Protein structure prediction is based mainly on the modeling of proteins by homology to known structures; this knowledge-based approach is the most promising method to date. Although it is used in the whole area of protein research, no general rules concerning the quality and applicability of concepts and procedures used in homology modeling have been put forward yet. Therefore, the main goal of the present work is to provide tools for the assessment of accuracy of modeling at a given level of sequence homology. A large set of known structures from different conformational and functional classes, but various degrees of homology was selected. Pairwise structure superpositions were performed. Starting with the definition of the structurally conserved regions and determination of topologically correct sequence alignments, we correlated geometrical properties with sequence homology (defined by the 250 PAM Dayhoff Matrix) and identity. It is shown that both the topological differences of the protein backbones and the relative positions of corresponding side chains diverge with decreasing sequence identity. Below 50% identity, the deviation in regions that are structurally not conserved continually increases, thus implying that with decreasing sequence identity modeling has to take into account more and more structurally diverging loop regions that are difficult to predict.