Biomedical subjects

Julian Gough

Publications and source records attributed to Julian Gough.

At least 19 recordsLinked to original sources

PubMed2007

New developments in the InterPro database.

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 (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro). The InterProScan search tool is now also available via a web service at http://www.ebi.ac.uk/Tools/webservices/WSInterProScan.html.

Databases, Protein↗

PubMed2006

The SUPERFAMILY database in 2007: families and functions.

The SUPERFAMILY database provides protein domain assignments, at the SCOP 'superfamily' level, for the predicted protein sequences in over 400 completed genomes. A superfamily groups together domains of different families which have a common evolutionary ancestor based on structural, functional and sequence data. SUPERFAMILY domain assignments are generated using an expert curated set of profile hidden Markov models. All models and structural assignments are available for browsing and download from http://supfam.org. The web interface includes services such as domain architectures and alignment details for all protein assignments, searchable domain combinations, domain occurrence network visualization, detection of over- or under-represented superfamilies for a given genome by comparison with other genomes, assignment of manually submitted sequences and keyword searches. In this update we describe the SUPERFAMILY database and outline two major developments: (i) incorporation of family level assignments and (ii) a superfamily-level functional annotation. The SUPERFAMILY database can be used for general protein evolution and superfamily-specific studies, genomic annotation, and structural genomics target suggestion and assessment.

Databases, Protein↗

PubMed2006

Genomic scale sub-family assignment of protein domains.

Many classification schemes for proteins and domains are either hierarchical or semi-hierarchical yet most databases, especially those offering genome-wide analysis, only provide assignments to sequences at one level of their hierarchy. Given an established hierarchy, the problem of assigning new sequences to lower levels of that existing hierarchy is less hard (but no less important) than the initial top level assignment which requires the detection of the most distant relationships. A solution to this problem is described here in the form of a new procedure which can be thought of as a hybrid between pairwise and profile methods. The hybrid method is a general procedure that can be applied to any pre-defined hierarchy, at any level, including in principle multiple sub-levels. It has been tested on the SCOP classification via the SUPERFAMILY database and performs significantly better than either pairwise or profile methods alone. Perhaps the greatest advantage of the hybrid method over other possible approaches to the problem is that within the framework of an existing profile library, the assignments are fully automatic and come at almost no additional computational cost. Hence it has already been applied at the SCOP family level to all genomes in the SUPERFAMILY database, providing a wealth of new data to the biological and bioinformatics communities.

Computational Biology↗

PubMed2006

Distinguishing protein-coding from non-coding RNAs through support vector machines.

RIKEN's FANTOM project has revealed many previously unknown coding sequences, as well as an unexpected degree of variation in transcripts resulting from alternative promoter usage and splicing. Ever more transcripts that do not code for proteins have been identified by transcriptome studies, in general. Increasing evidence points to the important cellular roles of such non-coding RNAs (ncRNAs). The distinction of protein-coding RNA transcripts from ncRNA transcripts is therefore an important problem in understanding the transcriptome and carrying out its annotation. Very few in silico methods have specifically addressed this problem. Here, we introduce CONC (for "coding or non-coding"), a novel method based on support vector machines that classifies transcripts according to features they would have if they were coding for proteins. These features include peptide length, amino acid composition, predicted secondary structure content, predicted percentage of exposed residues, compositional entropy, number of homologs from database searches, and alignment entropy. Nucleotide frequencies are also incorporated into the method. Confirmed coding cDNAs for eukaryotic proteins from the Swiss-Prot database constituted the set of true positives, ncRNAs from RNAdb and NONCODE the true negatives. Ten-fold cross-validation suggested that CONC distinguished coding RNAs from ncRNAs at about 97% specificity and 98% sensitivity. Applied to 102,801 mouse cDNAs from the FANTOM3 dataset, our method reliably identified over 14,000 ncRNAs and estimated the total number of ncRNAs to be about 28,000.

Animals↗

PubMed2006

A phylogenomic profile of globins.

