The protein structure prediction is one of the key problems in Structural Bioinformatics. The protein function is directly related to its conformation and the folding can provide to researchers better understandings about the protein roles in the cell. Several computational methods have been proposed over the last decades to tackle the problem. In this paper, we propose an ab initio algorithm with database information for the protein structure prediction problem. We do so by designing some versions of a multi-agent system that use concepts of dynamic distributed evolutionary algorithms to speed up and improve the optimization by better adapting the algorithm to the target protein. The dynamic strategy consists of auto-adapting the number of optimization agents according to the needs and current status of the optimization process. The system is able to scale in/out itself depending on some diversity criteria. The algorithms also take advantage of structural knowledge from the Protein Data Bank to better guide the search and constraint the state space. To validate our computational strategies, we tested them on a set of eight protein sequences. The obtained results were topologically compatible with the experimental correspondent ones, thus corroborating the promising performance of the strategies.