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Structure | 1997

CATH – a hierarchic classification of protein domain structures

Christine A. Orengo; Alex D. Michie; Susan Jones; David Jones; Mark B. Swindells; Janet M. Thornton

BACKGROUND Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can also be assigned. The ever increasing number of known protein structures is too large to classify all proteins manually, therefore, automatic methods are needed for fast evaluation of protein structures. RESULTS We present a semi-automatic procedure for deriving a novel hierarchical classification of protein domain structures (CATH). The four main levels of our classification are protein class (C), architecture (A), topology (T) and homologous superfamily (H). Class is the simplest level, and it essentially describes the secondary structure composition of each domain. In contrast, architecture summarises the shape revealed by the orientations of the secondary structure units, such as barrels and sandwiches. At the topology level, sequential connectivity is considered, such that members of the same architecture might have quite different topologies. When structures belonging to the same T-level have suitably high similarities combined with similar functions, the proteins are assumed to be evolutionarily related and put into the same homologous superfamily. CONCLUSIONS Analysis of the structural families generated by CATH reveals the prominent features of protein structure space. We find that nearly a third of the homologous superfamilies (H-levels) belong to ten major T-levels, which we call superfolds, and furthermore that nearly two-thirds of these H-levels cluster into nine simple architectures. A database of well-characterised protein structure families, such as CATH, will facilitate the assignment of structure-function/evolution relationships to both known and newly determined protein structures.


Nucleic Acids Research | 2009

InterPro: the integrative protein signature database

Sarah Hunter; Rolf Apweiler; Teresa K. Attwood; Amos Marc Bairoch; Alex Bateman; David Binns; Peer Bork; Ujjwal Das; Louise Daugherty; Lauranne Duquenne; Robert D. Finn; Julian Gough; Daniel H. Haft; Nicolas Hulo; Daniel Kahn; Elizabeth Kelly; Aurélie Laugraud; Ivica Letunic; David M. Lonsdale; Rodrigo Lopez; John Maslen; Craig McAnulla; Jennifer McDowall; Jaina Mistry; Alex L. Mitchell; Nicola Mulder; Darren A. Natale; Christine A. Orengo; Antony F. Quinn; Jeremy D. Selengut

The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total ∼58 000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein–protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).


Nucleic Acids Research | 2012

InterPro in 2011: new developments in the family and domain prediction database

Sarah Hunter; P. D. Jones; Alex L. Mitchell; Rolf Apweiler; Teresa K. Attwood; Alex Bateman; Thomas Bernard; David Binns; Peer Bork; Sarah W. Burge; Edouard de Castro; Penny Coggill; Matthew Corbett; Ujjwal Das; Louise Daugherty; Lauranne Duquenne; Robert D. Finn; Matthew Fraser; Julian Gough; Daniel H. Haft; Nicolas Hulo; Daniel Kahn; Elizabeth Kelly; Ivica Letunic; David M. Lonsdale; Rodrigo Lopez; John Maslen; Craig McAnulla; Jennifer McDowall; Conor McMenamin

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.


Nucleic Acids Research | 2015

The InterPro protein families database: the classification resource after 15 years

Alex L. Mitchell; Hsin-Yu Chang; Louise Daugherty; Matthew Fraser; Sarah Hunter; Rodrigo Lopez; Craig McAnulla; Conor McMenamin; Gift Nuka; Sebastien Pesseat; Amaia Sangrador-Vegas; Maxim Scheremetjew; Claudia Rato; Siew-Yit Yong; Alex Bateman; Marco Punta; Teresa K. Attwood; Christian J. A. Sigrist; Nicole Redaschi; Catherine Rivoire; Ioannis Xenarios; Daniel Kahn; Dominique Guyot; Peer Bork; Ivica Letunic; Julian Gough; Matt E. Oates; Daniel H. Haft; Hongzhan Huang; Darren A. Natale

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.


Journal of Molecular Biology | 1989

Protein structure alignment

William R. Taylor; Christine A. Orengo

A new method of comparing protein structures is described, based on distance plot analysis. It is relatively insensitive to insertions and deletions in sequence and is tolerant of the displacement of equivalent substructures between the two molecules being compared. When presented with the co-ordinate sets of two structures, the method will produce automatically an alignment of their sequences based on structural criteria. The method uses the dynamic programming optimization technique, which is widely used in the comparison of protein sequences and thus unifies the techniques of protein structure and sequence comparison. Typical structure comparison problems were examined and the results of the new method compared to the published results obtained using conventional methods. In most examples, the new method produced a result that was equivalent, and in some cases superior, to those reported in the literature.