BACKGROUND: Globins occur in all three kingdoms of life: they can be classified into single-domain globins and chimeric globins. The latter comprise the flavohemoglobins with a C-terminal FAD-binding domain and the gene-regulating globin coupled sensors, with variable C-terminal domains. The single-domain globins encompass sequences related to chimeric globins and "truncated" hemoglobins with a 2-over-2 instead of the canonical 3-over-3 alpha-helical fold. RESULTS: A census of globins in 26 archaeal, 245 bacterial and 49 eukaryote genomes was carried out. Only approximately 25% of archaea have globins, including globin coupled sensors, related single domain globins and 2-over-2 globins. From one to seven globins per genome were found in approximately 65% of the bacterial genomes: the presence and number of globins are positively correlated with genome size. Globins appear to be mostly absent in Bacteroidetes/Chlorobi, Chlamydia, Lactobacillales, Mollicutes, Rickettsiales, Pastorellales and Spirochaetes. Single domain globins occur in metazoans and flavohemoglobins are found in fungi, diplomonads and mycetozoans. Although red algae have single domain globins, including 2-over-2 globins, the green algae and ciliates have only 2-over-2 globins. Plants have symbiotic and nonsymbiotic single domain hemoglobins and 2-over-2 hemoglobins. Over 90% of eukaryotes have globins: the nematode Caenorhabditis has the most putative globins, approximately 33. No globins occur in the parasitic, unicellular eukaryotes such as Encephalitozoon, Entamoeba, Plasmodium and Trypanosoma. CONCLUSION: Although Bacteria have all three types of globins, Archaeado not have flavohemoglobins and Eukaryotes lack globin coupled sensors. Since the hemoglobins in organisms other than animals are enzymes or sensors, it is likely that the evolution of an oxygen transport function accompanied the emergence of multicellular animals.

Amino Acid Sequence↗

PubMed2006

A database of bacterial lipoproteins (DOLOP) with functional assignments to predicted lipoproteins.

Lipid modification of the N-terminal Cys residue (N-acyl-S-diacylglyceryl-Cys) has been found to be an essential, ubiquitous, and unique bacterial posttranslational modification. Such a modification allows anchoring of even highly hydrophilic proteins to the membrane which carry out a variety of functions important for bacteria, including pathogenesis. Hence, being able to identify such proteins is of great value. To this end, we have created a comprehensive database of bacterial lipoproteins, called DOLOP, which contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. The website also features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include the following: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins. The website, along with additional information on the biosynthetic pathway, statistics on predicted lipoproteins, and related figures, is available at http://www.mrc-lmb.cam.ac.uk/genomes/dolop/.

Bacterial Proteins↗

PubMed2006

Transcript annotation in FANTOM3: mouse gene catalog based on physical cDNAs.

The international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM2, comprised 60,770 full-length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein-coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full-length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web-based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full-length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding (including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full-length cDNAs. The total number of distinct non-protein-coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and final expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.

Animals↗

PubMed2005

Three globin lineages belonging to two structural classes in genomes from the three kingdoms of life.

Although most globins, including the N-terminal domains within chimeric proteins such as flavohemoglobins and globin-coupled sensors, exhibit a 3/3 helical sandwich structure, many bacterial, plant, and ciliate globins have a 2/2 helical sandwich structure. We carried out a comprehensive survey of globins in the genomes from the three kingdoms of life. Bayesian phylogenetic trees based on manually aligned sequences indicate the possibility of past horizontal globin gene transfers from bacteria to eukaryotes. blastp searches revealed the presence of 3/3 single-domain globins related to the globin domains of the bacterial and fungal flavohemoglobins in many bacteria, a red alga, and a diatom. Iterated psi-blast searches based on groups of globin sequences found that only the single-domain globins and flavohemoglobins recognize the eukaryote 3/3 globins, including vertebrate neuroglobins, alpha- and beta-globins, and cytoglobins. The 2/2 globins recognize the flavohemoglobins, as do the globin coupled sensors and the closely related single-domain protoglobins. However, the 2/2 globins and the globin-coupled sensors do not recognize each other. Thus, all globins appear to be distributed among three lineages: (i) the 3/3 plant and metazoan globins, single-domain globins, and flavohemoglobins; (ii) the bacterial 3/3 globin-coupled sensors and protoglobins; and (iii) the bacterial, plant, and ciliate 2/2 globins. The three lineages may have evolved from an ancestral 3/3 or 2/2 globin. Furthermore, it appears likely that the predominant functions of globins are enzymatic and that oxygen transport is a specialized development that accompanied the evolution of metazoans.

Bayes Theorem↗

PubMed2005

InterPro, progress and status in 2005.

InterPro, an integrated documentation resource of protein families, domains and functional sites, was created to integrate the major protein signature databases. Currently, it includes PROSITE, Pfam, PRINTS, ProDom, SMART, TIGRFAMs, PIRSF and SUPERFAMILY. Signatures are manually integrated into InterPro entries that are curated to provide biological and functional information. Annotation is provided in an abstract, Gene Ontology mapping and links to specialized databases. New features of InterPro include extended protein match views, taxonomic range information and protein 3D structure data. One of the new match views is the InterPro Domain Architecture view, which shows the domain composition of protein matches. Two new entry types were introduced to better describe InterPro entries: these are active site and binding site. PIRSF and the structure-based SUPERFAMILY are the latest member databases to join InterPro, and CATH and PANTHER are soon to be integrated. InterPro release 8.0 contains 11 007 entries, representing 2573 domains, 8166 families, 201 repeats, 26 active sites, 21 binding sites and 20 post-translational modification sites. InterPro covers over 78% of all proteins in the Swiss-Prot and TrEMBL components of UniProt. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro).

