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An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins.

ABSTRACT: With a growing number of structures available in the Brookhaven Protein Data Bank, automatic methods for domain identification are required for the construction of databases. Domains are considered to be clusters of secondary structure elements. Thus, helices and strands are first clustered using intersecondary structural distances between C alpha positions, and dendrograms based on this distance measure are used to identify domains. Individual domains are recognized by a disjoint factor, which enables the automatic identification and classification into disjoint, interacting, and conjoint domains. Application to a database of 83 protein families and 18 unique structures shows that the approach provides an effective delineation of boundaries and identifies those proteins that can be considered as a single domain. A quantitative estimate of the interaction between domains has been proposed. The database of protein domains is a useful tool for understanding protein folding, for recognizing protein folds, and for understanding structure-activity relationships.

SUBMITTER: Sowdhamini R

PROVIDER: S-EPMC2143076 | biostudies-other | 1995 Mar

REPOSITORIES: biostudies-other

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An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins.

Sowdhamini R R Blundell T L TL

Protein science : a publication of the Protein Society 19950301 3

With a growing number of structures available in the Brookhaven Protein Data Bank, automatic methods for domain identification are required for the construction of databases. Domains are considered to be clusters of secondary structure elements. Thus, helices and strands are first clustered using intersecondary structural distances between C alpha positions, and dendrograms based on this distance measure are used to identify domains. Individual domains are recognized by a disjoint factor, which ...[more]

PMID: 7795532

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Project description:BackgroundProteins are comprised of one or several building blocks, known as domains. Such domains can be classified into families according to their evolutionary origin. Whereas sequencing technologies have advanced immensely in recent years, there are no matching computational methodologies for large-scale determination of protein domains and their boundaries. We provide and rigorously evaluate a novel set of domain families that is automatically generated from sequence data. Our domain family identification process, called EVEREST (EVolutionary Ensembles of REcurrent SegmenTs), begins by constructing a library of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families. The selection of the best putative families is done using machine learning techniques. A statistical model is then created for each of the chosen families. This procedure is then iterated: the aforementioned statistical models are used to scan all protein sequences, to recreate a library of segments and to cluster them again.ResultsProcessing the Swiss-Prot section of the UniProt Knoledgebase, release 7.2, EVEREST defines 20,230 domains, covering 85% of the amino acids of the Swiss-Prot database. EVEREST annotates 11,852 proteins (6% of the database) that are not annotated by Pfam A. In addition, in 43,086 proteins (20% of the database), EVEREST annotates a part of the protein that is not annotated by Pfam A. Performance tests show that EVEREST recovers 56% of Pfam A families and 63% of SCOP families with high accuracy, and suggests previously unknown domain families with at least 51% fidelity. EVEREST domains are often a combination of domains as defined by Pfam or SCOP and are frequently sub-domains of such domains.ConclusionThe EVEREST process and its output domain families provide an exhaustive and validated view of the protein domain world that is automatically generated from sequence data. The EVEREST library of domain families, accessible for browsing and download at 1, provides a complementary view to that provided by other existing libraries. Furthermore, since it is automatic, the EVEREST process is scalable and we will run it in the future on larger databases as well. The EVEREST source files are available for download from the EVEREST web site.

Dataset Information

An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins.

Publications

An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins.

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