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PubMed2003

HLA nomenclature and the IMGT/HLA sequence database.

Early in their study it was recognized that the genes encoding the HLA molecules were highly polymorphic and that there was a need for a systematic nomenclature. The result was the WHO Nomenclature Committee for Factors of the HLA System, which first met in 1968, and laid down the criteria for successive meetings. This committee meets regularly to discuss issues of nomenclature and has published 16 major reports documenting firstly the HLA antigens and more recently the genes and alleles. The standardization of HLA antigenic specificities has been controlled by the exchange of typing reagents and cells in the International Histocompatibility Workshops. Since 1989 when a large number of HLA allele sequences were first analysed and named, the job of curating and maintaining a database of sequences has been of prime importance. In 1998 the IMGT/HLA database became the official repository for HLA sequences. In addition to the nucleotide and protein sequences the database contains information of the cell from which the sequence was obtained. The database which provides tools for sequence analysis and the submission of new data, is updated quarterly and now contains over 1500 HLA allele sequences.

Computational Biology↗

PubMed2001

Combined automated PCR cloning, in vitro transcription/translation and two-dimensional electrophoresis for bacterial proteome analysis.

The most popular approach for proteomics analysis is based on the combination of two-dimensional gel electrophoresis and mass spectrometry (MS). Although very effective, the method suffers from a number of limitations, the most serious one being the necessity to have expensive and sophisticated instrumentation requiring handling by skilled personnel. Here we propose an alternative approach which may offer some advantages over the current methods, at least for some specific applications. The method is based on two-dimensional gel separation of radiolabeled synthetic proteins derived from transcription/translation reactions of linear polymerase chain reaction amplified genes. The gel is autoradiographed and this is superimposed on the sample gel whose protein spots have to be identified. Matching between autoradiographs and sample gel spots allows immediate protein identification. The method has been validated identifying six proteins from a membrane protein preparation of Neisseria meningitidis MC58 strain. All proteins were correctly identified as judged by confirmation analysis with MS. The approach is particularly useful when a specific subset of proteins needs to be identified in a complex protein mixture.

Animals↗

PubMed1998

New goals for the U.S. Human Genome Project: 1998-2003.

The Human Genome Project has successfully completed all the major goals in its current 5-year plan, covering the period 1993-98. A new plan, for 1998-2003, is presented, in which human DNA sequencing will be the major emphasis. An ambitious schedule has been set to complete the full sequence by the end of 2003, 2 years ahead of previous projections. In the course of completing the sequence, a "working draft" of the human sequence will be produced by the end of 2001. The plan also includes goals for sequencing technology development; for studying human genome sequence variation; for developing technology for functional genomics; for completing the sequence of Caenorhabditis elegans and Drosophila melanogaster and starting the mouse genome; for studying the ethical, legal, and social implications of genome research; for bioinformatics and computational studies; and for training of genome scientists.

Animals↗

PubMed2003

Expanding the organismal scope of proteomics: cross-species protein identification by mass spectrometry and its implications.

Due to the limited applicability of conventional protein identification methods to the proteomes of organisms with unsequenced genomes, researchers have developed approaches to identify proteins using mass spectrometry and sequence similarity database searches. Both the integration of mass spectrometry with bioinformatics and genomic sequencing drive the expanding organismal scope of proteomics.

Amino Acid Sequence↗

PubMed2002

Browsing gene banks for Fe2S2 ferredoxins and structural modeling of 88 plant-type sequences: an analysis of fold and function.

One-hundred-and-seventy-nine sequences of Fe2S2 ferredoxins and ferredoxin precursors were identified in and retrieved from currently available protein and cDNA databases. On the basis of their cluster-binding patterns, these sequences were divided into three groups: those containing the CX4CX2CXnC pattern (plant-type ferredoxins), those with the CX5CX2CXnC pattern (adrenodoxins), and those with a different pattern. These three groups contain, respectively, 139, 36, and 4 sequences. After excluding ferredoxin precursors in the first group, two subgroups were identified, again based on their cluster-binding patterns: 88 sequences had the CX4CX2CX29C pattern, and 29 had the CX4CX2CXmC (m not equal 29) pattern. The structures of the 88 ferredoxins with the CX4CX2CX29C pattern were modeled based on the available experimental structures of nine proteins within this same group. The modeling procedure was tested by building structural models for the ferredoxins with known structures. The models resulted, on average, in being within 1 A of the backbone root-mean-square deviation from the corresponding experimental structures. In addition, these structural models were shown to be of high quality by using assessment procedures based on energetic and stereochemical parameters. Thus, these models formed a reliable structural database for this group of ferredoxins, which is meaningful within the framework of current structural genomics efforts. From the analysis of the structural database generated it was observed that the secondary structural elements and the overall three-dimensional structures are maintained throughout the superfamily. In particular, the residues in the hydrophobic core of the protein were found to be either absolutely conserved or conservatively substituted. In addition, certain solvent-accessible charged groups, as well as hydrophobic groups, were found to be conserved to the same degree as the core residues. The patterns of conservation of exposed residues identified the regions of the protein that are critical for its function in electron transfer. An extensive analysis of protein-protein interactions is now possible. Some conserved interactions between residues have been identified and related to structural and/or functional features. All this information could not be obtained from the analyses of the primary sequences alone. Finally, the analysis of the sequences of the related subgroup featuring the CX4CX2CXmC (m not equal 29) cluster-binding pattern in the light of the structural and functional insights provided by the inspection of the mentioned structural database affords some hints on the functional features of ferredoxins belonging to this subgroup.

