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

By Antonina Andreeva, Dave Howorth, John-Marc Chandonia, Steven E. Brenner, Tim J. P. Hubbard, Cyrus Chothia, Alexey G. Murzin. 2007

975 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy, Evaluation & Benchmarks, Computational Biology & Bioinformatics

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Citations in the corpus

The Pfam protein families database in 2019 (2018, methodology): “We aim to ensure that Pfam entries and clan relationships are consistent with other structural classifications [e.g. CATH (10), SCOP (11)], and entries are consistent with each other within a clan, ideally having the same Pfam type and models of comparable sizes.”
SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures (2013, methodology): “We also compared domains that had been added by the previous automated method (4), which had added 30 852 new domains in total to versions 1.”
SIFTS: Structure Integration with Function, Taxonomy and Sequences resource (2012, methodology): “Once the correct UniProtKB entry (or entries, in the case of a chimera) has been identiﬁed, further annotation is obtained from the IntEnz, Pfam and InterPro databases and cross-reference information from the structure family databases CATH and SCOP is integrated whenever new versions of these resou”
Template-based protein structure modeling using the RaptorX web server (2012, background): “The Structural Classification of Proteins (SCOP) database provides a description of the structural and evolutionary relation of most proteins in the PDB 45,46 .”
One contact for every twelve residues allows robust and accurate topology-level protein structure modeling (2013, neutral): “On the basis of a consensus definition of SCOP38 and CATH, 39 > 99.9% of proteins are not in the same fold when the TM-score 5 0.4, but when the TM-score 5 0.6, > 90% are in the same fold; hence TM-scores greater than 0.5 indicate that the overall fold is very likely correctly modeled.”
InterPro in 2017—beyond protein family and domain annotations (2016, background): “InterPro entries are also automatically annotated with cross-references to a range of relevant databases, including the pathway databases ENZYME (18), MetaCyc (19), UniPathway (20) and KEGG (21), and various 3D structure databases (22,23).”
Dali server: conservation mapping in 3D (2010, methodology): “The ability of the walking strategy to retrieve biologically interesting hits was assessed by comparison with the SCOP classiﬁcation (13), which is a hierarchical classiﬁcation of protein structures and curated by experts.”
Protein interface conservation across structure space (2010, methodology): “Structural neighbors belonging to the same family, superfamily, or fold were taken from the SCOP 1.73 database (37).”
Advances and pitfalls of protein structural alignment. (2009, positive): “The ﬁrst two depend on a manually curated reference alignment (e.g. HOMSTRAD [45], CDD [46,47], Sisyphus [48], BIRC [49]), or classiﬁcation (e.g. SCOP [50] and CATH [51]) as the standard of truth.”
ACLAME: A CLAssification of Mobile genetic Elements, update 2010 (2009, methodology): “001) against the non-redundant (nr) database from NCBI (8), the SwissProt database (9) and the SCOP database (10) to collect a maximum number of similar sequences.”
Searching ECOD for homologous domains by sequence and structure (2018, background): “75 and a rearrangement of its hierarchy and domain boundaries (Andreeva et al., 2008).”
BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology (2015, methodology): “A similar browsing tool provides users with a view into BindingDB based on protein fold families (www.bindingdb.org/SCOP/scop.jsp), based on the widely used SCOP classification (27).”
Protein sequence comparison and fold recognition: progress and good-practice benchmarking. (2011, methodology): “though CATH [58], Pfam [5], and COPS [59] are sometimes used, the SCOP database [60] has emerged as de facto standard.”
The structure of mouse cytomegalovirus m04 protein determined from sparse NMR data reveals a new fold of the m02-m06 family of viral immune modulators (2014, neutral): “Although the Ig fold is typically characterized by seven strands on two sheets (Structural Classification of Proteins [Andreeva et al., 2008]), the m04 core structure has a total of nine strands, four of which (b2–b3–b8–b7) are short (4 or 5 residues each) and form one layer of the b sandwich, while”