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PubMed2006

Augur--a computational pipeline for whole genome microbial surface protein prediction and classification.

UNLABELLED: The analysis of protein function is a challenge and a major bottleneck towards well-annotated and analysed microbial genomes. In particular, bacterial surface proteins present an opportunity for pharmacological intervention and vaccine development. We present Augur, an automatic prediction pipeline that integrates major surface prediction algorithms and enables comparative analysis, classification and visualization for gram-positive bacteria on a genomic scale. AVAILABILITY: http://bioinfo.mikrobio.med.uni-giessen.de/augur

Algorithms↗
PubMedDate not supplied

Chemometric analysis of ligand receptor complementarity: identifying Complementary Ligands Based on Receptor Information (CoLiBRI).

We have developed a novel structure-based chemoinformatics approach to search for Complimentary Ligands Based on Receptor Information (CoLiBRI). CoLiBRI is based on the representation of both receptor binding sites and their respective ligands in a space of universal chemical descriptors. The binding site atoms involved in the interaction with ligands are identified by the means of a computational geometry technique known as Delaunay tessellation as applied to X-ray characterized ligand-receptor complexes. TAE/RECON multiple chemical descriptors are calculated independently for each ligand as well as for its active site atoms. The representation of both ligands and active sites using chemical descriptors allows the application of well-known chemometric techniques in order to correlate chemical similarities between active sites and their respective ligands. We have established a protocol to map patterns of nearest neighbor active site vectors in a multidimensional TAE/RECON space onto those of their complementary ligands and vice versa. This protocol affords the prediction of a virtual complementary ligand vector in the ligand chemical space from the position of a known active site vector. This prediction is followed by chemical similarity calculations between this virtual ligand vector and those calculated for molecules in a chemical database to identify real compounds most similar to the virtual ligand. Consequently, the knowledge of the receptor active site structure affords straightforward and efficient identification of its complementary ligands in large databases of chemical compounds using rapid chemical similarity searches. Conversely, starting from the ligand chemical structure, one may identify possible complementary receptor cavities as well. We have applied the CoLiBRI approach to a data set of 800 X-ray characterized ligand-receptor complexes in the PDBbind database. Using a k nearest neighbor (kNN) pattern recognition approach and variable selection, we have shown that knowledge of the active site structure affords identification of its complimentary ligand among the top 1% of a large chemical database in over 90% of all test active sites when a binding site of the same protein family was present in the training set. In the case where test receptors are highly dissimilar and not present among the receptor families in the training set, the prediction accuracy is decreased; however, CoLiBRI was still able to quickly eliminate 75% of the chemical database as improbable ligands. CoLiBRI affords rapid prefiltering of a large chemical database to eliminate compounds that have little chance of binding to a receptor active site.

Algorithms↗
PubMed2004

Protein sequence databases.

A variety of protein sequence databases exist, ranging from simple sequence repositories, which store data with little or no manual intervention in the creation of the records, to expertly curated universal databases that cover all species and in which the original sequence data are enhanced by the manual addition of further information in each sequence record. As the focus of researchers moves from the genome to the proteins encoded by it, these databases will play an even more important role as central comprehensive resources of protein information. Several the leading protein sequence databases are discussed here, with special emphasis on the databases now provided by the Universal Protein Knowledgebase (UniProt) consortium.

Animals↗
PubMedDate not supplied

Proteomics-grade de novo sequencing approach.

The conventional approach in modern proteomics to identify proteins from limited information provided by molecular and fragment masses of their enzymatic degradation products carries an inherent risk of both false positive and false negative identifications. For reliable identification of even known proteins, complete de novo sequencing of their peptides is desired. The main problems of conventional sequencing based on tandem mass spectrometry are incomplete backbone fragmentation and the frequent overlap of fragment masses. In this work, the first proteomics-grade de novo approach is presented, where the above problems are alleviated by the use of complementary fragmentation techniques CAD and ECD. Implementation of a high-current, large-area dispenser cathode as a source of low-energy electrons provided efficient ECD of doubly charged peptides, the most abundant species (65-80%), in a typical trypsin-based proteomics experiment. A new linear de novo algorithm is developed combining efficiency and speed, processing on a conventional 3 GHz PC, 1000 MS/MS data sets in 60 s. More than 6% of all MS/MS data for doubly charged peptides yielded complete sequences, and another 13% gave nearly complete sequences with a maximum gap of two amino acid residues. These figures are comparable with the typical success rates (5-15%) of database identification. For peptides reliably found in the database (Mowse score > or = 34), the agreement with de novo-derived full sequences was >95%. Full sequences were derived in 67% of the cases when full sequence information was present in MS/MS spectra. Thus the new de novo sequencing approach reached the same level of efficiency and reliability as conventional database-identification strategies.