Nucleic Acids Research | 2007

New developments in the InterPro database

Nicola Mulder; Rolf Apweiler; Teresa K. Attwood; Amos Marc Bairoch; Alex Bateman; David Binns; Peer Bork; Virginie Buillard; Lorenzo Cerutti; Richard R. Copley; Emmanuel Courcelle; Ujjwal Das; Louise Daugherty; Mark Dibley; Robert D. Finn; Wolfgang Fleischmann; Julian Gough; Daniel H. Haft; Nicolas Hulo; Sarah Hunter; Daniel Kahn; Alexander Kanapin; Anish Kejariwal; Alberto Labarga; Petra S. Langendijk-Genevaux; David M. Lonsdale; Rodrigo Lopez; Ivica Letunic; John Maslen; Craig McAnulla

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (), and for download by anonymous FTP (). The InterProScan search tool is now also available via a web service at .


Nature Reviews Molecular Cell Biology | 2007

Predicting protein function from sequence and structure

David A. Lee; Oliver Redfern; Christine A. Orengo

While the number of sequenced genomes continues to grow, experimentally verified functional annotation of whole genomes remains patchy. Structural genomics projects are yielding many protein structures that have unknown function. Nevertheless, subsequent experimental investigation is costly and time-consuming, which makes computational methods for predicting protein function very attractive. There is an increasing number of noteworthy methods for predicting protein function from sequence and structural data alone, many of which are readily available to cell biologists who are aware of the strengths and pitfalls of each available technique.


Nucleic Acids Research | 2017

InterPro in 2017—beyond protein family and domain annotations

Robert D. Finn; Teresa K. Attwood; Patricia C. Babbitt; Alex Bateman; Peer Bork; Alan Bridge; Hsin Yu Chang; Zsuzsanna Dosztányi; Sara El-Gebali; Matthew Fraser; Julian Gough; David R Haft; Gemma L. Holliday; Hongzhan Huang; Xiaosong Huang; Ivica Letunic; Rodrigo Lopez; Shennan Lu; Huaiyu Mi; Jaina Mistry; Darren A. Natale; Marco Necci; Gift Nuka; Christine A. Orengo; Youngmi Park; Sebastien Pesseat; Damiano Piovesan; Simon Potter; Neil D. Rawlings; Nicole Redaschi

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPros predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.


Methods in Enzymology | 1996

SSAP: sequential structure alignment program for protein structure comparison.

Christine A. Orengo; William R. Taylor

Publisher Summary This chapter discusses the methods that are flexible enough to align distantly related structures and, therefore, most suitable for identifying and analyzing protein fold families. It illustrates different ways of overcoming the various difficulties encountered and discusses the method, sequential structure alignment program (SSAP) and the various modifications that are required to handle complex similarities. The need to identify similar motifs among proteins and the development of a multiple comparison method that can identify the consensus structure for a family of related proteins are discussed. Analysis of protein structural families and identification of consensus structural templates will improve both template- and threading-based prediction algorithms for identifying the fold of a protein, particularly for the superfolds. For more complex and less frequently observed folds, characterization of common structural motifs and any associated sequence patterns would be expected to improve prediction.


Nucleic Acids Research | 2007

The CATH domain structure database: new protocols and classification levels give a more comprehensive resource for exploring evolution

Lesley H. Greene; Tony E. Lewis; Sarah Addou; Alison L. Cuff; Timothy Dallman; Mark Dibley; Oliver Redfern; Frances M. G. Pearl; Rekha Nambudiry; Adam J. Reid; Ian Sillitoe; Corin Yeats; Janet M. Thornton; Christine A. Orengo

We report the latest release (version 3.0) of the CATH protein domain database (). There has been a 20% increase in the number of structural domains classified in CATH, up to 86 151 domains. Release 3.0 comprises 1110 fold groups and 2147 homologous superfamilies. To cope with the increases in diverse structural homologues being determined by the structural genomics initiatives, more sensitive methods have been developed for identifying boundaries in multi-domain proteins and for recognising homologues. The CATH classification update is now being driven by an integrated pipeline that links these automated procedures with validation steps, that have been made easier by the provision of information rich web pages summarising comparison scores and relevant links to external sites for each domain being classified. An analysis of the population of domains in the CATH hierarchy and several domain characteristics are presented for version 3.0. We also report an update of the CATH Dictionary of homologous structures (CATH-DHS) which now contains multiple structural alignments, consensus information and functional annotations for 1459 well populated superfamilies in CATH. CATH is directly linked to the Gene3D database which is a projection of CATH structural data onto ∼2 million sequences in completed genomes and UniProt.

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Janet M. Thornton

European Bioinformatics Institute

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Ian Sillitoe

University College London

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David A. Lee

Queen Mary University of London

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Corin Yeats

University College London

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Oliver Redfern

University College London

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Annabel E. Todd

University College London

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David Jones

University College London

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