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Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high performance computing. In this paper, we present a framework to map hierarchical genetic algorithms for protein folding problems onto computational grids. It has been designed to take advantage of the communication characteristics of a computational grid. By using this framework, the two level communication parts of hierarchical genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on different levels conveniently. Our experiments show that it can lead to significant runtime savings on PC clusters and computational grids.