Bioinformatics database infrastructure for biotechnology research

Many databases are available that provide valuable data resources for the biotechnological researcher. According to their core data, they can be divided into different types. Some databases provide primary data, like all published nucleotide sequences, others deal with protein sequences. In addition to these two basic types of databases, a huge number of more specialized resources are available, like databases about protein structures, protein identification, special features of genes and/or proteins, or certain organisms. Furthermore, some resources offer integrated views on different types of data, allowing the user to do easy customized queries over large datasets and to compare different types of data.     

Recent advances in kinase signaling network profiling by mass spectrometry

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.     

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.     

Histone Sequence Database: new histone fold family members.

Searches of the major public protein databases with core and linker chicken and human histone sequences have resulted in the compilation of an annotated set of histone protein sequences. In addition, new database searches with two distinct motif search algorithms have identified several members of the histone fold family, including human DRAP1 and yeast CSE4. Database resources include information on conflicts between similar sequence entries in different source databases, multiple sequence alignments, links to the Entrez integrated information retrieval system, structures for histone and histone fold proteins, and the ability to visualize structural data through Cn3D. The database currently contains >1000 protein sequences, which are searchable by protein type, accession number, organism name, or any other free text appearing in the definition line of the entry. All sequences and alignments in this database are available through the World Wide Web at http://www.nhgri.nih. gov/DIR/GTB/HISTONES or http://www.ncbi.nlm.nih. gov/Baxevani/HISTONES     

PCAS – a precomputed proteome annotation database resource

Background  

Many model proteomes or "complete" sets of proteins of given organisms are now publicly available. Much effort has been invested in computational annotation of those "draft" proteomes. Motif or domain based algorithms play a pivotal role in functional classification of proteins. Employing most available computational algorithms, mainly motif or domain recognition algorithms, we set up to develop an online proteome annotation system with integrated proteome annotation data to complement existing resources.     

Early developmental and stress responsive ESTs from mungbean, <Emphasis Type="Italic">Vigna radiata</Emphasis> (L.) Wilczek,seedlings

Although mungbean (Vigna radiata (L.) Wilczek) is commonly used as human food; the genomic resources of this species available in databases are limited. This study aims to develop expressed sequence tag (EST) resources for mungbean genes informative to early seedling development and chilling response. Two mungbean varieties that differ in disease resistance were found to also differ in their susceptibility to chilling temperatures. A total of 1,198 ESTs were obtained from one cDNA library and four PCR-select cDNA subtraction libraries; among these 523 were clustered into 136 contigs and 675 were singletons. The 811 non-redundant uniESTs were compared to GenBank using the Basic Local Alignment Search Tool (BLAST) and WU-BLAST algorithms, of these only 489 uniESTs had significant sequence homology, which may be involved in resuming the metabolic activity of seedlings, switching on photomorphogenesis, fuelling photosynthesis and/or initiating the unique developmental programs. Their encoded proteins may associate with regulatory proteins to trigger a direct stress response or participate in acclimation to environmental stressors. The uniEST platform reported will enrich the genomic resources of mungbean for functional genomic research on seedling development and chilling response of tropical crops and provide targets for improving the chilling tolerance of the tropical crops. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.     

iPfam: visualization of protein-protein interactions in PDB at domain and amino acid resolutions

SUMMARY: There are many resources that contain information about binary interactions between proteins. However, protein interactions are defined by only a subset of residues in any protein. We have implemented a web resource that allows the investigation of protein interactions in the Protein Data Bank structures at the level of Pfam domains and amino acid residues. This detailed knowledge relies on the fact that there are a large number of multidomain proteins and protein complexes being deposited in the structure databases. The resource called iPfam is hosted within the Pfam UK website. Most resources focus on the interactions between proteins; iPfam includes these as well as interactions between domains in a single protein. AVAILABILITY: iPfam is available on the Web for browsing at http://www.sanger.ac.uk/Software/Pfam/iPfam/; the source-data for iPfam is freely available in relational tables via the ftp site ftp://ftp.sanger.ac.uk/pub/databases/Pfam/database_files/.     

A comprehensive dictionary of protein accession codes for complete protein accession identifier alias resolving

In mass spectrometry-based proteomics, protein identification results usually consist of peptide sequences and database-dependent accession identifiers of the matching proteins. Often certain annotations are only available in particular databases that in turn must be queried by a certain identifier. In order to simplify and unify the tracing of identified proteins back to their original annotation information, a system capable of set-oriented mapping the different accession identifiers of proteins derived from multiple sequence database sources has been developed. This allows unification of the access to protein information and tracing to other online resources providing additional information as well as resolving cross-references of protein identifications. The interface of seqDB is available via http://www.protein-ms.de following the link to seqDB.     

An overview of human protein databases and their application to functional proteomics in health and disease

Functional proteomics can be defined as a strategy to couple proteomic information with biochemical and physiological analyses with the aim of understanding better the functions of proteins in normal and diseased organs. In recent years, a variety of publicly available bioinformatics databases have been developed to support protein-related information management and biological knowledge discovery. In addition to being used to annotate the proteome, these resources also offer the opportunity to develop global approaches to the study of the functional role of proteins both in health and disease. Here, we present a comprehensive review of the major human protein bioinformatics databases. We conclude this review by discussing a few examples that illustrate the importance of these databases in functional proteomics research.     

