Chemical probing suggests redox-regulation of the carbonic anhydrase activity of mycobacterial Rv1284

The mycobacterial enzyme Rv1284 is a member of the ߭carbonic anhydrase family that is considered essential for survival of the pathogen. The active site cavity of this dimeric protein is characterized by an exceptionally small volume and harbours a catalytic zinc ion coordinated by two cysteine and one histidine residue side chains. Using the natural products polycarpine and emodin as chemical probes in crystallographic experiments and stopped-flow enzyme assays, we report that the catalytic activity can be reversibly inhibited by oxidation. Oxidative conditions lead to the removal of one of the active site cysteine residues from the coordination sphere of the catalytic metal ion by engagement in a disulfide bond with another cysteine residue close by. The subsequent loss of the metal ion, which is supported by crystallographic analysis, may thus render the protein catalytically inactive. The oxidative inhibition of Rv1284 can be reversed by exposing the protein to reducing conditions. Because the physical size of the chemical probes used in the present study substantially exceeds the active site volume, we hypothesized that these compounds exert their effects from a surface-bound location and identified Tyr120 as a critical residue for oxidative inactivation. These findings link conditions of oxidative stress to pH homeostasis of the pathogen. Because oxidative stress and acidification are defence mechanisms employed by the innate immune system of the host, we suggest that Rv1284 may be a component of the mycobacterial survival strategy.Griffith Sciences, Griffith Institute for Drug DiscoveryFull Tex

Concept and application of a computational vaccinology workflow

BACKGROUND : The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders. RESULTS : We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage. CONCLUSION : Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges

The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications

BACKGROUND: The large amount of data that are currently produced in the biological sciences can no longer be explored and visualized efficiently with traditional, specialized software. Instead, new capabilities are needed that offer flexibility, rapid application development and deployment as standalone applications or available through the Web. RESULTS: We describe a new software toolkit – the Molecular Biology Toolkit (MBT; ) – that enables fast development of applications for protein analysis and visualization. The toolkit is written in Java, thus offering platform-independence and Internet delivery capabilities. Several applications of the toolkit are introduced to illustrate the functionality that can be achieved. CONCLUSIONS: The MBT provides a well-organized assortment of core classes that provide a uniform data model for the description of biological structures and automate most common tasks associated with the development of applications in the molecular sciences (data loading, derivation of typical structural information, visualization of sequence and standard structural entities)

ePlant and the 3D Data Display Initiative: Integrative Systems Biology on the World Wide Web

Visualization tools for biological data are often limited in their ability to interactively integrate data at multiple scales. These computational tools are also typically limited by two-dimensional displays and programmatic implementations that require separate configurations for each of the user's computing devices and recompilation for functional expansion. Towards overcoming these limitations we have developed “ePlant” (http://bar.utoronto.ca/eplant) – a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. These tools display data spanning multiple biological scales on interactive three-dimensional models. Currently, ePlant consists of the following modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. The ePlant's protein structure explorer module represents experimentally determined and theoretical structures covering >70% of the Arabidopsis proteome. The ePlant framework is accessed entirely through a web browser, and is therefore platform-independent. It can be applied to any model organism. To facilitate the development of three-dimensional displays of biological data on the world wide web we have established the “3D Data Display Initiative” (http://3ddi.org)

Alternative Splicing and Protein Structure Evolution

In den letzten Jahren gab es in verschiedensten Bereichen der Biologie einen dramatischen Anstieg verfügbarer, experimenteller Daten. Diese erlauben zum ersten Mal eine detailierte Analyse der Funktionsweisen von zellulären Komponenten wie Genen und Proteinen, die Analyse ihrer Verknüpfung in zellulären Netzwerken sowie der Geschichte ihrer Evolution. Insbesondere der Bioinformatik kommt hier eine wichtige Rolle in der Datenaufbereitung und ihrer biologischen Interpretation zu. In der vorliegenden Doktorarbeit werden zwei wichtige Bereiche der aktuellen bioinformatischen Forschung untersucht, nämlich die Analyse von Proteinstrukturevolution und Ähnlichkeiten zwischen Proteinstrukturen, sowie die Analyse von alternativem Splicing, einem integralen Prozess in eukaryotischen Zellen, der zur funktionellen Diversität beiträgt. Insbesondere führen wir mit dieser Arbeit die Idee einer kombinierten Analyse der beiden Mechanismen (Strukturevolution und Splicing) ein. Wir zeigen, dass sich durch eine kombinierte Betrachtung neue Einsichten gewinnen lassen, wie Strukturevolution und alternatives Splicing sowie eine Kopplung beider Mechanismen zu funktioneller und struktureller Komplexität in höheren Organismen beitragen. Die in der Arbeit vorgestellten Methoden, Hypothesen und Ergebnisse können dabei einen Beitrag zu unserem Verständnis der Funktionsweise von Strukturevolution und alternativem Splicing bei der Entstehung komplexer Organismen leisten wodurch beide, traditionell getrennte Bereiche der Bioinformatik in Zukunft voneinander profitieren können

PROMPT: a protein mapping and comparison tool

BACKGROUND: Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available. RESULTS: PROMPT is a comprehensive bioinformatics software environment which enables the user to compare arbitrary protein sequence sets, revealing statistically significant differences in their annotation features. It allows automatic retrieval and integration of data from a multitude of molecular biological databases as well as from a custom XML format. Similarity-based mapping of sequence IDs makes it possible to link experimental information obtained from different sources despite discrepancies in gene identifiers and minor sequence variation. PROMPT provides a full set of statistical procedures to address the following four use cases: i) comparison of the frequencies of categorical annotations between two sets, ii) enrichment of nominal features in one set with respect to another one, iii) comparison of numeric distributions, and iv) correlation of numeric variables. Analysis results can be visualized in the form of plots and spreadsheets and exported in various formats, including Microsoft Excel. CONCLUSION: PROMPT is a versatile, platform-independent, easily expandable, stand-alone application designed to be a practical workhorse in analysing and mining protein sequences and associated annotation. The availability of the Java Application Programming Interface and scripting capabilities on one hand, and the intuitive Graphical User Interface with context-sensitive help system on the other, make it equally accessible to professional bioinformaticians and biologically-oriented users. PROMPT is freely available for academic users from

