Extending CATH: increasing coverage of the protein structure universe and linking structure with function

By Alison L. Cuff, Ian Sillitoe, Tony E. Lewis, Andrew B. Clegg, R. Rentzsch, Nicholas Furnham, M. Pellegrini-Calace, David T. Jones, Janet M. Thornton, Christine A. Orengo. 2010

137 citations recorded by the source corpus.

CATH version 3.3 (class, architecture, topology, homology) contains 128 688 domains, 2386 homologous superfamilies and 1233 fold groups, and reflects a major focus on classifying structural genomics (SG) structures and transmembrane proteins, both of which are likely to add structural novelty to the database and therefore increase the coverage of protein fold space within CATH. For CATH version 3.4 we have significantly improved the presentation of sequence information and associated functional information for CATH superfamilies. The CATH superfamily pages now reflect both the functional and structural diversity within the superfamily and include structural alignments of close and distant relatives within the superfamily, annotated with functional information and details of conserved residues. A significantly more efficient search function for CATH has been established by implementing the search server Solr (http://lucene.apache.org/solr/). The CATH v3.4 webpages have been built using the Catalyst web framework.

Subfields: Cell Biology, Materials Chemistry, Molecular Biology

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Citations in the corpus

lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests (2013, methodology): “For estimating lDDT scores of random protein pairs, 200 protein models with wrong fold were generated by selecting pairs of structures with different CATH topologies, generating models by rebuilding side chains on the backbone of the other protein, and computing lDDT scores for these decoy models.”
SIFTS: Structure Integration with Function, Taxonomy and Sequences resource (2012, methodology): “There is one tab-delimited ﬁle for each resource, i.e. UniProtKB, NCBI taxonomy, EC enzyme classiﬁcation, InterPro, GO, CATH and Pfam.”
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.”
The RCSB Protein Data Bank: new resources for research and education (2012, background): “uk/iubmb), Transporter Classification System (9), and structure classifications SCOP (10) and CATH (11).”
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 InterPro protein families database: the classification resource after 15 years (2014, background): “However, as they are based on two different structural classification resources (CATH (22) and SCOP (23), respectively), there can be differences in scope and domain boundaries, which can make integration problematic.”
De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts (2014, methodology): “*obtained from SCOP database [35] and verified in CATH [36].”
Computational prediction of secondary and supersecondary structures. (2013, methodology): “The first attempt to predict coiled coils was based on scoring the propensity for formation of coiled coils in the predicted (input) sequence by calculating similarity to a position-specific scoring matrix derived from a statistical analysis of a coiled coil database (67).”
Predicting protein residue-residue contacts using deep networks and boosting (2012, methodology): “Using the CATH structure classification database (Cuff et al. , 2011), we indentified and grouped 140 of the proteins in DNCON_TEST as mainly alpha, α (29), mainly beta, β (30) and alpha beta, αβ (81).”
Gene3D: a domain-based resource for comparative genomics, functional annotation and protein network analysis (2011, methodology): “Proteins are generally composed of one or more discrete independently folding units known as domains and the CATH database (5) uses a combination of manual curation and automated evidence gathering to generate a superfamily classification of such structures in the PDB (6).”