CUBE - the division of Computational Systems Biology - is part of the Department of Microbiology and Ecosystem Science at the Faculty of Life Sciences of the University of Vienna.
The mission of CUBE is to advance our understanding of biological systems, ranging from single species to multi-species systems and ecosystems, based on data from large-scale bioanalytical methods. We develop, improve and apply computational methods for the interpretation of molecular information in biology, and establishes and analyses quantitative mathematical models. CUBE is highly engaged in basic research, in the translation of basic knowledge to medical and biological applications, and in the training of students on all educational levels. We support an innovative and open-minded, creative and collaborative atmosphere inside and outside our team, facilitate the exchange with the society and contribute to a vital and responsible research community in computational biology.
Interactions between bacteria and eukaryotes are widespread in all ecosystems on earth and often lead to symbiotic relationships. The most prominent themes in current research are different types of human-microbe interactions, such as the interplay of human microbiomes with their host or human infections by bacterial pathogens. Understanding of bacterial interactions with other hosts, such as livestock animals and crop plants, are becoming crucial for sustaining nutrition and gaining renewable energy. Protein secretion systems play a key role in the interaction of bacteria and hosts. So far, sequence similarity searches and models of signal peptides were the main tools for the computational prediction of secreted proteins and secretion systems.
In my talk I will introduce recent improvements towards better annotation of bacterial secreted proteins and Type III, IV, VI secretion systems. We have bundled various tools to recognize Type III secretion signals, conserved binding sites of Type III chaperones, eukaryotic-like domains and subcellular targeting signals in the host. We could demonstrate that the combination of these approaches allows a more precise modeling of bacterial secretomes. The rapidly increasing number of available microbial genomes allowed us to develop a novel tool that predicts not only core genes of bacterial secretion systems in bacterial genomes but also their completeness and potential functionality. The approach is based on machine-learning techniques and can be easily applied to thousands of genomes. This method not only predicts secretion systems in newly sequenced genomes and metagenomes, but also suggests that previously unknown proteins are important for the function of protein secretion systems.