Amino Acid Sequence↗

PubMedDate not supplied

Potential for false positive identifications from large databases through tandem mass spectrometry.

The biomedical research community at large is increasingly employing shotgun proteomics for large-scale identification of proteins from enzymatic digests. Typically, the approach used to identify proteins and peptides from tandem mass spectral data is based on the matching of experimentally generated tandem mass spectra to the theoretical best match from a protein database. Here, we present the potential difficulties of using such an approach without statistical consideration of the false positive rate, especially when large databases, as are encountered in eukaryotes are considered. This is illustrated by searching a dataset generated from a multidimensional separation of a eukaryotic tryptic digest against an in silico generated random protein database, which generated a significant number of positive matches, even when previously suggested score filtering criteria are used.

Algorithms↗

PubMed1998

Automated protein sequence database classification. I. Integration of compositional similarity search, local similarity search, and multiple sequence alignment.

MOTIVATION: Genome sequencing projects require the periodic application of analysis tools that can classify and multiply align related protein sequence domains. Full automation of this task requires an efficient integration of similarity and alignment techniques. RESULTS: We have developed a fully automated process that classifies entire protein sequence databases, resulting in alignment of the homologous sequences. The successive steps of the procedure are based on compositional and local sequence similarity searches followed by multiple sequence alignments. Global similarities are detected from the pairwise comparison of amino acid and dipeptide compositions of each protein. After the elimination of all but one sequence from each detected cluster of closely related proteins, the remaining sequences are compiled in a suffix tree which is self-compared to detect local sequence similarities. Sets of proteins which share similar sequence segments are then weighted according to their closeness and multiply aligned using a fast hierarchical dynamic programming algorithm. Computational strategies were devised to minimize computer processing time and memory space requirements. The accuracy of the sequence classifications has been evaluated for 12 462 primary structures distributed over 341 known families. The percentage of sequences with missed or incorrect family assignments was 6.8% on the test set. This low error level is only twice that of the manually constructed PROSITE database ( 3.4% ) and is substantially better than that found for the automatically built PRODOM database ( 34.9% ). AVAILABILITY: The resulting database, called DOMO, is available through database search routine SRS at Infobiogen (http://www.infobiogen.fr/srs5/), EBI (http://srs.ebi.ac.uk:5000/) and EMBL (http://www.embl-heidelberg.de/srs5/) World Wide Web sites. CONTACT: gracy@infobiogen.fr

Algorithms↗

PubMed2004

SCOP database in 2004: refinements integrate structure and sequence family data.

The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are hierarchically classified into families, superfamilies, folds and classes. The continual accumulation of sequence and structural data allows more rigorous analysis and provides important information for understanding the protein world and its evolutionary repertoire. SCOP participates in a project that aims to rationalize and integrate the data on proteins held in several sequence and structure databases. As part of this project, starting with release 1.63, we have initiated a refinement of the SCOP classification, which introduces a number of changes mostly at the levels below superfamily. The pending SCOP reclassification will be carried out gradually through a number of future releases. In addition to the expanded set of static links to external resources, available at the level of domain entries, we have started modernization of the interface capabilities of SCOP allowing more dynamic links with other databases. SCOP can be accessed at http://scop.mrc-lmb.cam.ac.uk/scop.

Animals↗

PubMed2004

SMART 4.0: towards genomic data integration.

SMART (Simple Modular Architecture Research Tool) is a web tool (http://smart.embl.de/) for the identification and annotation of protein domains, and provides a platform for the comparative study of complex domain architectures in genes and proteins. The January 2004 release of SMART contains 685 protein domains. New developments in SMART are centred on the integration of data from completed metazoan genomes. SMART now uses predicted proteins from complete genomes in its source sequence databases, and integrates these with predictions of orthology. New visualization tools have been developed to allow analysis of gene intron-exon structure within the context of protein domain structure, and to align these displays to provide schematic comparisons of orthologous genes, or multiple transcripts from the same gene. Other improvements include the ability to query SMART by Gene Ontology terms, improved structure database searching and batch retrieval of multiple entries.

