Coarse-grained models have been extremely valuable in promoting our understanding of protein folding. However, the quantitative accuracy of existing simplified models is strongly hindered either from the complete removal of frustration (as in the widely used Gō-like models) or from the compromise with the minimal frustration principle and/or realistic protein geometry (as in the simple on-lattice models). We present a coarse-grained model that "naturally" incorporates sequence details and energetic frustration into an overall minimally frustrated folding landscape. The model is coupled with an optimization procedure to design the parameters of the protein Hamiltonian to fold into a desired native structure. The application to the study of src-Src homology 3 domain shows that this coarse-grained model contains the main physical-chemical ingredients that are responsible for shaping the folding landscape of this protein. The results illustrate the importance of nonnative interactions and energetic heterogeneity for a quantitative characterization of folding mechanisms.