Publications (18)127.81 Total impact
-
Article: An evolutionary and structural characterization of mammalian protein complex organization.
[show abstract] [hide abstract]
ABSTRACT: We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes. As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tends to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins. We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes.BMC Genomics 01/2009; 9:629. · 4.07 Impact Factor -
Article: DIMA 2.0--predicted and known domain interactions.
[show abstract] [hide abstract]
ABSTRACT: 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.Nucleic Acids Research 02/2008; 36(Database issue):D651-5. · 8.03 Impact Factor -
Article: An evolutionary and structural characterization of mammalian protein complex organization
[show abstract] [hide abstract]
ABSTRACT: Abstract Background We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes. Results As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tends to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins. Conclusion We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes.BMC Genomics. 01/2008; -
Article: The minimum information required for reporting a molecular interaction experiment (MIMIx).
[show abstract] [hide abstract]
ABSTRACT: A wealth of molecular interaction data is available in the literature, ranging from large-scale datasets to a single interaction confirmed by several different techniques. These data are all too often reported either as free text or in tables of variable format, and are often missing key pieces of information essential for a full understanding of the experiment. Here we propose MIMIx, the minimum information required for reporting a molecular interaction experiment. Adherence to these reporting guidelines will result in publications of increased clarity and usefulness to the scientific community and will support the rapid, systematic capture of molecular interaction data in public databases, thereby improving access to valuable interaction data.Nature Biotechnology 09/2007; 25(8):894-8. · 23.27 Impact Factor -
Article: Computational prediction of domain interactions.
[show abstract] [hide abstract]
ABSTRACT: Conserved domains carry many of the functional features found in the proteins of an organism. This includes not only catalytic activity, substrate binding, and structural features but also molecular adapters, which mediate the physical interactions between proteins or proteins with other molecules. In addition, two conserved domains can be linked not by physical contact but by a common function like forming a binding pocket. Although a wealth of experimental data has been collected and carefully curated for protein-protein interactions, as of today little useful data is available from major databases with respect to relations on the domain level. This lack of data makes computational prediction of domain-domain interactions a very important endeavor. In this chapter, we discuss the available experimental data (iPfam) and describe some important approaches to the problem of identifying interacting and/or functionally linked domain pairs from different kinds of input data. Specifically, we will discuss phylogenetic profiling on the level of conserved protein domains on one hand and inference of domain-interactions from observed or predicted protein-protein interactions datasets on the other. We explore the predictive power of these predictions and point out the importance of deploying as many different methods as possible for the best results.Methods in molecular biology (Clifton, N.J.) 02/2007; 396:3-15. -
Article: Broadening the horizon--level 2.5 of the HUPO-PSI format for molecular interactions.
[show abstract] [hide abstract]
ABSTRACT: Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions. The HUPO-PSI has further developed the PSI-MI XML schema to enable the description of interactions between a wider range of molecular types, for example nucleic acids, chemical entities, and molecular complexes. Extensive details about each supported molecular interaction can now be captured, including the biological role of each molecule within that interaction, detailed description of interacting domains, and the kinetic parameters of the interaction. The format is supported by data management and analysis tools and has been adopted by major interaction data providers. Additionally, a simpler, tab-delimited format MITAB2.5 has been developed for the benefit of users who require only minimal information in an easy to access configuration. The PSI-MI XML2.5 and MITAB2.5 formats have been jointly developed by interaction data producers and providers from both the academic and commercial sector, and are already widely implemented and well supported by an active development community. PSI-MI XML2.5 enables the description of highly detailed molecular interaction data and facilitates data exchange between databases and users without loss of information. MITAB2.5 is a simpler format appropriate for fast Perl parsing or loading into Microsoft Excel.BMC Biology 02/2007; 5:44. · 5.75 Impact Factor -
Article: Broadening the horizon – level 2.5 of the HUPO-PSI format for molecular interactions
[show abstract] [hide abstract]
ABSTRACT: Abstract Background Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions. Results The HUPO-PSI has further developed the PSI-MI XML schema to enable the description of interactions between a wider range of molecular types, for example nucleic acids, chemical entities, and molecular complexes. Extensive details about each supported molecular interaction can now be captured, including the biological role of each molecule within that interaction, detailed description of interacting domains, and the kinetic parameters of the interaction. The format is supported by data management and analysis tools and has been adopted by major interaction data providers. Additionally, a simpler, tab-delimited format MITAB2.5 has been developed for the benefit of users who require only minimal information in an easy to access configuration. Conclusion The PSI-MI XML2.5 and MITAB2.5 formats have been jointly developed by interaction data producers and providers from both the academic and commercial sector, and are already widely implemented and well supported by an active development community. PSI-MI XML2.5 enables the description of highly detailed molecular interaction data and facilitates data exchange between databases and users without loss of information. MITAB2.5 is a simpler format appropriate for fast Perl parsing or loading into Microsoft Excel.BMC Biology. 01/2007; -
Article: Insights from the genome of the biotrophic fungal plant pathogen Ustilago maydis.
