Abstract Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed. We have improved the linear encoding methodology and developed efficient search tools that can rapidly retrieve structural homologs from large protein databases. The new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation) has been proposed. SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce improved substitution matrices. Here we report the application of SARST to detect novel structural relationships such as circular permutation (CP) and domain swapping (DS).