The European Bioinformatics Institute (EBI) databases

Rodriguez-Tomé, Patricia; Stoehr, Peter; Cameron, Graham; Flores, Tomas P.

doi:10.1093/nar/22.17.3445

Cited by 136 publications

(38 citation statements)

References 57 publications

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“…studied were manually aligned and compared with reference sequences of members of the gamma subdivision of the class Proteobacteria by focusing on members of the most closely related genera. The reference sequences were obtained from public databases (EMBL [5] and Ribosomal Database Project [ 151). The intrageneric relationships of the Acinetobacter spp.…”

Section: Materials and Methods Bacterial Strainsmentioning

confidence: 99%

Phylogenetic Relationship of the Twenty-One DNA Groups of the Genus Acinetobacter as Revealed by 16S Ribosomal DNA Sequence Analysis

Ibrahim

Gerner‐Smidt

Liesack

1997

International Journal of Systematic Bacteriology

105

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The inter-and intrageneric relationships of members of the genus Acinetobacter were investigated by performing a comparative sequence analysis of PCR-amplified 16s ribosomal DNAs (rDNAs) from 21 strains representing all of the DNA groups that have been described. Phylogenetic treeing confirmed that Acinetobacter spp. form a coherent cluster within the gamma subdivision of the class Proteobacteria that includes strains with overall levels of 16s rDNA sequence similarity of more than 94%. The analysis of intrageneric relationships suggested that the majority of the strains cluster in five clearly distinguishable clusters, and this conclusion was supported by the results obtained with the different methods used for phylogenetic analysis (i.e., the maximum-likelihood, parsimony, and distance matrix methods). The first cluster contains the representatives of DNA groups 2 (Acinetobacter baumannii) and TU13, whereas the second cluster comprises representatives of DNA groups 3, "Close To TU13," and "between 1 and 3." The representatives of closely related Acinetobacter DNA groups 8 (Acinetobacter Zwofii) and 9 belong to the third cluster, which includes the representative of DNA group 6 as well. The fourth cluster is formed by DNA groups BJ15, BJ16, and BJ17, and the fifth cluster comprises DNA groups 1 (Acinetobacter calcoaceticus), BJ14, 10, and 11. Within the fifth cluster the 16s rDNA sequences of DNA group 10 and 11 strains are nearly identical. The representatives of DNA groups 4 (Acinetobacter haemolyticus) , 5 (Acinetobacter junii) , 7 (Acinetobacter johnsonii) , 12 (Acinetobacter radioresistens), TU14, and TU15 form individual branches that are not significantly affiliated with any of the five clusters identified. Apart from the clustering of the most closely related DNA groups, the general topology of the distance dendrogram revealed some discrepancy with previous DNA-DNA hybridization data, which may point to the inadequacy of comparative 16s rDNA sequence analysis for reflecting true evolutionary relationships of closely related bacterial taxa. Important, however, was the presence of unique sequence motifs in each of the 21 different DNA groups studied, which may be useful for rapid differentiation of DNA groups of the genus Acinetobacter.Members of the genus Acinetobacter are ubiquitously distributed in nature. They are gram-negative, strictly aerobic, nonmotile, diplococcoid rods that are oxidase negative and catalase positive. Taxonomically, the genus Acinetobacter has a rather long and complicated history. Although at least 21 different DNA groups (genomospecies) have been reported in the literature, only seven species have been delineated. The most recent classification of members of the genus Acinetobacter is the classification of Bouvet and Grimont (1). A total of 85 strains were characterized by DNA-DNA hybridization, and 74 strains were placed in 12 different DNA groups, each comprising strains that exhibited more than 70% DNA-DNA relatedness and AT, values of 5°C or less (AT, is the difference betw...

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Section: Materials and Methods Bacterial Strainsmentioning

confidence: 99%

Phylogenetic Relationship of the Twenty-One DNA Groups of the Genus Acinetobacter as Revealed by 16S Ribosomal DNA Sequence Analysis

Ibrahim

Gerner‐Smidt

Liesack

1997

International Journal of Systematic Bacteriology

105

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“…When the SwissProt (Bairoch and Boeckmann, 1994; release 32) and translated EMBL database (Emmert et al, 1994;release 45) were searched by using the program Blast (Altschul et al, 1990) the most significant hit was a sequence from yeast with the protein identification (PID) number G609377. This sequence, contained in chromosome XI1 of Saccharomyces cerevisiue (accession number U19027), has been putatively assigned to kynureninase, based on sequence similarity to a protein fragment in the PIR database (George et al, 1994) annotated to belong to rat kynureninase (accession number PS0370).…”

