is a non-for-profit research and development (R&D) organization. Our mission is to create, inspire and incubate innovative ideas in life-sciences. We also promote and facilitate unrestricted and effective research of…
is a non-for-profit research and development (R&D) organization. Our mission is to create, inspire and incubate innovative ideas in life-sciences. We also promote and facilitate unrestricted and effective research of…
Structural genomics is a wide term describing the determination of a structure representation based on information contained in the genome, and at present is almost exclusively limited to the proteins. Although in common understanding genetic information means “genes and their encoded protein products”, thousands of human genes produce transcripts which are biologically important but they do not produce proteins. Furthermore, even though the sequence of the human DNA is known by now, the meaning of the most of the sequences still remains unknown. It is very likely that a large amount of genes has been highly underestimated, mainly because the actual gene finders work well only for large, highly expressed, evolutionary conserved protein-coding genes. Most of those genome elements encode RNA from which transfer and ribosomal RNAs are the classical examples. But beside these well-known molecules there is a vast unknown world of tiny RNAs that might play a crucial role in a number of cellular processes. Those elements are named Noncoding RNAs (ncRNA) and they perform their function without transcription to the protein product. Here, we propose the development of the integrated bioinformatics platform that is specifically addressed for detecting, verifying, and classifying noncoding RNAs. This complex approach to “computational RNomics” will provide a pipeline which will be capable of detecting RNA motifs with low sequence conservation. It will also integrate the RNA motif prediction which should significantly improve the quality of the RNA homologues searching.
This project concerns the design of cryptographic schemes that are secure even if implemented on not-secure devices. The motivation for this work comes from an observation that most of the real-life attacks on cryptographic devices do not break their mathematical foundations, but exploit vulnerabilities of their implementations. This concerns both the cryptographic software executed on PCs (that can be attacked by viruses), and the implementations on hardware (that can be subject to the side-channel attacks). Typically, fixing this problem was left to the practitioners, since it was a common belief that theory cannot be of any help here. However, new exciting results in cryptography suggest that this view was too pessimistic: there exist methods to design cryptographic protocols in such a way that they are secure even if the hardware on which they are executed cannot be fully trusted. The goal of this project is to investigate these methods further, unify them in a solid mathematical theory (many of them were developed independently), and propose new ideas in this area. The project will be mostly theoretical (although some practical experiments may be performed). Our main interest lies within the theory of private circuits, bounded-retrieval model, physically-observable cryptography, and human-assisted cryptography. We view these theories just as the point of departure, since the field is largely unexplored and we expect to witness completely new ideas soon.
Our particle-based method allows us to synthesise high complexity peptide arrays by combinatorial synthesis and for an unrivalled prize. We plan to further develop this new technology up to the level of robust prototype machines, and mate it to bioinformatics and readout tools. Together, our procedure(s) should boost the field of proteomics in a similar way as the lithographic technologies did with the field of genomics. Central to our novel method are the activated chemical building blocks that are “frozen” within solid amino acid particles. Thereby, we can use a colour laser printer to send them to defined addresses on a 2D support, where the particles are simply melted to induce a spatially defined coupling reaction of now freed amino acid derivatives. By repeated printing and melting cycles this simple trick yields high complexity peptide arrays. Based on existing pre-prototypes, we will develop a user-friendly peptide laser printer that spatially defined addresses our 20 different amino acid toners in high resolution to a support (WP1), and a scanner that especially fast and sensitive reads out the large formats delivered by the peptide laser printer (WP2). The increased production of amino acid toners and array supports are other bottlenecks in the output of peptide arrays that are tackled in WP3. This should allow us to increase the output of individual peptide spots from currently 0,5 Million to >10 Million peptides per month. Finally, to foster a market for high complexity peptide arrays, we will work out paradigmatic application examples in WP4. These aim to directly screen for antibiotic or apoptosis inducing D-peptides, and for the comprehensive readout of the different antibodies that patrol the serum of autoimmune patients. Based on user-friendly prototype machines, on first paradigmatic application examples for high complexity peptide arrays, and shielded by a strong patent, the participating SMEs will commercialise this new technology.
Enzymes are extremely powerful natural catalysts able to perform almost any type of chemical reaction while being mild by nature and highly specific. In fact, the delicate functioning of enzymes forms the basis of every living creature. The catalytic potential of enzymes is more and more appreciated by the industry as many industrial processes rely on these sophisticated catalysts. However, the number of reactions catalyzed by enzymes is restricted as enzymes only have evolved to catalyze reactions that are physiologically relevant. Furthermore, enzymes have adapted to the direct (cellular) environment in which they have to function (e.g. operative at ambient temperature, resilient towards proteolysis, catalytic turnover rate should fit with metabolic enzyme partners). This excludes the existence of enzymes that do not fit within boundaries set by nature. It is a great challenge to go beyond these natural boundaries and develop methodologies to design ‘unnatural’ tailor-made enzymes. Ideally it should become possible to (re)design enzymes to convert pre-defined substrates. Such designer enzymes could theoretically exhibit unsurpassed catalytic properties and, obviously, will be of significant interest for industrial biotechnology. The OXYGREEN project aims at the design and construction of novel oxygenating enzymes (designer oxygenases) for the production of compounds that can be used in medicine, food and agriculture and the development of novel powerful and generic enzyme redesign tools for this purpose. The enzymes and whole-cell biocatalysts that will be developed should catalyze the specific incorporation of oxygen to afford synthesis of bioactive compounds in a selective and clean way, with minimal side products and with no use of toxic materials. For this, generic platform technologies (novel high-throughput methodology and methods for engineering dedicated host cells) will be developed that allow effective structure-inspired directed evolution of enzyme.
