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J E Bray

Publications and source records attributed to J E Bray.

8 recordsLinked to original sources
PubMed2003

The CATH database: an extended protein family resource for structural and functional genomics.

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.

Animals↗
PubMed2001

A rapid classification protocol for the CATH Domain Database to support structural genomics.

In order to support the structural genomic initiatives, both by rapidly classifying newly determined structures and by suggesting suitable targets for structure determination, we have recently developed several new protocols for classifying structures in the CATH domain database (http://www.biochem.ucl.ac.uk/bsm/cath). These aim to increase the speed of classification of new structures using fast algorithms for structure comparison (GRATH) and to improve the sensitivity in recognising distant structural relatives by incorporating sequence information from relatives in the genomes (DomainFinder). In order to ensure the integrity of the database given the expected increase in data, the CATH Protein Family Database (CATH-PFDB), which currently includes 25,320 structural domains and a further 160,000 sequence relatives has now been installed in a relational ORACLE database. This was essential for developing more rigorous validation procedures and for allowing efficient querying of the database, particularly for genome analysis. The associated Dictionary of Homologous Superfamilies [Bray,J.E., Todd,A.E., Pearl,F.M.G., Thornton,J.M. and Orengo,C.A. (2000) Protein Eng., 13, 153-165], which provides multiple structural alignments and functional information to assist in assigning new relatives, has also been expanded recently and now includes information for 903 homologous superfamilies. In order to improve coverage of known structures, preliminary classification levels are now provided for new structures at interim stages in the classification protocol. Since a large proportion of new structures can be rapidly classified using profile-based sequence analysis [e.g. PSI-BLAST: Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z., Miller,W. and Lipman,D.J. (1997) Nucleic Acids Res., 25, 3389-3402], this provides preliminary classification for easily recognisable homologues, which in the latest release of CATH (version 1.7) represented nearly three-quarters of the non-identical structures.

Computational Biology↗
PubMed2000

Assigning genomic sequences to CATH.

We report the latest release (version 1.6) of the CATH protein domains database (http://www.biochem.ucl. ac.uk/bsm/cath ). This is a hierarchical classification of 18 577 domains into evolutionary families and structural groupings. We have identified 1028 homo-logous superfamilies in which the proteins have both structural, and sequence or functional similarity. These can be further clustered into 672 fold groups and 35 distinct architectures. Recent developments of the database include the generation of 3D templates for recognising structural relatives in each fold group, which has led to significant improvements in the speed and accuracy of updating the database and also means that less manual validation is required. We also report the establishment of the CATH-PFDB (Protein Family Database), which associates 1D sequences with the 3D homologous superfamilies. Sequences showing identifiable homology to entries in CATH have been extracted from GenBank using PSI-BLAST. A CATH-PSIBLAST server has been established, which allows you to scan a new sequence against the database. The CATH Dictionary of Homologous Superfamilies (DHS), which contains validated multiple structural alignments annotated with consensus functional information for evolutionary protein superfamilies, has been updated to include annotations associated with sequence relatives identified in GenBank. The DHS is a powerful tool for considering the variation of functional properties within a given CATH superfamily and in deciding what functional properties may be reliably inherited by a newly identified relative.

Amino Acid Sequence↗
PubMed2000

Using the CATH domain database to assign structures and functions to the genome sequences.

The CATH database of protein structures contains approximately 18000 domains organized according to their (C)lass, (A)rchitecture, (T)opology and (H)omologous superfamily. Relationships between evolutionary related structures (homologues) within the database have been used to test the sensitivity of various sequence search methods in order to identify relatives in Genbank and other sequence databases. Subsequent application of the most sensitive and efficient algorithms, gapped blast and the profile based method, Position Specific Iterated Basic Local Alignment Tool (PSI-BLAST), could be used to assign structural data to between 22 and 36 % of microbial genomes in order to improve functional annotation and enhance understanding of biological mechanism. However, on a cautionary note, an analysis of functional conservation within fold groups and homologous superfamilies in the CATH database, revealed that whilst function was conserved in nearly 55% of enzyme families, function had diverged considerably, in some highly populated families. In these families, functional properties should be inherited far more cautiously and the probable effects of substitutions in key functional residues carefully assessed.

Algorithms↗
PubMed2000

The CATH Dictionary of Homologous Superfamilies (DHS): a consensus approach for identifying distant structural homologues.

A consensus approach has been developed for identifying distant structural homologues. This is based on the CATH Dictionary of Homologous Superfamilies (DHS), a database of validated multiple structural alignments annotated with consensus functional information for evolutionary protein superfamilies (URL: http://www. biochem.ucl.ac.uk/bsm/dhs). Multiple structural alignments have been generated for 362 well-populated superfamilies in the CATH structural domain database and annotated with secondary structure, physicochemical properties, functional sequence patterns and protein-ligand interaction data. Consensus functional information for each superfamily includes descriptions and keywords extracted from SWISS-PROT and the ENZYME database. The Dictionary provides a powerful resource to validate, examine and visualize key structural and functional features of each homologous superfamily. The value of the DHS, for assessing functional variability and identifying distant evolutionary relationships, is illustrated using the pyridoxal-5'-phosphate (PLP) binding aspartate aminotransferase superfamily. The DHS also provides a tool for examining sequence-structure relationships for proteins within each fold group.

Amino Acid Sequence↗
PubMed1999

The CATH Database provides insights into protein structure/function relationships.

We report the latest release (version 1.4) of the CATH protein domains database (http://www.biochem.ucl.ac.uk/bsm/cath). This is a hierarchical classification of 13 359 protein domain structures into evolutionary families and structural groupings. We currently identify 827 homologous families in which the proteins have both structual similarity and sequence and/or functional similarity. These can be further clustered into 593 fold groups and 32 distinct architectures. Using our structural classification and associated data on protein functions, stored in the database (EC identifiers, SWISS-PROT keywords and information from the Enzyme database and literature) we have been able to analyse the correlation between the 3D structure and function. More than 96% of folds in the PDB are associated with a single homologous family. However, within the superfolds, three or more different functions are observed. Considering enzyme functions, more than 95% of clearly homologous families exhibit either single or closely related functions, as demonstrated by the EC identifiers of their relatives. Our analysis supports the view that determining structures, for example as part of a 'structural genomics' initiative, will make a major contribution to interpreting genome data.

Algorithms↗
PubMed1999

Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction.

CASP3 saw a substantial increase in the volume of ab initio 3D prediction data, with 507 datasets for fifteen selected targets and sixty-one groups participating. As with CASP2, methods ranged from computationally intensive strategies that attempt to recreate the physical and chemical forces involved in protein folding to the more recent knowledge-based approaches. These exploit information from the structure databases, extracting potentially similar fragments and/or distance constraints derived from multiple sequence alignments. The knowledge-based approaches generally gave more consistently successful predictions across the range of targets, particularly that of the Baker group (Bystroff and Baker, J Mol Biol 1998;281:565-577; Simons et al. Proteins Suppl 1999;3:171-176), which used a fragment library. In the secondary structure prediction category, the most successful approaches built on the concepts used in PHD (Rost et al. Comput Appl Biosci 1994;10:53-60), an accepted standard in this field. Like PHD, they exploit neural networks but have different strategies for incorporating multiple sequence data or position-dependent weight matrices for training the networks. Analysis of the contact data, for which only six groups participated, suggested that as yet this data provides a rather weak signal. However, in combination with other types of prediction data it can sometimes be a useful constraint for identifying the correct structure.

Animals↗