A novel fragment replacement strategy for the fragment-based protein structure prediction is proposed. Despite the recent advance of de novo prediction of protein tertiary structure, intricate protein topologies still exist at unsatisfactory prediction quality. Although this difficulty is in part due to the accuracy of energy functions, it also relates to the search ability of sampling methods. To enhance the global optimization method that finds low-energy conformations, this study tests a biased sampling approach. The proposed approach is inspired by the fact that local structures of a protein have geometrical rigidity and flexibility. For capturing the pivotal local structures to generate various topologies, this approach first measures the energetic fluctuation of target fragments on dihedral angles of a protein, and then the quantity is converted to probability used by probabilistic selection of fragment replacement. Due to the requirement of the dihedral angles, a Genetic Algorithm implements the proposed idea, and experimental results show that the GA is capable of providing the dihedral angles as template-like proteins. The results suggest that the proposed approach can reach low-energy conformations with comparable prediction quality to that of an existing method. Interestingly, the low-energy states were associated with the frequent replacement of fragments in natively-coil regions. However, unfavorable compactification of the predicted models was observed. All experimental data are available at http://www.proteinsilico.org/PRO/.