Selecting the best quality model from a set of predicted structures is one of the most important aspects of protein structure prediction. We have developed model quality assessment programs that select high quality models which account for both the Calpha backbone and side-chain atom positions. The new methods are based on the consensus method with consideration of the side-chain environment of a protein structure and the secondary structure agreement. This Side-chain Environment Consensus (SEC) method is compared with the conventional consensus method, 3D-Jury (Ginalski K. et al., Bioinformatics, 19, 1015-1018 (2003)), which takes into account only the Calpha backbone atoms of the protein model. As the result, it was found that the SEC method selects the models with more accurate positioning of the side-chain atoms than the 3D-Jury method. When the SEC method was used in combination with the 3D-Jury method (3DJ+SEC), models were selected with improved quality both in the Calpha backbone and side-chain atom positions. Moreover, the CIRCLE (CCL) method (Terashi G. et al., Proteins, 69 (Suppl. 8), 98-107 (2007)) based on the 3D-1D profile score has been shown to select the best possible models that are the closest to the native structures from candidate models. Accordingly, the 3DJ+SEC+CCL method, in which CIRCLE is used after reducing the number of candidates by the 3DJ+SEC consensus method, was found to be very effective in selecting high quality models. Thus, the best method (the 3DJ+SEC+CCL method) includes the consensus approaches of the Calpha backbone and the side-chains, the secondary structure agreement and the 3D-1D profile score which corresponds to the free energy-like score in the residues of the protein model. In short, new algorithms are introduced in protein structure evaluation methods that are based on a side-chain consensus score. Additionally, in order to apply the 3DJ+SEC+CCL method and indicate the usefulness of this method, a model of human Cabin1, a protein associated with p53 function and cancer, is created using various internet modeling and alignment servers.