organic system, thus methods for the computational prediction of structure are sought after. In the ab initio case where only the RNA sequence is known, the currently dominant structure prediction techniques employ minimization of the free energy of a given RNA molecule via a thermodynamic model. However, the minimum free energy structure is rarely the native structure; this is thought to be due to errors in the thermodynamic model parameters, which are experimentally determined.Cluster analysis performed by [6] on a sampling of structures from a Boltzmann weighted ensemble determined that the best cluster centroid had an improved sensitivity and significantly improved positive predictive value over the minimum free energy structure in the ensemble. Based on this result, we investigated the combination of an existing evolutionary algorithm for RNA secondary structure prediction with a clustering algorithm.

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