Computational models of protein folding and ligand docking are large and complex. Few systematic methods have yet been developed to optimize the parameters in such models. We describe here an iterative parameter optimization strategy that is based on minimizing a structural error measure by descent in parameter space. At the start, we know the 'correct' native structure that we want the model to produce, and an initial set of parameters representing the relative strengths of interactions between the amino acids. The parameters are changed systematically until the model native structure converges as closely as possible to the correct native structure. As a test, we apply this parameter optimization method to the recently developed Gaussian model of protein folding: each amino acid is represented as a bead and all bonds, covalent and noncovalent, are represented by Hooke's law springs. We show that even though the Gaussian model has continuous degrees of freedom, parameters can be chosen to cause its ground state to be identical to that of Go-type lattice models, for which the global ground states are known. Parameters for a more realistic protein model can also be obtained to produce structures close to the real native structures in the protein database.