The construction of a bioactive peptide database in Metazoa

Bioactive peptides play critical roles in regulating most biological processes in animals, and have considerable biological, medical and industrial importance. A number of peptides have been discovered usually based on their biological activities in vitro or based on their sequence similarities in silico. Through searches in Swiss-Prot and Trembl protein databases using BLAST alignment tools and other in silico methods, all currently known bioactive peptides and their precursor proteins are extracted. In addition, 132 recently discovered putative peptide genes in Drosophila as well as their orthologs in other species are collected. In total, 20 027 bioactive peptides from 19 438 precursor proteins covering 2820 metazoan species are retained, and they, respectively, make up a peptide and a peptide precursor database. The peptides and peptide precursor proteins are further classified into 373 families, 178 of which are represented by Prosite Pfam or Smart motifs, or by typical peptide motifs that have been constructed recently. The remaining 195 families are novel peptide families. The motifs characterizing the 178 peptide families are saved into a peptide motif database. The peptide, peptide precursor and peptide motif databases (version 1.0) are the most complete peptide, precursor and peptide motif collection in Metazoa so far. They are available on the WWW at http://www.peptides.be/.     

The homeodomain resource: a prototype database for a large protein family

The Homeodomain Resource is an annotated collection of non-redundant protein sequences, three-dimensional structures and genomic information for the homeodomain protein family. Release 2.0 contains 765 full-length homeodomain-containing sequences, 29 experimentally derived structures and 116 homeobox loci implicated in human genetic disorders. Entries are fully hyperlinked to facilitate easy retrieval of the original records from source databases. A simple search engine with a graphical user interface is provided to query the component databases and assemble customized data sets. A new feature for this release is the addition of more automated methods for database searching, maintenance and implementation of efficient data management. The Homeodomain Resource is freely available through the WWW at http://genome.nhgri.nih.gov/homeodomain     

Availability of short amino acid sequences in proteins

Much attention is being paid to protein databases as an important information source for proteome research. Although used extensively for similarity searches, protein databases themselves have not fully been characterized. In a systematic attempt to reveal protein-database characters that could contribute to revealing how protein chains are constructed, frequency distributions of all possible combinatorial sets of three, four, and five amino acids ("triplets," "quartets," and "pentats"; collectively called constituent sequences) have been examined in the nonredundant (nr) protein database, demonstrating the existence of nonrandom bias in their "availability" at the population level. Nonexistent short sequences of pentats were found that showed low availability in biological proteins against their expected probabilities of occurrence. Among them, six representative ones were successfully synthesized as peptides with reasonably high yields in a conventional Fmoc method, excluding the possibility that a putative physicochemical energy barrier in forming them could be a direct cause for the low availability. They were also expressed as soluble fusion proteins in a conventional Escherichia coli BL21Star(DE3) system with reasonably high yield, again excluding a possible difficulty in their biological synthesis. Together, these results suggest that information on three-dimensional structures and functions of proteins exists in the context of connections of short constituent sequences, and that proteins are composed of evolutionarily selected constituent sequences, which are reflected in their availability differences in the database. These results may have biological implications for protein structural studies.     

GeneRecords: a relational database for GenBank flat file parsing and data manipulation in personal computers

Extracting the desired data from a database entry for later analysis is a constant need in the biological sequence analysis community; GeneRecords 1.0 is a solution for GenBank biological flat file parsing, as it implements a structured representation of each feature and feature qualifier in GenBank following import in a common database managing system usable in a personal computer (Macintosh and Windows environments). This collection of related databases enables the local management of GenBank records, allowing indexing, retrieval and analysis of both information and sequences on a personal computer. AVAILABILITY: The current release, including the FileMaker Pro runtime application (built for Windows and Macintosh environments), is freely available at http://apollo11.isto.unibo.it/software/     

DNA reference alignment benchmarks based on tertiary structure of encoded proteins

MOTIVATION: Multiple sequence alignments (MSAs) are at the heart of bioinformatics analysis. Recently, a number of multiple protein sequence alignment benchmarks (i.e. BAliBASE, OXBench, PREFAB and SMART) have been released to evaluate new and existing MSA applications. These databases have been well received by researchers and help to quantitatively evaluate MSA programs on protein sequences. Unfortunately, analogous DNA benchmarks are not available, making evaluation of MSA programs difficult for DNA sequences. RESULTS: This work presents the first known multiple DNA sequence alignment benchmarks that are (1) comprised of protein-coding portions of DNA (2) based on biological features such as the tertiary structure of encoded proteins. These reference DNA databases contain a total of 3545 alignments, comprising of 68 581 sequences. Two versions of the database are available: mdsa_100s and mdsa_all. The mdsa_100s version contains the alignments of the data sets that TBLASTN found 100% sequence identity for each sequence. The mdsa_all version includes all hits with an E-value score above the threshold of 0.001. A primary use of these databases is to benchmark the performance of MSA applications on DNA data sets. The first such case study is included in the Supplementary Material.     

PhosphoBase, a database of phosphorylation sites: release 2.0.

