A Boltzmann Filter Improves the Prediction of RNA Folding Pathways in a Massively Parallel Genetic Algorithm

Abstract

RNA folding using the massively parallel genetic algorithm (GA) has been enhanced by the addition of a Boltzmann filter. The filter uses the Boltzmann probability distribution in conjunction with Metropolis' relaxation algorithm. The combination of these two concepts within the GA's massively parallel computational environment helps guide the genetic algorithm to more accurately reflect RNA folding pathways and thus final solution structures. Helical regions (base-paired stems) now form in the structures based upon the stochastic properties of the thermodynamic parameters that have been determined from experiments. Thus, structural changes occur based upon the relative energetic impact that the change causes rather than just geometric conflicts alone. As a result, when comparing the predictions to phyloge-netically determined structures, over multiple runs, fewer false-positive stems (predicted incorrectly) and more true-positive stems (predicted correctly) are generated, and the total number of predicted stems representing a solution is diminished. In addition, the significance (rate of occurrence) of the true-positive stems is increased. Thus, the predicted results more accurately reflect phylogenetically determined structures.