Protein structural similarity search by Ramachandran codes

Background: 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 and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases. Results: We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence si...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Wei-Cheng Lo, Po-Jung Huang, Chih-Hung Chang, Ping-Chiang Lyu
• Keywords: BMCBI 2007, Proteins, Similarity Search, Structural Similarity Search