The project will address these issues in marine diatoms using information based on two completed diatom genome sequences. Important topics that will be addressed include carbon sequestration, nutrient acquisition, the rise and fall of algal blooms, and biofouling. We will study gene expression profiles at the whole genome level in response to ecologically-relevant stimuli, will manipulate expression of candidate key genes by reverse genetics, and will study phylogenetic histories and ecological significance of these genes in a range of diatoms.
Combatting and eventually eradicating the new coronavirus causing Severe Acute Respiratory Syndrome (SARS) requires specific and efficient antiviral drugs and improved diagnostics. The Sino-European Project on SARS Diagnostics and Antivirals (SEPSDA) is an integrated project that applies modern biotechnical technology for the generation of improved diagnostics and of lead compounds for antiviral drugs. SEPSDA brings together leading SARS researchers from Germany, Denmark, Poland, and China, who together have an excellent publication record on the molecular biology of SARS coronavirus (SARS-CoV). Several of the existing anti-SARS drug leads as well as the first antibody-based diagnostic kit were created by members of SEPSDA. Participation of four leading laboratories in China brings SEPSDA in the unique position of having access to samples from Chinese patients at various stages of disease. Serological studies will lead to improved SARS diagnostics.
Analysis of the genome and the proteome of SARS coronavirus by sequencing and advanced bioinformatics will further determine the genetic variability of the virus isolates and identify new possible targets for therapeutic intervention, both at the RNA and the protein level. SEPSDA aims at determining the three-dimensional structures of all soluble SARS-CoV proteins or domains thereof. This structural genomics approach will provide the basis for the virtual screening of large compound databases, including those containing Chinese traditional medicines, for molecules potentially interfering with the function of the viral proteins or their interaction partners in the host cell. Candidate inhibitors will be tested in cell culture and improved by synthetic chemistry. After patenting, the lead compounds will be offered to an industrial platform on SARS, yet to be created, which should form an interface between SEPSDA and the pharmaceutical industry.
Genome scale analysis of the immune response against pathogenic micro-organisms; identification of diagnostic markers, vaccine candidates and development of an integrated micro array platform for clinical investigations.
The genome sequences of microbial organisms responsible for diseases of world-wide medical importance have been sequenced or will be available in the near future. Technologies for producing large numbers of proteins have been developed and high-throughput assays such as protein micro arrays have been clinically validated for detecting the presence of antibodies, in serum, directed against microbial antigens. These achievements offer the opportunity of investigating the natural immune response against the whole proteome of a variety of micro-organisms. Powerful combinations of genomic information, molecular tools and immunological assays are becoming available to help identify the antigens that function as targets of protective immunity or could be used as markers for serodiagnosis. We propose here to identify in micro-organisms of great medical relevance (M. pneumoniae, C. pneumoniae, L. pneumophila, coronavirus spp and P. falciparum), a large collection of surface and secreted proteins as well as putative endotoxins. This protein repertoire will be produced as recombinant molecules or as sets of overlapping synthetic peptides and printed on array slides. The serum reactivity of groups of individuals with proven history of exposure to the selected micro-organisms will be analysed against the arrayed proteins to identify diagnostic markers and correlates of protection.
This project will significantly expand the SMEs bank of Intellectual Property and contribute to expertise within the RTDs. It is anticipated that the proposed work in high throughput protein expression, software analysis, surface peptides synthesis, protein and peptide surface capture, and array reader instrumentation will create an integrated platform of great commercial and research value. Finally it will contribute to unravelling how the humoral immune response interacts with the microbial proteomes thus filling the gap between genomic data and development of novel vaccines and diagnostic tools.
Deciphering the information on genome sequences in terms of the biological function of the genes and proteins is a major challenge of the post-genomic era. Currently, the bulk of function assignments for newly sequenced genomes is performed using bioinformatics tools that infer the function of a gene on the basis of sequence similarity with other genes of known function. It is now well recognised that these primary, sequence similarity-based function annotation procedures are frequently inaccurate and error prone. Continuing to use them without clearly defining the limits of their applicability would lead to an unmanageable propagation of errors that could jeopardise progress in Biology. On the other hand, various novel bodies of data and resources are becoming available. These provide information on context-based aspects of the biological function of genes, namely on physical and functional interactions between genes and proteins, and on whole networks and processes. In parallel structural genomics efforts world wide are providing a much better coverage of the structural motifs adopted by proteins and on their interactions. The availability of these additional and novel data offers an unprecedented opportunity for the development of methods for incorporating higher-level functional features into the annotation pipeline.
The GeneFun project aims at addressing these two important issues. The issue of annotation errors will de addressed by developing criteria for evaluating the reliability of the annotations currently available in databases. These criteria will be used to assign reliability scores to these annotations and will be incorporated into standard annotation pipelines, for future use. The issue of incorporating higher-level features into functional annotations will be addressed by combining sequence and structure information in order to identify non-linear functional features (e.g. interaction sites), and by integrating available and newly developed methods for inferring function from higher-level and context-based information (protein domain architecture, protein-protein interaction, genomic context such as gene order etc.).
To achieve these aims several European groups with strong track record in developing novel methods and analyses in comparative genomics, structural- and systems- oriented bioinformatics, and in information technology, have teamed up with an experimental group from Canada, which is well known for its outstanding achievements in the field of structural and functional proteomics. The expected output of the GeneFun project is: improved procedures for inferring function on the basis of sequence similarity, a set of procedures for predicting non-linear functional features from sequence and 3D structure in a more automated way, and benchmarked procedures for predicting context-based functional features. Major efforts will be devoted to devising protocols that optimally combine the results from several methods. In particular Web-based servers to the individual and combined procedures will be developed, and made available to the scientific community. The community will be introduced to these new tools through open workshops and training sessions.