Algorithms↗

PubMed2004

Aptamer database.

The aptamer database is designed to contain comprehensive sequence information on aptamers and unnatural ribozymes that have been generated by in vitro selection methods. Such data are not normally collected in 'natural' sequence databases, such as GenBank. Besides serving as a storehouse of sequences that may have diagnostic or therapeutic utility, the database serves as a valuable resource for theoretical biologists who describe and explore fitness landscapes. The database is updated monthly and is publicly available at http://aptamer. icmb.utexas.edu/.

Base Pairing↗

PubMed2004

ESTHER, the database of the alpha/beta-hydrolase fold superfamily of proteins.

The alpha/beta-hydrolase fold is characterized by a beta-sheet core of five to eight strands connected by alpha-helices to form a alpha/beta/alpha sandwich. In most of the family members the beta-strands are parallels, but some show an inversion in the order of the first strands, resulting in antiparallel orientation. The members of the superfamily diverged from a common ancestor into a number of hydrolytic enzymes with a wide range of substrate specificities, together with other proteins with no recognized catalytic activity. In the enzymes the catalytic triad residues are presented on loops, of which one, the nucleophile elbow, is the most conserved feature of the fold. Of the other proteins, which all lack from one to all of the catalytic residues, some may simply be 'inactive' enzymes while others are known to be involved in surface recognition functions. The ESTHER database (http://bioweb.ensam.inra.fr/esther) gathers and annotates all the published information related to gene and protein sequences of this superfamily, as well as biochemical, pharmacological and structural data, and connects them so as to provide the bases for studying structure-function relationships within the family. The most recent developments of the database, which include a section on human diseases related to members of the family, are described.

Animals↗

PubMed2004

A web server for performing electronic PCR.

'Electronic PCR' (e-PCR) refers to a computational procedure that is used to search DNA sequences for sequence tagged sites (STSs), each of which is defined by a pair of primer sequences and an expected PCR product size. To gain speed, our implementation extracts short 'words' from the 3' end of each primer and stores them in a sorted hash table that can be accessed efficiently during the search. One recent improvement is the use of overlapping discontinuous words to allow matches to be found despite the presence of a mismatch. Moreover, it is possible to allow gaps in the alignment between the primer and the sequence. The effect of these changes is to improve sensitivity without significantly affecting specificity. The new software provides a search mode using a query STS against a sequence database to augment the previously available mode using a query sequence against an STS database. Finally, e-PCR may now be used through a web service, with search results linked to other web resources such as the UniSTS database and the MapViewer genome browser. The e-PCR web server may be found at www.ncbi.nlm.nih.gov/sutils/e-pcr.

Algorithms↗

PubMed2004

FRAGS: estimation of coding sequence substitution rates from fragmentary data.

BACKGROUND: Rates of substitution in protein-coding sequences can provide important insights into evolutionary processes that are of biomedical and theoretical interest. Increased availability of coding sequence data has enabled researchers to estimate more accurately the coding sequence divergence of pairs of organisms. However the use of different data sources, alignment protocols and methods to estimate substitution rates leads to widely varying estimates of key parameters that define the coding sequence divergence of orthologous genes. Although complete genome sequence data are not available for all organisms, fragmentary sequence data can provide accurate estimates of substitution rates provided that an appropriate and consistent methodology is used and that differences in the estimates obtainable from different data sources are taken into account. RESULTS: We have developed FRAGS, an application framework that uses existing, freely available software components to construct in-frame alignments and estimate coding substitution rates from fragmentary sequence data. Coding sequence substitution estimates for human and chimpanzee sequences, generated by FRAGS, reveal that methodological differences can give rise to significantly different estimates of important substitution parameters. The estimated substitution rates were also used to infer upper-bounds on the amount of sequencing error in the datasets that we have analysed. CONCLUSION: We have developed a system that performs robust estimation of substitution rates for orthologous sequences from a pair of organisms. Our system can be used when fragmentary genomic or transcript data is available from one of the organisms and the other is a completely sequenced genome within the Ensembl database. As well as estimating substitution statistics our system enables the user to manage and query alignment and substitution data.

Animals↗

PubMed2004

PathoPlant: a database on plant-pathogen interactions.