[show abstract] [hide abstract]
ABSTRACT: Ustilago maydis is a ubiquitous pathogen of maize and a well-established model organism for the study of plant-microbe interactions. This basidiomycete fungus does not use aggressive virulence strategies to kill its host. U. maydis belongs to the group of biotrophic parasites (the smuts) that depend on living tissue for proliferation and development. Here we report the genome sequence for a member of this economically important group of biotrophic fungi. The 20.5-million-base U. maydis genome assembly contains 6,902 predicted protein-encoding genes and lacks pathogenicity signatures found in the genomes of aggressive pathogenic fungi, for example a battery of cell-wall-degrading enzymes. However, we detected unexpected genomic features responsible for the pathogenicity of this organism. Specifically, we found 12 clusters of genes encoding small secreted proteins with unknown function. A significant fraction of these genes exists in small gene families. Expression analysis showed that most of the genes contained in these clusters are regulated together and induced in infected tissue. Deletion of individual clusters altered the virulence of U. maydis in five cases, ranging from a complete lack of symptoms to hypervirulence. Despite years of research into the mechanism of pathogenicity in U. maydis, no 'true' virulence factors had been previously identified. Thus, the discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi. Genomic analysis is, similarly, likely to open up new avenues for the discovery of virulence determinants in other pathogens.Nature 12/2006; 444(7115):97-101. · 36.28 Impact Factor -
Article: Resources and Tools for Investigating Biomolecular Networks in Mammals
[show abstract] [hide abstract]
ABSTRACT: Molecular databases serve as primary information resources for the analysis of biological networks providing an essential and invaluable treasure for information exploration. Tools for projecting experimental data sets onto known functional information are a major need to support the analysis of samples produced in clinical research. A new concept is the notation of functional modules, i.e. the characterisation of sets of proteins that perform a defined biological function in cooperation. The determination and analysis of functional modules overcome the limitations of the analysis of individual genes and their properties. Although functional modules are not suitable to fully capture systems properties, they have the potential to unify the information generated by different types of experiments. We describe advances related to the problem of integrating heterogeneous data sets into functional modules for mouse and/or human cellular networks based on publicly available data resources, including advances in the design of ontologies for functional classification, problems of automatic protein functional annotation and integration of microarray data.Current Pharmaceutical Design 09/2006; 12(29):3723-3734. · 3.87 Impact Factor -
Article: The DIMA web resource--exploring the protein domain network.
[show abstract] [hide abstract]
ABSTRACT: Conserved domains represent essential building blocks of most known proteins. Owing to their role as modular components carrying out specific functions they form a network based both on functional relations and direct physical interactions. We have previously shown that domain interaction networks provide substantially novel information with respect to networks built on full-length protein chains. In this work we present a comprehensive web resource for exploring the Domain Interaction MAp (DIMA), interactively. The tool aims at integration of multiple data sources and prediction techniques, two of which have been implemented so far: domain phylogenetic profiling and experimentally demonstrated domain contacts from known three-dimensional structures. A powerful yet simple user interface enables the user to compute, visualize, navigate and download domain networks based on specific search criteria. Availability: http://mips.gsf.de/genre/proj/dimaBioinformatics 05/2006; 22(8):997-8. · 5.47 Impact Factor -
Article: Resources and tools for investigating biomolecular networks in mammals.