Section: S-t30mentioning

confidence: 99%

Isolation and Expression of a cDNA Clone Encoding Human Kynureninase

Alberati-Giani

Buchli

Malherbe

et al. 1996

European Journal of Biochemistry

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Kynureninase (L-kynurenine hydrolase), a pyridoxal-5'-phosphate-(pyridoxal-P)-depe11dent enzyme, catalyses the cleavage of L-kynurenine and L-3-hydroxykynurenine into anthranilic and 3-hydroxyanthranilic acids, respectively. In this report, we describe the isolation of a cDNA clone encoding human kynureninase. Degenerate oligonucleotides designed from the amino acid sequences of peptides from rat liver kynureninase, were used as primers for reverse-transcription PCR of rat kidney RNA. The resulting rat cDNA product was then used to screen a human hepatoma cell line (Hep G,) cDNA library. Analysis of a positive cDNA clone showed the presence of an insert of 1651 nucleotides containing an open reading frame coding for a protein of 456 amino acids (theoretical molecular mass = 52357 Da). The predicted amino acid sequence of human kynureninase displayed high similarity to that reported for the rat enzyme and to a Sarcharomyces cerevisiae gene product putatively ascribed to kynureninase. Profile analysis of kynureninase primary structure indicated the presence of a pyridoxal-P-binding site consensus sequence assigned to class-V aminotransferases, with Lys276 being the residue binding the cofactor. RNA blot analysis of human tissues, including brain, showed the presence of an -2.0-kb mRNA species in all tissues tested. A second mRNA species (-2.6 kb) was also detected in some tissues. After transfection of HEK-293 cells with the cDNA coding for kynureninase, the K,,, values of L-kynurenine and DL-3-hydroxykynurenine for the recombinant enzyme were 671 -C 37 pM and 13.2 t 2.0 pM, respectively.

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“…Duplicate entries of HLA-A2 binders were deleted as were non-9-mer peptides, resulting in a total of 485 binders. Since experimentally confirmed non-binders are relatively sparse, we generated 9-mers utilizing amino acid frequencies as in SwissProt (Emmert et al (1994)) and make the assumption that such randomly generated 9mers are highly unlikely to bind to the MHC. This was reinforced by deleting any generated peptide (from the non-binder pool) that was present in the binding pools, giving rise to a total of 500 non-binders.…”

Section: Hla-a2mentioning

confidence: 99%

Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation

Xiao¹,

Segal²

2005

Statistical Applications in Genetics and Molecular Biology

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Identification of peptides binding to Major Histocompatibility Complex (MHC) molecules is important for accelerating vaccine development and improving immunotherapy. Accordingly, a wide variety of prediction methods have been applied in this context. In this paper, we introduce (tree-based) ensemble classifiers for such problems and contrast their predictive performance with forefront existing methods for both MHC class I and class II molecules. In addition, we investigate the impact of differing peptide representation schemes on performance. Finally, classifier predictions are used to conduct genomewide scans of a diverse collection of HIV-1 strains, enabling assessment of epitope conservation. We investigated all combinations of six classification methods (classification trees, artificial neural networks, support vector machines, as well as the more recently devised ensemble methods (bagging, random forests, boosting) with four peptide representation schemes (amino acid sequence, select biophysical properties, select quantitative structure-activity relationship (QSAR) descriptors, and the combination of the latter two) in predicting peptide binding to an MHC class I molecule (HLA-A2) and MHC class II molecule (HLA-DR4). Our results show that the ensemble methods are consistently more accurate than the other three alternatives. Furthermore, they are robust with respect to parameter tuning. Among the four representation schemes, the amino acid sequence representation gave consistently (across classifiers) best results. This finding obviates the need for feature selection strategies incurred by use of biophysical and/or QSAR properties. We obtained, and aligned, a diverse set of 32 HIV-1 genomes and pursued genomewide HLA-DR4 epitope profiling by querying with respect to classifier predictions, as obtained under each of the four peptide representation schemes. We validated those epitopes conserved across strains against known T-cell epitopes. Once again, amino acid sequence representation was at least as effective as using properties. Assessment of novel epitope predictions awaits experimental verification.

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The European Bioinformatics Institute (EBI) databases

Cited by 136 publications

References 57 publications

Phylogenetic Relationship of the Twenty-One DNA Groups of the Genus Acinetobacter as Revealed by 16S Ribosomal DNA Sequence Analysis

Phylogenetic Relationship of the Twenty-One DNA Groups of the Genus Acinetobacter as Revealed by 16S Ribosomal DNA Sequence Analysis

Isolation and Expression of a cDNA Clone Encoding Human Kynureninase

Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation

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