Pathogen recognition and signal transduction during plant pathogenesis is essential for the activation of plant defense mechanisms. To facilitate easy access to published data and to permit comparative studies of different pathogen response pathways, a database is indispensable to give a broad overview of the components and reactions so far known. PathoPlant has been developed as a relational database to display relevant components and reactions involved in signal transduction related to plant-pathogen interactions. On the organism level, the tables 'plant', 'pathogen' and 'interaction' are used to describe incompatible interactions between plants and pathogens or diseases. On the molecular level, plant pathogenesis related information is organized in PathoPlant's main tables 'molecule', 'reaction' and 'location'. Signal transduction pathways are modeled as consecutive sequences of known molecules and corresponding reactions. PathoPlant entries are linked to associated internal records as well as to entries in external databases such as SWISS-PROT, GenBank, PubMed, and TRANSFAC. PathoPlant is available as a web-based service at http://www.pathoplant.de.

Computational Biology↗

PubMed2005

u-Genome: a database on genome design in unicellular genomes.

Unicellular eukaryotes were among the first ones to be selected for complete genome sequencing because of the small size of their genomes and their interactions with humans and a broad range of animals and plants. Currently, ten completely sequenced unicellular genome sequences have been publicly released and as the number of available unicellular genomes increases, comparative genomics analysis within this group of organisms becomes more and more instructive. However, such an analysis is difficult to carry out without a suitable platform gathering not only the original annotations but also relevant information available in public databases or obtained by applying common bioinformatics methods. With the aim of solving these difficulties, we have developed a web-accessible database named u-Genome, the unicellular genome design database. The database is unique in featuring three datasets namely (1) orthologous proteins (2) paralogous proteins and (3) statistical distributions on exons, introns, intergenic DNA and correlations between them. A tool, Uniview, designed to visualize the gene structures for individual genes in the genome is also integrated. This database is of importance in understanding unicellular genome design and architecture and evolution related studies. The database is available through a web interface at http://sege.ntu.edu.sg/wester/ugenome.

Animals↗

PubMed2005

Protein classification based on text document classification techniques.

The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k-nearest neighbor (k-NN), hidden markov model (HMM) and support vector machine (SVM) using alignment-based features have suggested that classifiers at the complexity of SVM are needed to attain high accuracy. Here, analogous to document classification, we applied Decision Tree and Naive Bayes classifiers with chi-square feature selection on counts of n-grams (i.e. short peptide sequences of length n) to this classification task. Using the GPCR dataset and evaluation protocol from the previous study, the Naive Bayes classifier attained an accuracy of 93.0 and 92.4% in level I and level II subfamily classification respectively, while SVM has a reported accuracy of 88.4 and 86.3%. This is a 39.7 and 44.5% reduction in residual error for level I and level II subfamily classification, respectively. The Decision Tree, while inferior to SVM, outperforms HMM in both level I and level II subfamily classification. For those GPCR families whose profiles are stored in the Protein FAMilies database of alignments and HMMs (PFAM), our method performs comparably to a search against those profiles. Finally, our method can be generalized to other protein families by applying it to the superfamily of nuclear receptors with 94.5, 97.8 and 93.6% accuracy in family, level I and level II subfamily classification respectively.

Algorithms↗

PubMed2003

Computational knowledge integration in biopharmaceutical research.

An initiative to increase biopharmaceutical research productivity by capturing, sharing and computationally integrating proprietary scientific discoveries with public knowledge is described. This initiative involves both organisational process change and multiple interoperating software systems. The software components rely on mutually supporting integration techniques. These include a richly structured ontology, statistical analysis of experimental data against stored conclusions, natural language processing of public literature, secure document repositories with lightweight metadata, web services integration, enterprise web portals and relational databases. This approach has already begun to increase scientific productivity in our enterprise by creating an organisational memory (OM) of internal research findings, accessible on the web. Through bringing together these components it has also been possible to construct a very large and expanding repository of biological pathway information linked to this repository of findings which is extremely useful in analysis of DNA microarray data. This repository, in turn, enables our research paradigm to be shifted towards more comprehensive systems-based understandings of drug action.

Algorithms↗

PubMed2005

Using bioinformatics for drug target identification from the genome.

Genomics and proteomics technologies have created a paradigm shift in the drug discovery process, with bioinformatics having a key role in the exploitation of genomic, transcriptomic, and proteomic data to gain insights into the molecular mechanisms that underlie disease and to identify potential drug targets. We discuss the current state of the art for some of the bioinformatic approaches to identifying drug targets, including identifying new members of successful target classes and their functions, predicting disease relevant genes, and constructing gene networks and protein interaction networks. In addition, we introduce drug target discovery using the strategy of systems biology, and discuss some of the data resources for the identification of drug targets. Although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires an understanding of the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work.

Alzheimer Disease↗