Extracting biological importance from protein structures is extremely helpful to understand the molecular nature. Although methods for protein structure-based alignment have been hitherto proposed in a number of ways, each method focuses on a part of alignment possibility. We have developed a generic method for pairwise structure-based alignment utilizing the population search ability of a Real-coded Genetic Algorithm. Our method simultaneously optimizes vector-expressed local fragment posture and global atomic superposition. Here, we report comparative results derived from the proposed method and existing methods. The experiments use three protein pairs well studied and a number of pairs derived from diverse protein families. The results show that our method provides useful two-layer similarity and statistical signi.cance at a time to be able to capture not only the remarkable difference between local alignment and global alignment but also biologically meaningful common folds and motifs. Interestingly, we unveiled a vague region in protein structure-function relationships. It may indicate the limit of using alpha-carbon backbones.