A new dynamical layout algorithm for complex biochemical reaction networks

Background  

To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly.     

Exploring the proteome of Plasmodium

With the entire genomic sequence of several species of Plasmodium soon to be available, researchers are now focusing on methods to study gene and protein expression at the whole organism level. Traditional methods of characterising and identifying large numbers of proteins from a complex protein mixture have relied predominantly on two-dimensional gel electrophoresis combined with N-terminal sequencing or mass spectrometry of individually prepared proteins. New proteomics methods are now available that are based on resolving small peptides derived from complex protein mixtures by high-resolution liquid chromatography and directly identifying them by tandem mass spectrometry (LC/LC/MS/MS) and sophisticated computer search algorithms against whole genome sequence databases. These newer proteomic methods have the potential to accelerate the reproducible identification of large numbers of proteins from various life cycle stages of Plasmodium and may help to better understand parasite biology and lead to the identification of new targets of vaccines and drugs.     

SCOOP: a simple method for identification of novel protein superfamily relationships

MOTIVATION: Profile searches of sequence databases are a sensitive way to detect sequence relationships. Sophisticated profile-profile comparison algorithms that have been recently introduced increase search sensitivity even further. RESULTS: In this article, a simpler approach than profile-profile comparison is presented that has a comparable performance to state-of-the-art tools such as COMPASS, HHsearch and PRC. This approach is called SCOOP (Simple Comparison Of Outputs Program), and is shown to find known relationships between families in the Pfam database as well as detect novel distant relationships between families. Several novel discoveries are presented including the discovery that a domain of unknown function (DUF283) found in Dicer proteins is related to double-stranded RNA-binding domains. AVAILABILITY: SCOOP is freely available under a GNU GPL license from http://www.sanger.ac.uk/Users/agb/SCOOP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Effect of cleavage enzyme, search algorithm and decoy database on mass spectrometric identification of wheat gluten proteins

While tandem mass spectrometry (MS/MS) is routinely used to identify proteins from complex mixtures, certain types of proteins present unique challenges for MS/MS analyses. The major wheat gluten proteins, gliadins and glutenins, are particularly difficult to distinguish by MS/MS. Each of these groups contains many individual proteins with similar sequences that include repetitive motifs rich in proline and glutamine. These proteins have few cleavable tryptic sites, often resulting in only one or two tryptic peptides that may not provide sufficient information for identification. Additionally, there are less than 14,000 complete protein sequences from wheat in the current NCBInr release. In this paper, MS/MS methods were optimized for the identification of the wheat gluten proteins. Chymotrypsin and thermolysin as well as trypsin were used to digest the proteins and the collision energy was adjusted to improve fragmentation of chymotryptic and thermolytic peptides. Specialized databases were constructed that included protein sequences derived from contigs from several assemblies of wheat expressed sequence tags (ESTs), including contigs assembled from ESTs of the cultivar under study. Two different search algorithms were used to interrogate the database and the results were analyzed and displayed using a commercially available software package (Scaffold). We examined the effect of protein database content and size on the false discovery rate. We found that as database size increased above 30,000 sequences there was a decrease in the number of proteins identified. Also, the type of decoy database influenced the number of proteins identified. Using three enzymes, two search algorithms and a specialized database allowed us to greatly increase the number of detected peptides and distinguish proteins within each gluten protein group.     

Key challenges for the creation and maintenance of specialist protein resources

As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus increasing the importance of good access to these data that vary from the supplemental material of individual articles, all the way to major reference databases with professional staff and long‐term funding. Specialist protein resources fill an important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins, using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our working together could enhance their accuracy, currency, and overall value. Proteins 2015; 83:1005–1013. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

ProteinHistorian: tools for the comparative analysis of eukaryote protein origin

The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed ProteinHistorian to make tools for performing analyses of protein origins widely available. Given a list of proteins of interest, ProteinHistorian estimates the phylogenetic age of each protein, quantifies enrichment for proteins of specific ages, and compares variation in protein age with other protein attributes. ProteinHistorian allows flexibility in the definition of protein age by including several algorithms for estimating ages from different databases of evolutionary relationships. We illustrate the use of ProteinHistorian with three example analyses. First, we demonstrate that proteins with high expression in human, compared to chimpanzee and rhesus macaque, are significantly younger than those with human-specific low expression. Next, we show that human proteins with annotated regulatory functions are significantly younger than proteins with catalytic functions. Finally, we compare protein length and age in many eukaryotic species and, as expected from previous studies, find a positive, though often weak, correlation between protein age and length. ProteinHistorian is available through a web server with an intuitive interface and as a set of command line tools; this allows biologists and bioinformaticians alike to integrate these approaches into their analysis pipelines. ProteinHistorian's modular, extensible design facilitates the integration of new datasets and algorithms. The ProteinHistorian web server, source code, and pre-computed ages for 32 eukaryotic genomes are freely available under the GNU public license at http://lighthouse.ucsf.edu/ProteinHistorian/.