References of "Krause, Roland 50002132"
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See detailMutation in the transcriptional regulator PhoP contributes to avirulence of Mycobacterium tuberculosis H37Ra strain.
Lee, Jong Seok; Krause, Roland UL; Schreiber, Jorg et al

in Cell Host & Microbe (2008), 3(2), 97-103

Attenuated strains of mycobacteria can be exploited to determine genes essential for their pathogenesis and persistence. To this goal, we sequenced the genome of H37Ra, an attenuated variant of ... [more ▼]

Attenuated strains of mycobacteria can be exploited to determine genes essential for their pathogenesis and persistence. To this goal, we sequenced the genome of H37Ra, an attenuated variant of Mycobacterium tuberculosis H37Rv strain. Comparison with H37Rv revealed three unique coding region polymorphisms. One polymorphism was located in the DNA-binding domain of the transcriptional regulator PhoP, causing the protein's diminished DNA-binding capacity. Temporal gene expression profiles showed that several genes with reduced expression in H37Ra were also repressed in an H37Rv phoP knockout strain. At later time points, genes of the dormancy regulon, typically expressed in a state of nonreplicating persistence, were upregulated in H37Ra. Complementation of H37Ra with H37Rv phoP partially restored its persistence in a murine macrophage infection model. Our approach demonstrates the feasibility of identifying minute but distinct differences between isogenic strains and illustrates the consequences of single point mutations on the survival stratagem of M. tuberculosis. [less ▲]

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See detailIdentifying protein complexes directly from high-throughput TAP data with Markov random fields.
Rungsarityotin, Wasinee; Krause, Roland UL; Schodl, Arno et al

in BMC Bioinformatics (2007), 8

BACKGROUND: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the ... [more ▼]

BACKGROUND: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes. RESULTS: We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes. CONCLUSION: We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes. [less ▲]

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See detailIdentifying protein complexes in high-throughput protein interaction screens using an infinite latent feature model.
Chu, Wei; Ghahramani, Zoubin; Krause, Roland UL et al

in Pacific Symposium on Biocomputing (2006)

We propose a Bayesian approach to identify protein complexes and their constituents from high-throughput protein-protein interaction screens. An infinite latent feature model that allows for multi-complex ... [more ▼]

We propose a Bayesian approach to identify protein complexes and their constituents from high-throughput protein-protein interaction screens. An infinite latent feature model that allows for multi-complex membership by individual proteins is coupled with a graph diffusion kernel that evaluates the likelihood of two proteins belonging to the same complex. Gibbs sampling is then used to infer a catalog of protein complexes from the interaction screen data. An advantage of this model is that it places no prior constraints on the number of complexes and automatically infers the number of significant complexes from the data. Validation results using affinity purification/mass spectrometry experimental data from yeast RNA-processing complexes indicate that our method is capable of partitioning the data in a biologically meaningful way. A supplementary web site containing larger versions of the figures is available at http://public.kgi.edu/wild/PSBO6/index.html. [less ▲]

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See detailShared components of protein complexes--versatile building blocks or biochemical artefacts?
von Mering, Christian; Bork, Peer; Dandekar, Thomas et al

in BioEssays : news and reviews in molecular, cellular and developmental biology (2004), 26(12), 1333-43

Protein complexes perform many important functions in the cell. Large-scale studies of protein-protein interactions have not only revealed new complexes but have also placed many proteins into multiple ... [more ▼]

Protein complexes perform many important functions in the cell. Large-scale studies of protein-protein interactions have not only revealed new complexes but have also placed many proteins into multiple complexes. Whilst the advocates of hypothesis-free research touted the discovery of these shared components as new links between diverse cellular processes, critical commentators denounced many of the findings as artefacts, thus questioning the usefulness of large-scale approaches. Here, we survey proteins known to be shared between complexes, as established in the literature, and compare them to shared components found in high-throughput screens. We discuss the various challenges to the identification and functional interpretation of bona fide shared components, namely contaminants, variant and megacomplexes, and transient interactions, and suggest that many of the novel shared components found in high-throughput screens are neither the results of contamination nor central components, but appear to be primarily regulatory links in cellular processes. [less ▲]

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See detailA comprehensive set of protein complexes in yeast: mining large scale protein-protein interaction screens.
Krause, Roland UL; von Mering, Christian; Bork, Peer

in Bioinformatics (Oxford, England) (2003), 19(15), 1901-8

MOTIVATION: The analysis of protein-protein interactions allows for detailed exploration of the cellular machinery. The biochemical purification of protein complexes followed by identification of ... [more ▼]

MOTIVATION: The analysis of protein-protein interactions allows for detailed exploration of the cellular machinery. The biochemical purification of protein complexes followed by identification of components by mass spectrometry is currently the method, which delivers the most reliable information--albeit that the data sets are still difficult to interpret. Consolidating individual experiments into protein complexes, especially for high-throughput screens, is complicated by many contaminants, the occurrence of proteins in otherwise dissimilar purifications due to functional re-use and technical limitations in the detection. A non-redundant collection of protein complexes from experimental data would be useful for biological interpretation, but manual assembly is tedious and often inconsistent. RESULTS: Here, we introduce a measure to define similarity within collections of purifications and generate a set of minimally redundant, comprehensive complexes using unsupervised clustering. AVAILABILITY: Programs and results are freely available from http://www.bork.embl-heidelberg.de/Docu/purclust/ [less ▲]

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See detailFunctional organization of the yeast proteome by systematic analysis of protein complexes.
Gavin, Anne-Claude; Bosche, Markus; Krause, Roland UL et al

in Nature (2002), 415(6868), 141-7

Most cellular processes are carried out by multiprotein complexes. The identification and analysis of their components provides insight into how the ensemble of expressed proteins (proteome) is organized ... [more ▼]

Most cellular processes are carried out by multiprotein complexes. The identification and analysis of their components provides insight into how the ensemble of expressed proteins (proteome) is organized into functional units. We used tandem-affinity purification (TAP) and mass spectrometry in a large-scale approach to characterize multiprotein complexes in Saccharomyces cerevisiae. We processed 1,739 genes, including 1,143 human orthologues of relevance to human biology, and purified 589 protein assemblies. Bioinformatic analysis of these assemblies defined 232 distinct multiprotein complexes and proposed new cellular roles for 344 proteins, including 231 proteins with no previous functional annotation. Comparison of yeast and human complexes showed that conservation across species extends from single proteins to their molecular environment. Our analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions. This higher-order map contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery. [less ▲]

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