PhosphoBase contains information about phosphorylated residues in proteins and data about peptide phosphorylation by a variety of protein kinases. The data are collected from literature and compiled into a common format. The current release of PhosphoBase (October 1998, version 2.0) comprises 414 phosphoprotein entries covering 1052 phosphorylatable serine, threonine and tyrosine residues. The kinetic data from peptide phosphorylation assays for approximately 330 oligopeptides is also included. The database entries are cross-referenced to the corresponding records in the Swiss-Prot protein database and literature references are linked to MedLine records. PhosphoBase is available via the WWW at http://www.cbs.dtu. dk/databases/PhosphoBase/     

SYFPEITHI: database for MHC ligands and peptide motifs

 The first version of the major histocompatibility complex (MHC) databank SYFPEITHI: database for MHC ligands and peptide motifs, is now available to the general public. It contains a collection of MHC class I and class II ligands and peptide motifs of humans and other species, such as apes, cattle, chicken, and mouse, for example, and is continuously updated. All motifs currently available are accessible as individual entries. Searches for MHC alleles, MHC motifs, natural ligands, T-cell epitopes, source proteins/organisms and references are possible. Hyperlinks to the EMBL and PubMed databases are included. In addition, ligand predictions are available for a number of MHC allelic products. The database content is restricted to published data only.     

SWISS-PROT: connecting biomolecular knowledge via a protein database

With the explosive growth of biological data, the development of new means of data storage was needed. More and more often biological information is no longer published in the conventional way via a publication in a scientific journal, but only deposited into a database. In the last two decades these databases have become essential tools for researchers in biological sciences. Biological databases can be classified according to the type of information they contain. There are basically three types of sequence-related databases (nucleic acid sequences, protein sequences and protein tertiary structures) as well as various specialized data collections. It is important to provide the users of biomolecular databases with a degree of integration between these databases as by nature all of these databases are connected in a scientific sense and each one of them is an important piece to biological complexity. In this review we will highlight our effort in connecting biological information as demonstrated in the SWISS-PROT protein database.     

HvrBase: compilation of mtDNA control region sequences from primates.

HvrBase is a compilation of human and ape mtDNA control region sequences. Sequences and related information on individuals, such as from where the sequences were obtained, is stored in three ASCII files as described previously. Moreover, the collection is also available as Mac/PC database application with a graphical user interface. It can be accessed through the WWW at URL http://www.eva.mpg.de/hvrbase. The current collection comprises 5846 human sequences from hypervariable region I (HVRI) and 2302 human sequences from hypervariable region II (HVRII). From apes, 295 HVRI sequences and 13 HVRII sequences are available.     

PPMdb: a plant plasma membrane database

PPMdb is a proteome database dedicated to proteins from plant plasma membranes. It provides comprehensive two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) maps, partial amino acid sequences and expression data. All this information is gathered and structured in a relational database, after being analyzed and annotated. PPMdb includes active links to related biological databases (EMBL, GenBank, GenPep, and SWISS-PROT and TrEMBL) as well as to MEDLINE abstracts. Information on specific protein spots can be displayed by clicking on the 2-D maps. In addition, users can query the database by accession number, protein name, pI and MW, and cellular location. Access to PPMdb is available at the following URL: http://sphinx.rug. ac.be:8080.     

PANTHER: a browsable database of gene products organized by biological function,using curated protein family and subfamily classification

The PANTHER database was designed for high-throughput analysis of protein sequences. One of the key features is a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators have associated the ontology terms with groups of protein sequences rather than individual sequences. Statistical models (Hidden Markov Models, or HMMs) are built from each of these groups. The advantage of this approach is that new sequences can be automatically classified as they become available. To ensure accurate functional classification, HMMs are constructed not only for families, but also for functionally distinct subfamilies. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, are available for each family. The current version of the PANTHER database includes training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs have been used to classify gene products across the entire genomes of human, and Drosophila melanogaster. The ontology terms and protein families and subfamilies, as well as Drosophila gene c;assifications, can be browsed and searched for free. Due to outstanding contractual obligations, access to human gene classifications and to protein family trees and multiple sequence alignments will temporarily require a nominal registration fee. PANTHER is publicly available on the web at http://panther.celera.com.     

Analysing proteomic data

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.     

Improved BLAST searches using longer words for protein seeding

MOTIVATION: The blastp and tblastn modules of BLAST are widely used methods for searching protein queries against protein and nucleotide databases, respectively. One heuristic used in BLAST is to consider only database sequences that contain a high-scoring match of length at most 5 to the query. We implemented the capability to use words of length 6 or 7. We demonstrate an improved trade-off between running time and retrieval accuracy, controlled by the score threshold used for short word matches. For example, the running time can be reduced by 20-30% while achieving ROC (receiver operator characteristic) scores similar to those obtained with current default parameters. AVAILABILITY: The option to use long words is in the NCBI C and C++ toolkit code for BLAST, starting with version 2.2.16 of blastall. A Linux executable used to produce the results herein is available at: ftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/protein_longwords     

The construction of Arabidopsis expressed sequence tag assemblies. A new resource to facilitate gene identification.

The generation of large numbers of partial cDNA sequences, or expressed sequence tags (ESTs), has provided a method with which to sample a large number of genes from an organism. More than 25,000 Arabidopsis thaliana ESTs have been deposited in public databases, producing the largest collection of ESTs for any plant species. We describe here the application of a method of reducing redundancy and increasing information content in this collection by grouping overlapping ESTs representing the same gene into a "contig" or assembly. The increased information content of these assemblies allows more putative identifications to be assigned based on the results of similarity searches with nucleotide and protein databases. The results of this analysis indicate that sequence information is available for approximately 12,600 nonoverlapping ESTs from Arabidopsis. Comparison of the assemblies with 953 Arabidopsis coding sequences indicates that up to 57% of all Arabidopsis genes are represented by an EST. Clustering analysis of these sequences suggests that between 300 and 700 gene families are represented by between 700 and 2000 sequences in the EST database. A database of the assembled sequences, their putative identifications, and cellular roles is available through the World Wide Web.