RNA pseudoknot prediction via an evolutionary algorithm

Beyond its critical role in protein synthesis, RNA has vital structural, functional, and regulatory roles in the cell. The shape of an RNA molecule primarily determines its function in organic systems, so there is notable interest in the computational prediction of RNA structure. Pseudoknots are relatively rare but important structural elements which are difficult to predict computationally. RnaPredict is an evolutionary algorithm (EA) developed for the prediction of RNA secondary structure. This research evaluates RnaPredict after its enhancement with the thermodynamic model from HotKnots, a model specifically designed to compute free energies of structures containing pseudoknots. The performance of the EA is evaluated against the original HotKnots algorithm. RnaPredict significantly improved upon the sensitivity and specificity of structures predicted by HotKnots.