[show abstract] [hide abstract]
ABSTRACT: Molecular databases serve as primary information resources for the analysis of biological networks providing an essential and invaluable treasure for information exploration. Tools for projecting experimental data sets onto known functional information are a major need to support the analysis of samples produced in clinical research. A new concept is the notation of functional modules, i.e. the characterisation of sets of proteins that perform a defined biological function in cooperation. The determination and analysis of functional modules overcome the limitations of the analysis of individual genes and their properties. Although functional modules are not suitable to fully capture systems properties, they have the potential to unify the information generated by different types of experiments. We describe advances related to the problem of integrating heterogeneous data sets into functional modules for mouse and/or human cellular networks based on publicly available data resources, including advances in the design of ontologies for functional classification, problems of automatic protein functional annotation and integration of microarray data.Current pharmaceutical design 02/2006; 12(29):3723-34. · 4.41 Impact Factor -
Article: MPact: the MIPS protein interaction resource on yeast.
[show abstract] [hide abstract]
ABSTRACT: In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein-protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called a gold standard because of its quality and comprehensiveness [H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107-1118]. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog [A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539-5545] is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through http://mips.gsf.de/genre/proj/mpact.Nucleic Acids Research 02/2006; 34(Database issue):D436-41. · 8.03 Impact Factor -
Article: FGDB: a comprehensive fungal genome resource on the plant pathogen Fusarium graminearum.
[show abstract] [hide abstract]
ABSTRACT: The MIPS Fusarium graminearum Genome Database (FGDB) is a comprehensive genome database on one of the most devastating fungal plant pathogens of wheat and barley. FGDB provides information on two gene sets independently derived by automated annotation of the F.graminearum genome sequence. A complete manually revised gene set will be completed within the near future. The initial results of systematic manual correction of gene calls are already part of the current gene set. The database can be accessed to retrieve information from bioinformatics analyses and functional classifications of the proteins. The data are also organized in the well established MIPS catalogs and novel query techniques are available to search the data. The comprehensive set of gene calls was also used for the design of an Affymetrix GeneChip. The resource is accessible on http://mips.gsf.de/genre/proj/fusarium/.Nucleic Acids Research 02/2006; 34(Database issue):D456-8. · 8.03 Impact Factor -
Article: The Mouse Functional Genome Database (MfunGD): functional annotation of proteins in the light of their cellular context.
[show abstract] [hide abstract]
ABSTRACT: MfunGD (http://mips.gsf.de/genre/proj/mfungd/) provides a resource for annotated mouse proteins and their occurrence in protein networks. Manual annotation concentrates on proteins which are found to interact physically with other proteins. Accordingly, manually curated information from a protein-protein interaction database (MPPI) and a database of mammalian protein complexes is interconnected with MfunGD. Protein function annotation is performed using the Functional Catalogue (FunCat) annotation scheme which is widely used for the analysis of protein networks. The dataset is also supplemented with information about the literature that was used in the annotation process as well as links to the SIMAP Fasta database, the Pedant protein analysis system and cross-references to external resources. Proteins that so far were not manually inspected are annotated automatically by a graphical probabilistic model and/or superparamagnetic clustering. The database is continuously expanding to include the rapidly growing amount of functional information about gene products from mouse. MfunGD is implemented in GenRE, a J2EE-based component-oriented multi-tier architecture following the separation of concern principle.Nucleic Acids Research 02/2006; 34(Database issue):D568-71. · 8.03 Impact Factor -
Article: The MIPS mammalian protein-protein interaction database.