Algorithms↗
PubMed2004

NASCArrays: a repository for microarray data generated by NASC's transcriptomics service.

NASC operates an Affymetrix 'GeneChip' (microarray) service for the Arabidopsis thaliana community. All data produced by the service are publicly available through our microarray data base 'NASCArrays' published at http://affymetrix. arabidopsis.info. The data are accessible through text searching and a series of data mining tools. All data are annotated with sample preparation details, and the original Affymetrix data are available for download. The database aims to be MIAME supportive and provide a coordinated resource for re searchers interested in the transcriptome of Arabidopsis. Using this database, data produced will be shared with other databases worldwide.

Arabidopsis↗
PubMed2000

Drosophila genomic sequence annotation using the BLOCKS+ database.

A simple and general homology-based method for gene finding was applied to the 2.9-Mb Drosophila melanogaster Adh region, the target sequence of the Genome Annotation Assessment Project (GASP). Each strand of the entire sequence was used as query of the BLOCKS+ database of conserved regions of proteins. This led to functional assignments for more than one-third of the genes and two-thirds of the transposons. Considering the enormous size of the query, the fact that only two false-positive matches were reported emphasizes the high selectivity of protein family-based methods for gene finding. We used the search results to improve BLOCKS+ by identifying compositionally biased blocks. Our results confirm that protein family databases can be used effectively in automated sequence annotation efforts.

Alcohol Dehydrogenase↗
PubMed2001

Prediction of protein functional domains from sequences using artificial neural networks.

An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison. Applications to the prediction of function are discussed.

Computational Biology↗
PubMed2004

The aMAZE LightBench: a web interface to a relational database of cellular processes.

The aMAZE LightBench (http://www.amaze.ulb. ac.be/) is a web interface to the aMAZE relational database, which contains information on gene expression, catalysed chemical reactions, regulatory interactions, protein assembly, as well as metabolic and signal transduction pathways. It allows the user to browse the information in an intuitive way, which also reflects the underlying data model. Moreover links are provided to literature references, and whenever appropriate, to external databases.

Biochemical Phenomena↗
PubMed2004

Bioinformatics and cellular signaling.

The understanding of cellular function requires an integrated analysis of context-specific, spatiotemporal data from diverse sources. Recent advances in describing the genomic and proteomic 'parts list' of the cell and deciphering the interrelationship of these parts are described, including genome-wide location analysis, standards for microarray data analysis, and two-hybrid and mass spectrometry approaches. This information is being collected and curated in databases such as the Alliance for Cellular Signaling (AfCS) Molecule Pages, which will serve as vital tools for the reconstruction and analysis of cellular signaling networks.

Cell Physiological Phenomena↗
PubMed2006

Introduction to bioinformatics.

This article introduces the field of bioinformatics and describes bioinformatic approaches and their application to the study of protein allergens. The predominant bioinformatics tools and resources are listed and discussed.

Allergens↗
PubMed2003

How well can we predict native contacts in proteins based on decoy structures and their energies?

One strategy for ab initio protein structure prediction is to generate a large number of possible structures (decoys) and select the most fitting ones based on a scoring or free energy function. The conformational space of a protein is huge, and chances are rare that any heuristically generated structure will directly fall in the neighborhood of the native structure. It is desirable that, instead of being thrown away, the unfitting decoy structures can provide insights into native structures so prediction can be made progressively. First, we demonstrate that a recently parameterized physics-based effective free energy function based on the GROMOS96 force field and a generalized Born/surface area solvent model is, as several other physics-based and knowledge-based models, capable of distinguishing native structures from decoy structures for a number of widely used decoy databases. Second, we observe a substantial increase in correlations of the effective free energies with the degree of similarity between the decoys and the native structure, if the similarity is measured by the content of native inter-residue contacts in a decoy structure rather than its root-mean-square deviation from the native structure. Finally, we investigate the possibility of predicting native contacts based on the frequency of occurrence of contacts in decoy structures. For most proteins contained in the decoy databases, a meaningful amount of native contacts can be predicted based on plain frequencies of occurrence at a relatively high level of accuracy. Relative to using plain frequencies, overwhelming improvements in sensitivity of the predictions are observed for the 4_state_reduced decoy sets by applying energy-dependent weighting of decoy structures in determining the frequency. There, approximately 80% native contacts can be predicted at an accuracy of approximately 80% using energy-weighted frequencies. The sensitivity of the plain frequency approach is much lower (20% to 40%). Such improvements are, however, not observed for the other decoy databases. The rationalization and implications of the results are discussed.

Algorithms↗
PubMed2004

Novel biocatalysis by database mining.

