Applications of semantic similarity measures

There has been much interest in uncovering protein-protein interactions and their underlying domain-domain interactions. Many experimental techniques have been developed, for example yeast-two-hybrid screening and tandem affinity purification. Since it is time consuming and expensive to perform exhaustive experimental screens, in silico methods are used for predicting interactions. However, all experimental and computational methods have considerable false positive and false negative rates. Therefore, it is necessary to validate experimentally determined and predicted interactions. One possibility for the validation of interactions is the comparison of the functions of the proteins or domains. Gene Ontology (GO) is widely accepted as a standard vocabulary for functional terms, and is used for annotating proteins and protein families with biological processes and their molecular functions. This annotation can be used for a functional comparison of interacting proteins or domains using semantic similarity measures. Another application of semantic similarity measures is the prioritization of disease genes. It is know that functionally similar proteins are often involved in the same or similar diseases. Therefore, functional similarity is used for predicting disease associations of proteins. In the first part of my talk, I will introduce some semantic and functional similarity measures that can be used for comparison of GO terms and proteins or protein families. Then, I will show their application for determining a confidence threshold for domain-domain interaction predictions. Additionally, I will present FunSimMat (http://www.funsimmat.de/), a comprehensive resource of functional similarity values available on the web. In the last part, I will introduce the problem of comparing diseases, and a first attempt to apply functional similarity measures based on GO to this problem

Applying systems biology methods to identify putative drug targets in the metabolism of the malaria pathogen Plasmodium falciparum

Trotz weltweiter Bemühungen, die Tropenkrankheit Malaria zurückzudrängen, erkranken jährlich bis zu einer halben Milliarde Menschen an Malaria mit der Folge von über einer Million Todesopfern. Da zur Zeit eine wirksame Impfung nicht in Sicht ist und sich Resistenzen gegen gängige Medikamente ausbreiten, werden dringend neue Antimalariamittel benötigt. Um die Suche nach neuen Angriffsorten für Medikamente zu unterstützen, untersucht die vorliegende Arbeit mit einem rechnergestützten Ansatz den Stoffwechsel von Plasmodium falciparum, dem tödlichsten Malaria-Erreger. Basierend auf einem aus dem aktuellen Forschungsstand rekonstruierten metabolischen Netzwerk des Parasiten werden metabolische Flüsse für die einzelnen Stadien des Lebenszyklus von P. falciparum berechnet. Dabei wird ein im Rahmen dieser Arbeit entwickelter Fluss-Bilanz-Analyse-Ansatz verwendet, der ausgehend von in den jeweiligen Entwicklungsstadien gemessenen Genexpressionsprofilen entsprechende Flussverteilungen ableitet. Für das so ermittelte stadienspezifische Flussgeschehen ergibt sich eine gute Übereinstimmung mit bekannten Austauschprozessen von Stoffen zwischen Parasit und infiziertem Erythrozyt. Knockout Simulationen, die mit Hilfe einer ähnlichen Vorhersagemethode durchgeführte werden, decken essentielle metabolische Reaktionen im Netzwerk auf. Fast 90% eines Sets von experimentell bestimmten essentiellen Enzymen wird wiedergefunden, wenn die Annahme getroffen wird, dass Nährstoffe nur begrenzt aus der Wirtszelle aufgenommen werden können. Die als essentiell vorhergesagten Enzyme stellen mögliche Angriffsorte für Medikamente dar. Anhand der Flussverteilungen, die für die einzelnen Entwicklungsstadien berechnet wurden, können diese potenziellen Targets nach Relevanz für Malaria Prophylaxe und Therapie sortiert werden, je nachdem, in welchem Stadium die Enzyme als aktiv vorhergesagt wurden. Dies bietet einen vielversprechenden Startpunkt für die Entwicklung von neuen Antimalariamitteln.Despite enormous efforts to combat malaria, the disease still afflicts up to half a billion people each year, of which more than one million die. Currently no effective vaccine is within sight, and resistances to antimalarial drugs are wide-spread. Thus, new medicines against malaria are urgently needed. In order to aid the process of drug target detection, the present work carries out a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. A comprehensive compartmentalized metabolic network is assembled, which is able to reproduce metabolic processes known from the literature to occur in the parasite. On the basis of this network metabolic fluxes are predicted for the individual life cycle stages of P. falciparum. In this context, a flux balance approach is developed to obtain metabolic flux distributions that are consistent with gene expression profiles observed during the respective stages. The predictions are found to be in good accordance with experimentally determined metabolite exchanges between parasite and infected erythrocyte. Knockout simulations, which are conducted with a similar approach, reveal indispensable metabolic reactions within the parasite. These putative drug targets cover almost 90% of a set of experimentally confirmed essential enzymes if the assumption is made that nutrient uptake from the host cell is limited. A comparison demonstrates that the applied flux balance approach yields target predictions with higher specificity than the topology based choke-point analysis. The previously predicted stage-specific flux distributions allow to filter the obtained set of drug target candidates with respect to malaria prophylaxis, therapy or both, providing a promising starting point for further drug development

Decomposing protein networks into domain-domain interactions

Summary: The application of novel experimental techniques has generated large networks of protein--protein interactions. Frequently, important information on the structure and cellular function of protein--protein interactions can be gained from the domains of interacting proteins. We have designed a Cytoscape plugin that decomposes interacting proteins into their respective domains and computes a putative network of corresponding domain--domain interactions. To this end, the network graph of proteins has been extended by additional node and edge types for domain interactions, including different node and edge shapes and coloring schemes used for visualization. An additional plugin provides supplementary web links to Internet resources on domain function and structure. Availability: Both Cytoscape plugins can be downloaded from http://www.cytoscape.org Contact: [email protected]

Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia

Recurrent chromosomal translocations involving the mixed lineage leukaemia (MLL) gene initiate aggressive forms of leukaemia, which are often refractory to conventional therapies1. Many MLL-fusion partners are members of the super elongation complex (SEC), a critical regulator of transcriptional elongation, suggesting that aberrant control of this process has an important role in leukaemia induction2, 3. Here we use a global proteomic strategy to demonstrate that MLL fusions, as part of SEC2, 3 and the polymerase-associated factor complex (PAFc)4, 5, are associated with the BET family of acetyl-lysine recognizing, chromatin ‘adaptor’ proteins. These data provided the basis for therapeutic intervention in MLL-fusion leukaemia, via the displacement of the BET family of proteins from chromatin. We show that a novel small molecule inhibitor of the BET family, GSK1210151A (I-BET151), has profound efficacy against human and murine MLL-fusion leukaemic cell lines, through the induction of early cell cycle arrest and apoptosis. I-BET151 treatment in two human leukaemia cell lines with different MLL fusions alters the expression of a common set of genes whose function may account for these phenotypic changes. The mode of action of I-BET151 is, at least in part, due to the inhibition of transcription at key genes (BCL2, C-MYC and CDK6) through the displacement of BRD3/4, PAFc and SEC components from chromatin. In vivo studies indicate that I-BET151 has significant therapeutic value, providing survival benefit in two distinct mouse models of murine MLL–AF9 and human MLL–AF4 leukaemia. Finally, the efficacy of I-BET151 against human leukaemia stem cells is demonstrated, providing further evidence of its potent therapeutic potential. These findings establish the displacement of BET proteins from chromatin as a promising epigenetic therapy for these aggressive leukaemias