P083 - GCB 2006 - German Conference on Bioinformatics 2006
Auflistung P083 - GCB 2006 - German Conference on Bioinformatics 2006 nach Erscheinungsdatum
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- KonferenzbeitragAb initio prediction of molecular fragments from tandem mass spectrometry data(German Conference on Bioinformatics, 2006) Heinonen, Markus; Rantanen, Ari; Mielikäinen, Taneli; Pitkänen, Esa; Kokkonen, Juha; Rousu, JuhoMass spectrometry is one of the key enabling measurement technologies for systems biology, due to its ability to quantify molecules in small concentrations. Tandem mass spectrometers tackle the main shortcoming of mass spectrometry, the fact that molecules with an equal mass-to-charge ratio are not separated. In tandem mass spectrometer molecules can be fragmented and the intensities of these fragments measured as well. However, this creates a need for methods for identifying the generated fragments. In this paper, we introduce a novel combinatorial approach for predicting the structure of molecular fragments that first enumerates all possible fragment candidates and then ranks them according the cost of cleaving a fragment from a molecule. Unlike many existing methods, our method does not rely on hand-coded fragmentation rule databases. Our method is able to predict the correct fragmentation of small-to-medium sized molecules with high accuracy.
- KonferenzbeitragPushing details into interaction networks(German Conference on Bioinformatics, 2006) Russell, Rob
- KonferenzbeitragEncoding evolvability: The hierarchical language of polyketide synthase protein interactions(German Conference on Bioinformatics, 2006) Thattai, Mukund
- KonferenzbeitragCombining sequence information with T-coffee(German Conference on Bioinformatics, 2006) Notredame, Cedric
- KonferenzbeitragShape distributions and protein similarity(German Conference on Bioinformatics, 2006) Canzar, Stefan; Remy, JanIn this paper we describe a similarity model that provides the objective basis for clustering proteins of similar structure. More specifically, we consider the following variant of the protein-protein similarity problem: We want to find proteins in a large database D that are very similar to a given query protein in terms of geometric shape. We give experimental evidence, that the shape similarity model of Osada, Funkhouser, Chazelle and Dobkin [OFCD02] can be transferred to the context of protein structure comparison. This model is very simple and leads to algorithms that have attractive space requirements and running times. For example, it took 0.39 seconds to retrieve the eight members of the seryl family out of 26, 600 domains. Furthermore, a very high agreement with one of the most popular classification schemes proved the significance of our simplified representation of complex proteins structure by a distribution of C��-C��distances.
- KonferenzbeitragCharacterization of protein interactions(German Conference on Bioinformatics, 2006) Küffner, Robert; Duchrow, Timo; Fundel, Kartin; Zimmer, RalfAvailable information on molecular interactions between proteins is currently incomplete with regard to detail and comprehensiveness. Although a number of repositories are already devoted to capture interaction data, only a small subset of the currently known interactions can be obtained that way. Besides further experiments, knowledge on interactions can only be complemented by applying text extraction methods to the literature. Currently, information to further characterize individual interactions can not be provided by interaction extraction approaches and is virtually nonexistent in repositories. We present an approach to not only confirm extracted interactions but also to characterize interactions with regard to four attributes such as activation vs. inhibition and protein-protein vs. protein-gene interactions. Here, training corpora with positional annotation of interacting proteins are required. As suitable corpora are rare, we propose an extensible curation protocol to conveniently characterize interactions by manual annotation of sentences so that machine learning approaches can be applied subsequently. We derived a training set by manually reading and annotating 269 sentences for 1090 candidate interactions; 439 of these are valid interactions, predicted via support vector machines at a precision of 83% and a recall of 87%. The prediction of interaction attributes from individual sentences on average yielded a precision of about 85% and a recall of 73%.
- KonferenzbeitragA novel, comprehensive method to detect and predict protein-protein interactions applied to the study of vesicular trafficking(German Conference on Bioinformatics, 2006) Winter, Christof; Baust, Thorsten; Hoflack, Bernard; Schroeder, MichaelMotivation. Computational methods to predict protein-protein interactions are of great need. They can help to formulate hypotheses, guide experimental research and serve as additional measures to assess the quality of data obtained in high-throughput interaction experiments. Here, we describe a fully automated threestep procedure to predict and confirm protein-protein interactions. By maximising the information from text mining of the biomedical literature, data from interaction databases, and from available protein structures, we aim at generating a comprehensive picture of known and novel potential interactions between a given set of proteins. Results. A recent proteomics assay to identify the protein machinery involved in vesicular trafficking between the biosynthetic and the endosomal compartments revealed 35 proteins that were found as part of membrane coats on liposomes. When applying our method to this data set, we are able to reconstruct most of the interactions known to the molecular biologist. In addition, we predict novel interactions, among these potential linkers of the AP-1 and the Arp2/3 complex to membrane-bound proteins as well as a potential GTPase-GTPase effector interaction. Conclusions. Our method allows for a comprehensive network reconstruction that can assist the molecular biologist. Predicted interactions are backed up by structural or experimental evidence and can be inferred at varying levels of confidence. Our method pinpoints existing key interactions and can facilitate the generation of hypotheses.
- KonferenzbeitragImaging-based systems biology(German Conference on Bioinformatics, 2006) Myers, Gene
- KonferenzbeitragDocking protein domains using a contact map representation(German Conference on Bioinformatics, 2006) Lise, Stefano; Jones, David