http://dl.gi.de/handle/20.500.12116/21226 Thu, 23 Jul 2026 14:13:43 GMT 2026-07-23T14:13:43Z http://dl.gi.de:80/bitstream/id/76732680-cf85-4fef-a127-b6b5f79edf6f/ http://dl.gi.de/handle/20.500.12116/21226 http://dl.gi.de/handle/20.500.12116/19668 German Conference on Bioinformatics 2010 Schomburg, Dietmar; Grote, Andreas Fri, 01 Jan 2010 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/19668 2010-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/19676 Quantitative comparison of genomic-wide protein domain distributions Parikesit, Arli A.; Stadler, Peter F.; Prohaska, Sonja J. Schomburg, Dietmar; Grote, Andreas Investigations into the origins and evolution of regulatory mechanisms require quantitative estimates of the abundance and co-occurrence of functional protein domains among distantly related genomes. Currently available databases, such as the SUPERFAMILY, are not designed for quantitative comparisons since they are built upon transcript and protein annotations provided by the various different genome annotation projects. Large biases are introduced by the differences in genome annotation protocols, which strongly depend on the availability of transcript information and well-annotated closely related organisms. Here we show that the combination of de novo gene predictors and subsequent HMM-based annotation of SCOP domains in the predicted peptides leads to consistent estimates with acceptable accuracy that in particular can be utilized for systematic studies of the evolution of protein domain occurrences and co-occurrences. As an application, we considered four major classes of DNA binding domains: zink-finger, leucine-zipper, winged-helix, and HMG-box. We found that different types of DNA binding domains systematically avoid each other throughout the evolution of Eukarya. In contrast, DNA binding domains belonging to the same superfamily readily co-occur in the same protein. Fri, 01 Jan 2010 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/19676 2010-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/19671 Shape-based barrier estimation for RNAs Bogomolov, Sergiy; Mann, Martin; Voß, Björn; Podelski, Andreas; Backofen, Rolf Schomburg, Dietmar; Grote, Andreas The ability of some RNA molecules to switch between different metastable conformations plays an important role in cellular processes. In order to identify such molecules and to predict their conformational changes one has to investigate the refolding pathways. As a qualitative measure of these transitions, the barrier height marks the energy peak along such refolding paths. We introduce a meta-heuristic to estimate such barriers, which is an NP-complete problem. To guide an arbitrary path heuristic, the method uses RNA shape representative structures as intermediate checkpoints for detours. This enables a broad but efficient search for refolding pathways. The resulting Shape Triples meta-heuristic enables a close to optimal estimation of the barrier height that outperforms the precision of the employed path heuristic. Fri, 01 Jan 2010 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/19671 2010-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/19674 CASOP GS: computing intervention strategies targeted at production improvement in genome-scale metabolic networks Bohl, Katrin; Figueiredo, Luís F. de; Hädicke, Oliver; Klamt, Steffen; Kost, Christian; Schuster, Stefan; Kaleta, Christoph Schomburg, Dietmar; Grote, Andreas Metabolic engineering aims to improve the production of desired biochemicals and proteins in organisms and therefore, plays a central role in Biotechnology. However, the design of overproducing strains is not straightforward due to the complexity of metabolic and regulatory networks. Thus, theoretical tools supporting the design of such strains have been developed. One particular method, CASOP, uses the set of elementary flux modes (EFMs) of a reaction network to propose strategies for the overproduction of a target compound. The advantage of CASOP over other approaches is that it does not consider a single specific flux distribution within the network but the whole set of possible flux distributions represented by the EFMs of the network. Moreover, its application results not only in the identification of candidate loci that can be knocked out, but additionally proposes overexpression candidates. However, the utilization of CASOP was restricted to small and medium scale metabolic networks so far, since the entire set of EFMs cannot be enumerated in such networks. This work presents an approach that allows to use CASOP even in genome-scale networks. This approach is based on an estimation of the score utilized in CASOP through a sample of EFMs within a genome-scale network. Using EFMs from the genome-scale metabolic network gives a more reliable picture of the metabolic capabilities of an organism required for the design of overproducing strains. We applied our new method to identify strategies for the overproduction of succinate and histidine in Escherichia coli. The succinate case study, in particular, proposes engineering targets which resemble known strategies already applied in E. coli. Availability: Source code and an executable are available upon request. Fri, 01 Jan 2010 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/19674 2010-01-01T00:00:00Z