[show abstract] [hide abstract]
ABSTRACT: The MIPS mammalian protein-protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental evidence that has been processed by human expert curators. We provide the full dataset for download and a flexible and powerful web interface for users with various requirements.Bioinformatics 04/2005; 21(6):832-4. · 5.47 Impact Factor -
Article: The MIPS mammalian protein?Cprotein interaction database.
Bioinformatics. 01/2005; 21:832-834. -
Article: Location and size of dopaminergic and serotonergic cell populations are controlled by the position of the midbrain-hindbrain organizer.
[show abstract] [hide abstract]
ABSTRACT: Midbrain dopaminergic and hindbrain serotonergic neurons play an important role in the modulation of behavior and are involved in a series of neuropsychiatric disorders. Despite the importance of these cells, little is known about the molecular mechanisms governing their development. During embryogenesis, midbrain dopaminergic neurons are specified rostral to the midbrain-hindbrain organizer (MHO), and hindbrain serotonergic neurons are specified caudal to it. We report that in transgenic mice in which Otx2 and accordingly the MHO are shifted caudally, the midbrain dopaminergic neuronal population expands to the ectopically positioned MHO and is enlarged. Complementary, the extension of the hindbrain serotonergic cell group is decreased. These changes are preserved in adulthood, and the additional, ectopic dopaminergic neurons project to the striatum, which is a proper dopaminergic target area. In addition, in mutants in which Otx2 and the MHO are shifted rostrally, dopaminergic and serotonergic neurons are relocated at the newly positioned MHO. However, in these mice, the size ratio between these two cell populations is changed in favor of the serotonergic cell population. To investigate whether the position of the MHO during embryogenesis is also of functional relevance for adult behavior, we tested mice with a caudally shifted MHO and report that these mutants show a higher locomotor activity. Together, we provide evidence that the position of the MHO determines the location and size of midbrain dopaminergic and hindbrain serotonergic cell populations in vivo. In addition, our data suggest that the position of the MHO during embryogenesis can modulate adult locomotor activity.Journal of Neuroscience 06/2003; 23(10):4199-207. · 7.11 Impact Factor -
Article: Analysis of integrated biomolecular networks using a generic network analysis suite
[show abstract] [hide abstract]
ABSTRACT: The informative value of biomolecular networks has shifted from being solely information resources for possible cellular partners (whether these embody proteins, (ribo)nucleic acids or small molecules) towards becoming models for the functional connectivity within a cell. These models are increasingly exploited to make quantitative predictions about the cell?s functional organization as well as about the functionality of individual elements in the network. A large number of concepts and methods have been proposed in order to interpret experimental data mapped to cellular networks these systems and to make use of the rich source of information they represent. We will present a system for the Comprehensive Analysis of Biomolecular Networks (CABiNet), capable of integrating available network analysis methods. Integration is done by classifying each method into one of four separate categories using standardized interfaces that encapsulate the functionality of the method in a distinct component with standardized in- and output. These components can be accessed individually or in an integrated form using a processing pipeline for semi-automatic analyses. Additionally, the system can be used to query both biomolecular networks as well as the derived results of network analysis methods, such as clustering algorithms, in order to provide a service for researchers who are focused towards the functional context of any particular cellular entity. CABiNet is designed in an easy-to-use and easy-to-extend software framework that allows a straightforward integration of novel components. We will demonstrate the capabilities of the system by introducing several use cases. The CABiNet suite can be accessed at http://mips.gsf.de/genre/proj/CABiNet. Source code including additional components that can be accessed using the API is available upon request.http://journal.imbio.de/index.php?paper_id=72.
Top co-authors
Top Journals
Institutions
-
2009
-
Helmholtz Zentrum München
- Institut für Bioinformatik und Systembiologie
München, Bavaria, Germany
-
-
2006–2008
-
University of Technology Munich
München, Bavaria, Germany
-
-
2007
-
EMBL-EBI
Cambridge, ENG, United Kingdom
-
