Meta-All: a system for managing metabolic pathway information

Background  

Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed.     

The Subviral RNA Database: a toolbox for viroids, the hepatitis delta virus and satellite RNAs research

Background  

Viroids, satellite RNAs, satellites viruses and the human hepatitis delta virus form the 'brotherhood' of the smallest known infectious RNA agents, known as the subviral RNAs. For most of these species, it is generally accepted that characteristics such as cell movement, replication, host specificity and pathogenicity are encoded in their RNA sequences and their resulting RNA structures. Although many sequences are indexed in publicly available databases, these sequence annotation databases do not provide the advanced searches and data manipulation capability for identifying and characterizing subviral RNA motifs.     

In Silico Characterization of Proteins: UniProt,InterPro and Integr8

Nucleic acid sequences from genome sequencing projects are submitted as raw data, from which biologists attempt to elucidate the function of the predicted gene products. The protein sequences are stored in public databases, such as the UniProt Knowledgebase (UniProtKB), where curators try to add predicted and experimental functional information. Protein function prediction can be done using sequence similarity searches, but an alternative approach is to use protein signatures, which classify proteins into families and domains. The major protein signature databases are available through the integrated InterPro database, which provides a classification of UniProtKB sequences. As well as characterization of proteins through protein families, many researchers are interested in analyzing the complete set of proteins from a genome (i.e. the proteome), and there are databases and resources that provide non-redundant proteome sets and analyses of proteins from organisms with completely sequenced genomes. This article reviews the tools and resources available on the web for single and large-scale protein characterization and whole proteome analysis.     

An analysis of the Sargasso Sea resource and the consequences for database composition

Background  

The environmental sequencing of the Sargasso Sea has introduced a huge new resource of genomic information. Unlike the protein sequences held in the current searchable databases, the Sargasso Sea sequences originate from a single marine environment and have been sequenced from species that are not easily obtainable by laboratory cultivation. The resource also contains very many fragments of whole protein sequences, a side effect of the shotgun sequencing method.     

Evolution of HSP70 gene and its implications regarding relationships between archaebacteria,eubacteria, and eukaryotes

The 70-kDa heat-shock protein (HSP70) constitutes the most conserved protein present in all organisms that is known to date. Based on global alignment of HSP70 sequences from organisms representing all three domains, numerous sequence signatures that are specific for prokaryotic and eukaryotic homologs have been identified. HSP70s from the two archaebacterial species examined (viz., Halobacterium marismortui and Methanosarcina mazei) have been found to contain all eubacterial but no eukaryotic signature sequences. Based on several novel features of the HSP70 family of proteins (viz., presence of tandem repeats of a 9-amino-acid [a.a.] polypeptide sequence and structural similarity between the first and second quadrants of HSP70, homology of the N-terminal half of HSP70 to the bacterial MreB protein, presence of a conserved insert of 23–27 a.a. in all HSP70s except those from archaebacteria and gram-positive eubacteria) a model for the evolution of HSP70 gene from an early stage is proposed. The HSP70 homologs from archaebacteria and gram-positive bacteria lacking the insert in the N-terminal quadrants are indicated to be the ancestral form of the protein. Detailed phylogenetic analyses of HSP70 sequence data (viz., by bootstrap analyses, maximum parsimony, and maximum likelihood methods) provide evidence that archaebacteria are not monophyletic and show a close evolutionary linkage with the gram-positive eubacteria. These results do not support the traditional archaebacterial tree, where a close relationship between archaebacterial and eukaryotic homologs is observed. To explain the phylogenies based on HSP70 and other gene sequences, a model for the origin of eukaryotic cells involving fusion between archaebacteria and gram-negative eubacteria is proposed. Correspondence to: R. S. Gupta     

Dynamic Protein-RNA recognition in primary MicroRNA processing

MicroRNAs are prevalent regulators of gene expression, controlling most of the proteome in multicellular organisms. To generate the functional small RNAs, precise processing steps are required. In animals, microRNA biogenesis is initiated by Microprocessor that minimally consists of the Drosha enzyme and its partner, DGCR8. This first step is critical for selecting primary microRNAs, and many RNA-binding proteins and regulatory pathways target both the accuracy and efficiency of microRNA maturation. Structures of Drosha and DGCR8 in complex with primary microRNAs elucidate how RNA structural features rather than sequence provide the framework for substrate recognition. Comparing multiple states of Microprocessor and the closely related Dicer homologs shed light on the dynamic protein-RNA complex assembly and disassembly required to recognize RNAs with diverse sequences via common structural features.     

Protein structural similarity search by Ramachandran codes

Background  

Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases.     

Evaluation and refinement of tmRNA structure using gene sequences from natural microbial communities

DNA harvested directly from complex natural microbial communities by PCR has been successfully used to predict RNase P RNA structure, and can potentially provide an abundant source of information for structural predictions of other RNAs. In this study, we utilized genetic variation in natural communities to test and refine the secondary and tertiary structural model for the bacterial tmRNA. The variability of proposed tmRNA secondary structures in different organisms and the lack of any predicted tertiary structure suggested that further refinement of the tmRNA could be useful. To increase the phylogenetic representation of tmRNA sequences, and thereby provide additional data for statistical comparative analysis, we amplified, sequenced, and compared tmRNA sequences from natural microbial communities. Using primers designed from gamma proteobacterial sequences, we determined 44 new tmRNA sequences from a variety of environmental DNA samples. Covariation analyses of these sequences, along with sequences from cultured organisms, confirmed most of the proposed tmRNA model but also provided evidence for a new tertiary interaction. This approach of gathering sequence information from natural microbial communities seems generally applicable in RNA structural analysis.     

FastGroupII: A web-based bioinformatics platform for analyses of large 16S rDNA libraries

Background  

High-throughput sequencing makes it possible to rapidly obtain thousands of 16S rDNA sequences from environmental samples. Bioinformatic tools for the analyses of large 16S rDNA sequence databases are needed to comprehensively describe and compare these datasets.