Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological pseudo-energies. Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.