DIMA 2.0—predicted and known domain interactions

DIMA—the domain interaction map has evolved from a simple web server for domain phylogenetic profiling into an integrative prediction resource combining both experimental data on domain–domain interactions and predictions from two different algorithms. With this update, DIMA obtains greatly improved coverage at the level of genomes and domains as well as with respect to available prediction approaches. The domain phylogenetic profiling method now uses SIMAP as its backend for exhaustive domain hit coverage: 7038 Pfam domains were profiled over 460 completely sequenced genomes.Domain pair exclusion predictions were produced from 83 969 distinct protein–protein interactions obtained from IntAct resulting in 21 513 domain pairs with significant domain pair exclusion algorithm scores. Additional predictions applying the same algorithm to predicted protein interactions from STRING yielded 2378 high-confidence pairs. Experimental data comes from iPfam (3074) and 3did (3034 pairs), two databases identifying domain contacts in solved protein structures. Taken together, these two resources yielded 3653 distinct interacting domain pairs. DIMA is available at http://mips.gsf.de/genre/proj/dima

DIMA 3.0: Domain Interaction Map

Domain Interaction MAp (DIMA, available at http://webclu.bio.wzw.tum.de/dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46 900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software

DASMIweb: online integration, analysis and assessment of distributed protein interaction data

In recent years, we have witnessed a substantial increase of the amount of available protein interaction data. However, most data are currently not readily accessible to the biologist at a single site, but scattered over multiple online repositories. Therefore, we have developed the DASMIweb server that affords the integration, analysis and qualitative assessment of distributed sources of interaction data in a dynamic fashion. Since DASMIweb allows for querying many different resources of protein and domain interactions simultaneously, it serves as an important starting point for interactome studies and assists the user in finding publicly accessible interaction data with minimal effort. The pool of queried resources is fully configurable and supports the inclusion of own interaction data or confidence scores. In particular, DASMIweb integrates confidence measures like functional similarity scores to assess individual interactions. The retrieved results can be exported in different file formats like MITAB or SIF. DASMIweb is freely available at http://www.dasmiweb.de

DASMI: exchanging, annotating and assessing molecular interaction data

Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet

An integrative approach to studying protein-protein interactions within the genome of Mycobacterium tuberculosis

Includes bibliographical references (leaves 96-103).Proteins mediate bio-chemical processes in cellular organisms by functioning as structural components, signaling molecules, enzymes, transcription factors and receptors, through complex interactions that they make with one another. The study of protein-protein interactions (PPI) within an organism is thus a vital step towards understanding the cellular life process of an organism. PPIs have been studied by specially designed experiments to identify pairs of proteins that are suspected to interact. Experimental approaches are quite expensive, yet still there are cases of wrongly identified protein-protein interactions. In this study, we predict PPIs computationally by exploiting known properties of proteins and PPIs and the requirement that proteins be present in the same cell compartment at the same time in order to interact (subcellular localization and gene expression)

Discovering Domain-Domain Interactions toward Genome-Wide Protein Interaction and Function Predictions

To fully understand the underlying mechanisms of living cells, it is essential to delineate the intricate interactions between the cell proteins at a genome scale. Insights into the protein functions will enrich our understanding in human diseases and contribute to future drug developments. My dissertation focuses on the development and optimization of machine learning algorithms to study protein-protein interactions and protein function annotations through discovery of domain-domain interactions. First of all, I developed a novel domain-based random decision forest framework (RDFF) that explored all possible domain module pairs in mediating protein interactions. RDFF achieved higher sensitivity (79.78%) and specificity (64.38%) in interaction predictions of S. cerevisiae proteins compared to the popular Maximum Likelihood Estimation (MLE) approach. RDFF can also infer interactions for both single-domain pairs and domain module pairs. Secondly, I proposed cross-species interacting domain patterns (CSIDOP) approach that not only increased fidelity of existing functional annotations, but also proposed novel annotations for unknown proteins. CSIDOP accurately determined functions for 95.42% of proteins in H. sapiens using 2,972 GO `molecular function' terms. In contrast, most existing methods can only achieve accuracies of 50% to 75% using much smaller number of categories. Additionally, we were able to assign novel annotations to 181 unknown H. sapiens proteins. Finally, I implemented a web-based system, called PINFUN, which enables users to make online protein-protein interaction and protein function predictions based on a large-scale collection of known and putative domain interactions

Targeting the bacterial cell envelope by molecular coevolution and high throughput phenotyping

Microbial species exhibit a wide repertoire of phenotypic responses to their surroundings, be it stresses posed by their environment, or signals from their bacterial community. Despite advances in computer vision, reporting such phenotypic responses is often done in a qualitative manner. In the course of my work I developed a user-friendly software tool to address the lack of a standardized, quantitative method to measure microbial phenotypes macroscopically. This freely available software, called Iris, can quantify a wide range of microbial phenotypes at the colony level and in a high-throughput fashion. Iris is already used by several research groups, and I present some of its diverse applications and potential for hypothesis generation. One such application is the quantification of the impact of each gene on the cell envelope permeability in E. coli. The Gram-negative bacterial cell envelope forms a barrier against antimicrobial drugs, drastically limiting the list of treatments effective against these organisms. To expand our knowledge on how this multi-layered is built and perturbed, we developed a rapid screening method to detect mutants with envelope defects. By screening a systematic gene deletion mutant collection in E. coli across 4 conditions, we identified a number of mutants with defects in envelope assembly. Among those were genes known to be involved in envelope biogenesis, as well as 102 genes of unknown function. In the course of my work I built upon and improved this screening approach, to acquire quantitative membrane permeability measurements that can be used for high- throughput chemical genomics approaches. Gram-negative bacterial envelope is both a permeability barrier, and a structural barrier. The structural component mainly consists of the rigid peptidoglycan (PG) sacculus, which gives the cells the ability to withstand both turgor pressure and environmental insults. Although biosynthesis of PG is central to bacteria and a target of β-lactam antibiotics, its regulation remains largely elusive. Recently, a number of regulators of PG biosynthesis have been identified, and shown to have coevolved with domains in PG synthases. With the aim of uncovering potential regulatory connections, I developed a computational approach to explore the coevolution of domains in proteins involved in cell wall biosynthesis and remodeling with other proteins in the cell. The method correctly identified existing regulatory interactions, and is readily applied to species across the bacterial kingdom