Databases, Protein↗

PubMed2004

Convergent evolution of domain architectures (is rare).

MOTIVATION: In this paper, we shall examine the evolution of domain architectures across 62 genomes of known phylogeny including all kingdoms of life. We look in particular at the possibility of convergent evolution, with a view to determining the extent to which the architectures observed in the genomes are due to functional necessity or evolutionary descent. We used domains of known structure, because from this and other information we know their evolutionary relationships. We use a range of methods including phylogenetic grouping, sequence similarity/alignment, mutation rates and comparative genomics to approach this difficult problem from several angles. RESULTS: Although we do not claim an exhaustive analysis, we conclude that between 0.4 and 4% of sequences are involved in convergent evolution of domain architectures, and expect the actual number to be close to the lower bound. We also made two incidental observations, albeit on a small sample: the events leading to convergent evolution appear to be random with no functional or structural preferences, and changes in the number of tandem repeat domains occur more readily than changes which alter the domain composition. CONCLUSION: The principal conclusion is that the observed domain architectures of the sequences in the genomes are driven by evolutionary descent rather than functional necessity. CONTACT: gough@supfam.org.

Conserved Sequence↗

PubMed2004

Supra-domains: evolutionary units larger than single protein domains.

Domains are the evolutionary units that comprise proteins, and most proteins are built from more than one domain. Domains can be shuffled by recombination to create proteins with new arrangements of domains. Using structural domain assignments, we examined the combinations of domains in the proteins of 131 completely sequenced organisms. We found two-domain and three-domain combinations that recur in different protein contexts with different partner domains. The domains within these combinations have a particular functional and spatial relationship. These units are larger than individual domains and we term them "supra-domains". Amongst the supra-domains, we identified some 1400 (1203 two-domain and 166 three-domain) combinations that are statistically significantly over-represented relative to the occurrence and versatility of the individual component domains. Over one-third of all structurally assigned multi-domain proteins contain these over-represented supra-domains. This means that investigation of the structural and functional relationships of the domains forming these popular combinations would be particularly useful for an understanding of multi-domain protein function and evolution as well as for genome annotation. These and other supra-domains were analysed for their versatility, duplication, their distribution across the three kingdoms of life and their functional classes. By examining the three-dimensional structures of several examples of supra-domains in different biological processes, we identify two basic types of spatial relationships between the component domains: the combined function of the two domains is such that either the geometry of the two domains is crucial and there is a tight constraint on the interface, or the precise orientation of the domains is less important and they are spatially separate. Frequently, the role of the supra-domain becomes clear only once the three-dimensional structure is known. Since this is the case for only a quarter of the supra-domains, we provide a list of the most important unknown supra-domains as potential targets for structural genomics projects.

Animals↗

PubMed2004

The SUPERFAMILY database in 2004: additions and improvements.

The SUPERFAMILY database provides structural assignments to protein sequences and a framework for analysis of the results. At the core of the database is a library of profile Hidden Markov Models that represent all proteins of known structure. The library is based on the SCOP classification of proteins: each model corresponds to a SCOP domain and aims to represent an entire superfamily. We have applied the library to predicted proteins from all completely sequenced genomes (currently 154), the Swiss-Prot and TrEMBL databases and other sequence collections. Close to 60% of all proteins have at least one match, and one half of all residues are covered by assignments. All models and full results are available for download and online browsing at http://supfam.org. Users can study the distribution of their superfamily of interest across all completely sequenced genomes, investigate with which other superfamilies it combines and retrieve proteins in which it occurs. Alternatively, concentrating on a particular genome as a whole, it is possible first, to find out its superfamily composition, and secondly, to compare it with that of other genomes to detect superfamilies that are over- or under-represented. In addition, the webserver provides the following standard services: sequence search; keyword search for genomes, superfamilies and sequence identifiers; and multiple alignment of genomic, PDB and custom sequences.

Animals↗

PubMed2004

The linked conservation of structure and function in a family of high diversity: the monomeric cupredoxins.

The monomeric cupredoxins are a highly divergent family of copper binding electron transport proteins that function in photosynthesis and respiration. To determine how function and structure are conserved in the context of large sequence differences, we have carried out a detailed analysis of the cupredoxins of known structure and their sequence homologs. The common structure of the cupredoxins is formed by a sandwich of two beta sheets which support a copper binding site. The structure of the deeply buried core is intimately coupled to the binding site on the surface of the protein; in each protein the conserved regions form one continuous substructure that extends from the surface active site and through the center of the molecule. Residues around the active site are conserved for functional reasons, while those deeper in the structure will be conserved for structural reasons. Together the two sets support each other.

