Data growth and its impact on the SCOP database: new developments

Abstract

The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. The SCOP hierarchy comprises the following levels: Species, Protein, Family, Superfamily, Fold and Class. While keeping the original classification scheme intact, we have changed the production of SCOP in order to cope with a rapid growth of new structural data and to facilitate the discovery of new protein relationships. We describe ongoing developments and new features implemented in SCOP. A new update protocol supports batch classification of new protein structures by their detected relationships at Family and Superfamily levels in contrast to our previous sequential handling of new structural data by release date. We introduce pre-SCOP, a preview of the SCOP developmental version that enables earlier access to the information on new relationships. We also discuss the impact of worldwide Structural Genomics initiatives, which are producing new protein structures at an increasing rate, on the rates of discovery and growth of protein families and superfamilies. SCOP can be accessed at http://scop.mrc-lmb.cam.ac.uk/scop.

The Protein Data Bank archive as an open data resource

“…More than 400 million coordinate sets were downloaded in 2013 from the wwPDB partner sites. Both the utility and the uniformity of PDB data have enabled the development of other databases and datarelated resources, including resources for drug discovery (for a review see [32]); resources focused on small molecules and ligands such as ChEMBL [33], DrugBank [34], BindingDB [35], BindingMOAD [36], and PDBBind [37]; protein structure classification and annotation resources, such as CATH [38,39], SCOP [40][41][42], and PDBsum [43,44]; and focused, specialty annotation resources such as Protein Data Bank of Transmembrane Proteins (PDBTM) [45], ArchDB for functional loops in structures [46], and 3did for protein-protein interaction surfaces [47]. These resources are frequently compiled in the annual Database Issue of Nucleic Acids Research.…”

Conformational characterization of disulfide bonds: A tool for protein classification

“…Different schemes have been introduced to classify the disulfide conformers (Harrison and Sternberg, 1996;Hutchinson and Thornton, 1996;Ozhogina and Bominaar, 2009;Schmidt et al, 2006;Srinivasan et al, 1990) and in this work we adopted the scheme proposed by Schmidt et al (2006). We analyzed a sample of disulfide bonds associated with a protein set extracted from SCOP data base (Andreeva et al, 2004;Andreeva et al, 2008;Murzin et al, 1995). The protein set included eleven superfamilies of small disulfideͲrich proteins (SDP) and the thioredoxinͲlike superͲfamily.…”

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

“…1c). We performed an all-against-all comparison of 5,287 representative domain sequences from the Structural Classification of Proteins (SCOP) database 11 . After one iteration, HHblits detected 107% more true positive pairs than PSI-BLAST and 53% more than HMMER3 at 1% false discovery rate, and after three iterations, the improvement was 147% over PSI-BLAST and 69% over HMMER3.…”