The CATH database: an extended protein family resource for structural and functional genomics
Source: Nucleic Acids Research, Volume 31, Number 1, 01 January 2003 , pp. 452-455(4)
Publisher: Oxford University Press
Abstract:The CATH database of protein domain structures (http://www.biochem.ucl.ac.uk/bsm/cath_new) currently contains 34 287 domain structures classified into 1383 superfamilies and 3285 sequence families. Each structural family is expanded with domain sequence relatives recruited from GenBank using a variety of efficient sequence search protocols and reliable thresholds. This extended resource, known as the CATH-protein family database (CATH-PFDB) contains a total of 310 000 domain sequences classified into 26 812 sequence families. New sequence search protocols have been designed, based on these intermediate sequence libraries, to allow more regular updating of the classification.
Further developments include the adaptation of a recently developed method for rapid structure comparison, based on secondary structure matching, for domain boundary assignment. The philosophy behind CATHEDRAL is the recognition of recurrent folds already classified in CATH. Benchmarking of CATHEDRAL, using manually validated domain assignments, demonstrated that 43% of domains boundaries could be completely automatically assigned. This is an improvement on a previous consensus approach for which only 10–20% of domains could be reliably processed in a completely automated fashion. Since domain boundary assignment is a significant bottleneck in the classification of new structures, CATHEDRAL will also help to increase the frequency of CATH updates.
Document Type: Research Article
Affiliations: 1: Department of Computer Science, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK 2: EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK 3: *To whom correspondence should be addressed., Email: email@example.com
Publication date: 2003-01-01
- Nucleic Acids Research (NAR) is a fully Open Access journal, providing rapid publication of leading edge research into the nucleic acids under the following categories: chemistry, computational biology, genomics, molecular biology, nucleic acid enzymes, RNA and structural biology. There is a Survey and Summary section, and methods papers are published
in NAR Methods Online. Each year the first issue is devoted to biological databases, and a later issue to relevant web-based software resources.