Broad-based adoption of biocatalytic methods will require widely available database tools, analogous to previous efforts compiling information for the facilitation of chemical synthesis. The analog to chemical reagents are enzymes. The analog to chemical synthetic routes are metabolic pathways. The free on-line database BRENDA exemplifies efforts to compile relevant information on enzymes for biocatalytic purposes. Likewise, the University of Minnesota Biocatalysis/Biodegradation Database focuses on novel enzymes and metabolic pathways useful in environmental and industrial biotechnology. The development of biocatalytic protocols will be facilitated by the increasing availability of well-curated database information on enzymatic enantioselectivity and capabilities for transforming disparate chemical functional groups.

Catalysis↗
PubMed2001

Phenotypic data in FlyBase.

Phenotypic analysis combined with molecular genetics is a powerful tool for mapping gene function onto the genome. Phenotypic data are, by their nature, descriptive, and as varied as the range of mutant phenotypes that can be presented by the organism under study. This paper discusses the mechanisms FlyBase has implemented to systematise published phenotypic data about Drosophila, and provides an introduction to the query tools available for the mining of the data. Though FlyBase is specific to Drosophila, the issues faced in devising protocols for capturing, storing and reporting data are the same issues faced by any database with an interest in using phenotypic data to maximise the potential of genomic analysis.

Alleles↗
PubMed2003

Structural classification of zinc fingers: survey and summary.

Zinc fingers are small protein domains in which zinc plays a structural role contributing to the stability of the domain. Zinc fingers are structurally diverse and are present among proteins that perform a broad range of functions in various cellular processes, such as replication and repair, transcription and translation, metabolism and signaling, cell proliferation and apoptosis. Zinc fingers typically function as interaction modules and bind to a wide variety of compounds, such as nucleic acids, proteins and small molecules. Here we present a comprehensive classification of zinc finger spatial structures. We find that each available zinc finger structure can be placed into one of eight fold groups that we define based on the structural properties in the vicinity of the zinc-binding site. Three of these fold groups comprise the majority of zinc fingers, namely, C2H2-like finger, treble clef finger and the zinc ribbon. Evolutionary relatedness of proteins within fold groups is not implied, but each group is divided into families of potential homologs. We compare our classification to existing groupings of zinc fingers and find that we define more encompassing fold groups, which bring together proteins whose similarities have previously remained unappreciated. We analyze functional properties of different zinc fingers and overlay them onto our classification. The classification helps in understanding the relationship between the structure, function and evolutionary history of these domains. The results are available as an online database of zinc finger structures.

Algorithms↗
PubMed2004

The European ribosomal RNA database.

The European ribosomal RNA database aims to compile all complete or nearly complete ribosomal RNA sequences from both the small (SSU) and large (LSU) ribosomal subunits. All sequences are available in aligned format. Sequence alignment is based on the secondary structure of the molecules, as determined by comparative sequence analysis. Additional information about the sequences, such as taxonomic classification of the organism from which they have been obtained, and literature references are also provided. In order to identify the closest relatives to newly determined sequences, BLAST searches can be performed, after which the best matching sequences are aligned and a phylogenetic tree is inferred. As of 2003, the European ribosomal RNA database is maintained at Ghent University (Belgium). The database can be consulted at http://www.psb.ugent.be/rRNA/.

Animals↗
PubMed2006

Enhanced automated function prediction using distantly related sequences and contextual association by PFP.

The impetus for the recent development and emergence of automated function prediction methods is an exponentially growing flood of new experimental data, the interpretation of which is hindered by a shortage of reliable annotations for proteins that lack experimental characterization or significant homologs in current databases. Here we introduce PFP, an automated function prediction server that provides the most probable annotations for a query sequence in each of the three branches of the Gene Ontology: biological process, molecular function, and cellular component. Rather than utilizing precise pattern matching to identify functional motifs in the sequences and structures of these proteins, we designed PFP to increase the coverage of function annotation by lowering resolution of predictions when a detailed function is not predictable. To do this we extend a traditional PSI-BLAST search by extracting and scoring annotations (GO terms) individually, including annotations from distantly related sequences, and applying a novel data mining tool, the Function Association Matrix, to score strongly associated pairs of annotations. We show that PFP can correctly assign function using only weakly similar sequences with a significantly better accuracy and coverage than a standard PSI-BLAST search, improving it more than fivefold. The most descriptive annotations predicted by PFP (GO depth > or = 8) can identify a significant subgraph in the GO with > 60% accuracy and approximately 100% coverage for our benchmark set. We also provide examples of the superb performance of PFP in an assessment of automated function prediction servers at the Automated Function Prediction Special Interest Group meeting at ISMB 2005 (AFP-SIG '05).

Algorithms↗