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

摘要

This paper builds on previous research from an EA used to predict secondary structure of RNA molecules. The EA predicts which specific canonical base pairs forms hydrogen bonds and helices. Three new thermodynamic models were integrated into our EA. The first 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 is 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.