Protein folding stands as one of the major interdisciplinary challenges of the last ten years, involving biology, chemistry, medicine and physics. In this PhD thesis, we investigate the solvent effects and the resulting hydrophobic effect on proteins. In particular, we focus on a simple lattice model of proteins (the HPW model), where the solvent is (semi)explicitly taken into account (by means of the model of Muller, Lee and Graziano (MLG)), investigating thermodynamical quantities of the modeled proteins and comparing the results with those of real proteins. We approach the protein design problem using the HPW model and we investigate some statistical and thermodynamic properties of the designed sequences. In particular we show that the HPW model is able to capture, within a single framework, both warm and cold denaturation of proteins. We further adapt the MLG model model to include chaotrope cosolvents in the solution. We show that the alteration of the network of hydrogen bonds between water molecules due to the presence of chaotropic agents induces destabilization of proteins. Moreover, we find that the presence of chaotropes originates an effective interaction between the chaotropes and the protein. In addition, we use a common method to extract effective amino-acid interactions on real proteins: a perceptron algorithm. The method applied to proteins designed with the HPW model reveals that the solvent effects can not be reproduced by effective residue interactions. Indeed, we show that it is not possible to determine a set of global interaction values able to stabilize all proteins at once.