Azurin↗

PubMed2003

Functional map and domain structure of MET, the product of the c-met protooncogene and receptor for hepatocyte growth factor/scatter factor.

Little is known about the large ectodomain of MET, the product of the c-met protooncogene and receptor for hepatocyte growth factor/scatter factor (HGF/SF). Here, we establish by deletion mutagenesis that the HGF/SF and heparin-binding sites of MET are contained within a large N-terminal domain spanning the alpha-chain (amino acids 25-307) and the first 212 amino acids of the beta-chain (amino acids 308-519). Neither the cystine-rich domain (amino acids 520-561) nor the C-terminal half of MET (amino acids 562-932) bind HGF/SF or heparin directly. The MET ectodomain, which behaves as a rod-shaped monomer with a large Stokes radius in solution, binds HGF/SF in the absence or presence of heparin, and forms a stable HGF/SF-heparin-MET complex with 1:1:1 stoichiometry. We also show that the ligand-binding domain adopts a beta-propeller fold, which is similar to the N-terminal domain of alphaV integrin, and that the C-terminal half contains four Ig domains (amino acids 563-654, 657-738, 742-836, and 839-924) of the unusual structural E set, which could be modeled on bacterial enzymes. Our studies provide 3D models and a functional map of the MET ectodomain. They have broad implications for structure-function of the MET receptor and the related semaphorin and plexin proteins.

Amino Acid Sequence↗

PubMed2003

Evolution of the protein repertoire.

Most proteins have been formed by gene duplication, recombination, and divergence. Proteins of known structure can be matched to about 50% of genome sequences, and these data provide a quantitative description and can suggest hypotheses about the origins of these processes.

Amino Acid Sequence↗

PubMed2003

Mouse proteome analysis.

A general overview of the protein sequence set for the mouse transcriptome produced during the FANTOM2 sequencing project is presented here. We applied different algorithms to characterize protein sequences derived from a nonredundant representative protein set (RPS) and a variant protein set (VPS) of the mouse transcriptome. The functional characterization and assignment of Gene Ontology terms was done by analysis of the proteome using InterPro. The Superfamily database analyses gave a detailed structural classification according to SCOP and provide additional evidence for the functional characterization of the proteome data. The MDS database analysis revealed new domains which are not presented in existing protein domain databases. Thus the transcriptome gives us a unique source of data for the detection of new functional groups. The data obtained for the RPS and VPS sets facilitated the comparison of different patterns of protein expression. A comparison of other existing mouse and human protein sequence sets (e.g., the International Protein Index) demonstrates the common patterns in mammalian proteomes. The analysis of the membrane organization within the transcriptome of multiple eukaryotes provides valuable statistics about the distribution of secretory and transmembrane proteins

Animals↗

PubMed2003

Development and evaluation of an automated annotation pipeline and cDNA annotation system.

Manual curation has long been held to be the "gold standard" for functional annotation of DNA sequence. Our experience with the annotation of more than 20,000 full-length cDNA sequences revealed problems with this approach, including inaccurate and inconsistent assignment of gene names, as well as many good assignments that were difficult to reproduce using only computational methods. For the FANTOM2 annotation of more than 60,000 cDNA clones, we developed a number of methods and tools to circumvent some of these problems, including an automated annotation pipeline that provides high-quality preliminary annotation for each sequence by introducing an "uninformative filter" that eliminates uninformative annotations, controlled vocabularies to accurately reflect both the functional assignments and the evidence supporting them, and a highly refined, Web-based manual annotation tool that allows users to view a wide array of sequence analyses and to assign gene names and putative functions using a consistent nomenclature. The ultimate utility of our approach is reflected in the low rate of reassignment of automated assignments by manual curation. Based on these results, we propose a new standard for large-scale annotation, in which the initial automated annotations are manually investigated and then computational methods are iteratively modified and improved based on the results of manual curation.

Animals↗

PubMed2002

The SUPERFAMILY database in structural genomics.

The SUPERFAMILY hidden Markov model library representing all proteins of known structure predicts the domain architecture of protein sequences and classifies them at the SCOP superfamily level. This analysis has been carried out on all completely sequenced genomes. The ways in which the database can be useful to crystallographers is discussed, in particular with a view to high-throughput structure determination. The application of the SUPERFAMILY database to different target-selection strategies is suggested: novel folds, novel domain combinations and targeted attacks on genomes. Use of the database for more general inquiry in the context of structural studies is also explained. The database provides evolutionary relationships between target proteins and other proteins of known structure through the SCOP database, genome assignments and multiple sequence alignments.

Amino Acid Sequence↗