Computational methods for integrating and analyzing human systems biology data

The combination of heterogeneous biological datasets is a key requirement for modern molecular systems biology. Of particular importance for our understanding of complex biological systems like the human cell are data about the interactions of proteins with other molecules. In this thesis, we develop and apply methods to improve the availability and the quality of such interaction data. We also demonstrate how these data can be used in interdisciplinary studies to discover new biological results. First, we develop technical systems for the instant integration of interaction data that are stored and maintained in separate online repositories. Second, we implement a computational framework for the application of multiple scoring algorithms to qualitatively assess different aspects of interaction data. Our methods are based on distributed client-server systems, ensuring that the services can be updated continuously. This promotes equal access to interaction data and allows researchers to expand the client-server systems with their own service. Third, we focus our application studies on integrative network-based analyses of human host factors for viral infections. Our applications provide new biological insights into the life cycle of the hepatitis C virus and identify new potential candidates for antiviral drug therapy.Die Kombination verschiedener biologischer Datensätze ist für die moderne molekulare Systembiologie unumgänglich. Eine besondere Bedeutung für unser Verständnis von komplexen biologischen Systemen wie der Zelle haben dabei Daten über die Wechselwirkungen von Proteinen mit anderen Molekülen. In dieser Arbeit entwickeln und verwenden wir Methoden zur Verbesserung der Verfügbarkeit und Bewertbarkeit von solchen Interaktionsdaten. Wir zeigen auch, wie diese Daten in interdisziplinären Studien genutzt werden können, um neue biologische Erkenntnisse zu gewinnen. Zuerst entwickeln wir technische Systeme, um Interaktionsdaten von verschiedenen Quellen des Internets zusammenzuführen. Danach entwickeln wir ein computergestütztes System, welches die Anwendung verschiedener Algorithmen ermöglicht, um unterschiedliche Aspekte von Wechselwirkungen qualitativ zu bewerten. Unsere Methoden basieren auf verteilten Client-Server-Systemen, die sicherstellen, dass einzelne Dienste dauerhaft aktuell gehalten werden können. Zudem fördert dies einen gleichberechtigten Zugang zu Interaktionsdaten, und Wissenschaftler können die Systeme mit eigenen Diensten erweitern. Unser Anwendungsschwerpunkt liegt auf der netzwerkbasierten Analyse humaner Wirtsfaktoren für virale Infektionen. Unsere Auswertungen tragen zu einem besseren Verständnis des Lebenszyklus des Hepatitis-C-Virus bei und zeigen Ansatzpunkte für die Entwicklung neuer antiviraler Medikamente auf

Computational methods for integrating and analyzing human systems biology data

The combination of heterogeneous biological datasets is a key requirement for modern molecular systems biology. Of particular importance for our understanding of complex biological systems like the human cell are data about the interactions of proteins with other molecules. In this thesis, we develop and apply methods to improve the availability and the quality of such interaction data. We also demonstrate how these data can be used in interdisciplinary studies to discover new biological results. First, we develop technical systems for the instant integration of interaction data that are stored and maintained in separate online repositories. Second, we implement a computational framework for the application of multiple scoring algorithms to qualitatively assess different aspects of interaction data. Our methods are based on distributed client-server systems, ensuring that the services can be updated continuously. This promotes equal access to interaction data and allows researchers to expand the client-server systems with their own service. Third, we focus our application studies on integrative network-based analyses of human host factors for viral infections. Our applications provide new biological insights into the life cycle of the hepatitis C virus and identify new potential candidates for antiviral drug therapy.Die Kombination verschiedener biologischer Datensätze ist für die moderne molekulare Systembiologie unumgänglich. Eine besondere Bedeutung für unser Verständnis von komplexen biologischen Systemen wie der Zelle haben dabei Daten über die Wechselwirkungen von Proteinen mit anderen Molekülen. In dieser Arbeit entwickeln und verwenden wir Methoden zur Verbesserung der Verfügbarkeit und Bewertbarkeit von solchen Interaktionsdaten. Wir zeigen auch, wie diese Daten in interdisziplinären Studien genutzt werden können, um neue biologische Erkenntnisse zu gewinnen. Zuerst entwickeln wir technische Systeme, um Interaktionsdaten von verschiedenen Quellen des Internets zusammenzuführen. Danach entwickeln wir ein computergestütztes System, welches die Anwendung verschiedener Algorithmen ermöglicht, um unterschiedliche Aspekte von Wechselwirkungen qualitativ zu bewerten. Unsere Methoden basieren auf verteilten Client-Server-Systemen, die sicherstellen, dass einzelne Dienste dauerhaft aktuell gehalten werden können. Zudem fördert dies einen gleichberechtigten Zugang zu Interaktionsdaten, und Wissenschaftler können die Systeme mit eigenen Diensten erweitern. Unser Anwendungsschwerpunkt liegt auf der netzwerkbasierten Analyse humaner Wirtsfaktoren für virale Infektionen. Unsere Auswertungen tragen zu einem besseren Verständnis des Lebenszyklus des Hepatitis-C-Virus bei und zeigen Ansatzpunkte für die Entwicklung neuer antiviraler Medikamente auf