CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site anno

Background: The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods. Results: The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Compar...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Gong-Hua Li, Jing-Fei Huang
• Keywords: BMCBI 2010, Protein, Protein Structural Similarity, Structural