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A consensus view of fold space: combining SCOP, CATH, and the Dali Domain Dictionary.

ABSTRACT: We have determined consensus protein-fold classifications on the basis of three classification methods, SCOP, CATH, and Dali. These classifications make use of different methods of defining and categorizing protein folds that lead to different views of protein-fold space. Pairwise comparisons of domains on the basis of their fold classifications show that much of the disagreement between the classification systems is due to differing domain definitions rather than assigning the same domain to different folds. However, there are significant differences in the fold assignments between the three systems. These remaining differences can be explained primarily in terms of the breadth of the fold classifications. Many structures may be defined as having one fold in one system, whereas far fewer are defined as having the analogous fold in another system. By comparing these folds for a nonredundant set of proteins, the consensus method breaks up broad fold classifications and combines restrictive fold classifications into metafolds, creating, in effect, an averaged view of fold space. This averaged view requires that the structural similarities between proteins having the same metafold be recognized by multiple classification systems. Thus, the consensus map is useful for researchers looking for fold similarities that are relatively independent of the method used to compare proteins. The 30 most populated metafolds, representing the folds of about half of a nonredundant subset of the PDB, are presented here. The full list of metafolds is presented on the Web.

SUBMITTER: Day R

PROVIDER: S-EPMC2366924 | biostudies-literature | 2003 Oct

REPOSITORIES: biostudies-literature

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A consensus view of fold space: combining SCOP, CATH, and the Dali Domain Dictionary.

Day Ryan R Beck David A C DA Armen Roger S RS Daggett Valerie V

Protein science : a publication of the Protein Society 20031001 10

We have determined consensus protein-fold classifications on the basis of three classification methods, SCOP, CATH, and Dali. These classifications make use of different methods of defining and categorizing protein folds that lead to different views of protein-fold space. Pairwise comparisons of domains on the basis of their fold classifications show that much of the disagreement between the classification systems is due to differing domain definitions rather than assigning the same domain to di ...[more]

PMID: 14500873

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Project description:The spatial arrangements of secondary structures in proteins, irrespective of their connectivity, depict the overall shape and organization of protein domains. These features have been used in the CATH and SCOP classifications to hierarchically partition fold space and define the architectural make up of proteins. Here we use phylogenomic methods and a census of CATH structures in hundreds of genomes to study the origin and diversification of protein architectures (A) and their associated topologies (T) and superfamilies (H). Phylogenies that describe the evolution of domain structures and proteomes were reconstructed from the structural census and used to generate timelines of domain discovery. Phylogenies of CATH domains at T and H levels of structural abstraction and associated chronologies revealed patterns of reductive evolution, the early rise of Archaea, three epochs in the evolution of the protein world, and patterns of structural sharing between superkingdoms. Phylogenies of proteomes confirmed the early appearance of Archaea. While these findings are in agreement with previous phylogenomic studies based on the SCOP classification, phylogenies unveiled sharing patterns between Archaea and Eukarya that are recent and can explain the canonical bacterial rooting typically recovered from sequence analysis. Phylogenies of CATH domains at A level uncovered general patterns of architectural origin and diversification. The tree of A structures showed that ancient structural designs such as the 3-layer (αβα) sandwich (3.40) or the orthogonal bundle (1.10) are comparatively simpler in their makeup and are involved in basic cellular functions. In contrast, modern structural designs such as prisms, propellers, 2-solenoid, super-roll, clam, trefoil and box are not widely distributed and were probably adopted to perform specialized functions. Our timelines therefore uncover a universal tendency towards protein structural complexity that is remarkable.

Project description:BackgroundThe past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals.ResultsThe use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions.ConclusionsWe developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance.

Dataset Information

A consensus view of fold space: combining SCOP, CATH, and the Dali Domain Dictionary.

Publications

A consensus view of fold space: combining SCOP, CATH, and the Dali Domain Dictionary.

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