A Gibbs sampling strategy for mining of protein-protein interaction networks and protein structures

Complex networks are general and can be used to model phenomena that belongs to different fields of research, from biochemical applications to social networks. However, due to the intrinsic complexity of real networks, their analysis can be computationally demanding. Recently, several statistic and probabilistic analysis approaches have been designed, resulting to be much faster, flexible and effective than deterministic algorithms. Among statistical methods, Gibbs sampling is one of the simplest and most powerful algorithms for solving complex optimization problems and it has been applied in different contexts. It has shown its effectiveness in computational biology but in sequence analysis rather than in network analysis. One approach to analyze complex networks is to compare them, in order to identify similar patterns of interconnections and predict the function or the role of some unknown nodes. Thus, this motivated the main goal of the thesis: designing and implementing novel graph mining techniques based on Gibbs sampling to compare two or more complex networks. The methodology is domain-independent and can work on any complex system of interacting entities with associated attributes. However, in this thesis we focus our attention on protein analysis overcoming the strong current limitations in this area. Proteins can be analyzed from two different points of view: (i) an internal perspective, i.e. the 3D structure of the protein, (ii) an external perspective, i.e. the interactions with other macromolecules. In both cases, a comparative analysis with other proteins of the same or distinct species can reveal important clues for the function of the protein and evolutionary convergences or divergences between different organisms in the way a specific function or process is carried out. First, we present two methods based on Gibbs sampling for the comparative analysis of protein-protein interaction networks: GASOLINE and SPECTRA. GASOLINE is a stochastic and greedy algorithm to find similar groups of interacting proteins in two or more networks. It can align many networks and more quickly than the state-of-the-art methods. SPECTRA is a framework to retrieve and compare networks of proteins that interact with one another in specific healthy or tumor tissues. The aim in this case is to identify changes in protein concentration or protein "behaviour" across different tissues. SPECTRA is an adaptation of GASOLINE for weighted protein-protein interaction networks with gene expressions as node weights. It is the first algorithm proposed for multiple comparison of tissue-specific interaction networks. We also describe a Gibbs sampling based algorithm for 3D protein structure comparison, called PROPOSAL, which finds local structural similarities across two or more protein structures. Experimental results confirm our computational predictions and show that the proposed algorithms are much faster and in most cases more accurate than existing methods

Development of a read mapping analysis software and computational pan genome analysis of 20 Pseudomonas aeruginosa strains

Hilker R. Development of a read mapping analysis software and computational pan genome analysis of 20 Pseudomonas aeruginosa strains. Bielefeld: Bielefeld University; 2015.In times of multi-resistant pathogenic bacteria, their detailed study is of utmost importance. Their comparative analysis can even aid the emerging field of personalized medicine by enabling optimized treatment depending on the presence of virulence factors and antibiotic resistances in the infection concerned. The weaknesses and functionality of these pathogenic bacteria can be investigated using modern computer science and novel sequencing technologies. One of these methods is the bioinformatics evaluation of high-throughput sequencing data. A pathogenic bacterium posing severe health care issues is the ubiquitous Pseudomonas aeruginosa. It is involved in a wide range of infections mainly affecting the pulmonary or urinary tract, open wounds and burns. The prevalence of chronic obstructive pulmonary disease cases with P. aeruginosa in Germany alone is ~600,000 per year. Within the framework of this dissertation, computational comparative genomics experiments were conducted with a panel of 20 of the most abundant Pseudomonas aeruginosa strains. 15 of these strains were isolated from clinical cases, while the remaining 5 were strains without a known infection history isolated from the environment. This division was chosen to enable direct comparison of the pathogenic potential of clinical and environmental strains and identification of their possible characteristic differences. When designing the bioinformatics experiments and searching for an efficient visualization and automatic analysis platform for read alignment (mapping) data, it became evident that no adequate solution was available that included all required functionalities. On these grounds, the decision was made to define two main subjects for this dissertation. Besides the P. aeruginosa pan genome analysis, a novel read mapping visualization and analysis software was developed and published in the journal Bioinformatics. This software - ReadXplorer - is partly based upon a prototype, which was developed during a preceding master's thesis at the Center for Biotechnology of the Bielefeld University under the name VAMP. The software was developed into a comprehensive user-friendly platform augmented with several newly developed and implemented automatic bioinformatics read mapping analyses. Two examples of these are the transcription start site detection and the single nucleotide polymorphism detection. Moreover, new intuitive visualizations were added to the existent ones and existing visualizations were greatly enhanced. ReadXplorer is designed to support not only DNA-seq data as accrued in the P. aeruginosa experiments, but also any kind of standard read mapping data as obtained from RNA-seq or ChIP-seq experiments. The data management was designed to comply with the latest performance and efficiency needs emerging from the large next generation sequencing data sets. Finally, ReadXplorer was empowered to deal with eukaryotic read mapping data as well. Amongst other software, ReadXplorer was then used to analyze different comparative genomics aspects of P. aeruginosa and to draw conclusions regarding the development of their pathogenicity. The list of conducted experiments includes phylogeny and gene set determination, analysis of regions of genomic plasticity and identification of single nucleotide polymorphisms. The achieved results were published in the journal Environmental Biology