Classifying TIM Barrel Protein Domain Structure by an Alignment Approach Using Best Hit Strategy and PSI-BLAST

Abstract

High-tech large-scale sequencing projects have identified a massive number of amino acid sequences for both known and putative proteins, but information on the three-dimensional (3D) structures of these proteins is limited. Several structure databases, such as the Structural Classification of Proteins (SCOP (Andreeva et al., 2008), release version 1.73) and the Class, Architecture, Topology, and Homologous superfamily (CATH (Cuff et al., 2009), release version 3.2.0), contain fewer than 60,000 entries in the Protein Data Bank (PDB (Berman et al., 2000), released on 12 May, 2009). This number of entries constitutes only about 15% of entries in Swiss-Prot (Bairoch et al., 2004), release version 57.2, with more than 400,000 entries). Either X-ray diffraction or NMR can be used to determine the 3D structure of a protein, but each method has its limitation (Dubchak et al., 1995). As such, extracting structural information from sequence databases is an important and complementary alternative to these experimental methods, especially when swiftly determining protein functions or discovering new compounds for medical or therapeutic purposes. From ASTRAL SCOP 1.73, it has been estimated that ~10% of known enzymes have triosephosphate isomerase (TIM) barrel domains. Moreover, TIM barrel proteins have been identified in five of six enzyme classes, oxidoreductases, transferases, hydrolases, lyases and isomerases, in the Enzyme nomenclature (ENZYME (Bairoch, 2000), released on 5 May, 2009) database; the ligases class does not contain TIM barrel protein. TIM barrel proteins are diverse in sequence and functionality and thus represent attractive targets for protein engineering and evolutionary studies. It is therefore important to examine TIM barrel protein domain structure classification in SCOP and ENZYME. In SCOP, there are six levels of hierarchy: class, fold, superfamily, family, protein domain and species. The classification of protein structures has, more recently, been facilitated by computer-aided algorithms. Previous research (Chou & Zhang, 1995; Dubchak et al., 1995; Lin et al., 2005, 2007) has shown that an overall prediction accuracy rate of 70-90% can be