Impact of an enhanced thermodynamic model on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction

Authors:
Kay C. Wiese;Andrew G. Hendriks
Affiliations:
School of Computing Science, Simon Fraser University, Surrey, BC, Canada;School of Computing Science, Simon Fraser University, Surrey, BC, Canada
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

RNA has important structural, functional, and regulatory parts in the cell as well as a critical role in multiple stages of protein synthesis. An RNA molecule's shape largely determines its function in an organic system. Accordingly, computational RNA structural prediction methods are of significant interest. For ab initio cases where only an RNA sequence is known, structure prediction techniques typically employ free energy minimization of a given RNA molecule via a thermodynamic model. Unfortunately, the minimum free energy structure is rarely the native structure. This is thought to be due to errors in the experimentally determined thermodynamic model parameters. RnaPredict is an evolutionary algorithm designed for the prediction of RNA secondary structure; it currently utilizes the stacking-energy thermodynamic models INN and INN-HB. The effect of an enhanced model, efn2, on RnaPredict is investigated. The efn2 model significantly improved the sensitivity and specificity of the majority of structures evaluated.