Probabilistic protein homology modeling

Searching sequence databases and building 3D models for proteins are important tasks for biologists. When the structure of a query protein is given, its function can be inferred. However, experimental methods for structure prediction are both expensive and time consuming. Fully automatic homology modeling refers to building a 3D model for a query sequence from an alignment to related homologous proteins with known structure (templates) by a computer. Current prediction servers can provide accurate models within a few hours to days. Our group has developed HHpred, which is one of the top performing structure prediction servers in the field. In general, homology based structure modeling consists of four steps: (1) finding homologous templates in a database, (2) selecting and (3) aligning templates to the query, (4) building a 3D model based on the alignment. In part one of this thesis, we will present improvements of step (2) and (4). Specifically, homology modeling has been shown to work best when multiple templates are selected instead of only a single one. Yet, current servers are using rather ad-hoc approaches to combine information from multiple templates. We provide a rigorous statistical framework for multi-template homology modeling. Given an alignment, we employ Modeller to calculate the most probable structure for a query. The 3D model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions. We find that the query’s atomic distance restraints can be accurately described by two-component Gaussian mixtures. Moreover, we derive statistical weights to quantify the redundancy among related templates. This allows us to apply the standard rules of probability theory to combine restraints from several templates. Together with a heuristic template selection strategy, we have implemented this approach within HHpred and could significantly improve model quality. Furthermore, we took part in CASP, a community wide competition for structure prediction, where we were ranked first in template based modeling and, at the same time, were more than 450 times faster than all other top servers. Homology modeling heavily relies on detecting and correctly aligning templates to the query sequence (step (1) and (3) from above). But remote homologies are difficult to detect and hard to align on a pure sequence level. Hence, modern tools are based on profiles instead of sequences. A profile summarizes the evolutionary history of a given sequence and consists of position specific amino acid probabilities for each residue. In addition to the similarity score between profile columns, most methods use extra terms that compare 1D structural properties such as secondary structure or solvent accessibility. These can be predicted from local profile windows. In the second part of this thesis, we develop a new score that is independent of any predefined structural property. For this purpose, we learn a library of 32 profile patterns that are most conserved in alignments of remotely homologous, structurally aligned proteins. Each so called “context state” in the library consists of a 13-residue sequence profile. We integrate the new context score into our Hmm-Hmm alignment tool HHsearch and improve especially the sensitivity and precision of difficult pairwise alignments significantly. Taken together, we introduced probabilistic methods to improve all four main steps in homology based structure prediction

Identifying indicator species for post-release monitoring of genetically modified, herbicide resistant crops

In Europe, regulations for release and placing-on-the-market of genetically modified (GM) crops require post-release monitoring of their impact on the environment. Monitoring potential adverse effects of GM crops includes direct effects as well as indirect effects, e.g. GM crop specific changes in land and pest management. Currently, there is a gap in the pre-release risk assessments conducted for regulatory approval of GM herbicide resistant (HR) crops. Since the relevant non-selective herbicides have been registered many years ago, in current dossiers requesting regulatory approval of GM HR crops, the environmental impacts of the corresponding non-selective herbicides are either entirely omitted or the applicant simply refers to the eco-toxicological safety assessments conducted for its original pesticide approval that do not address environmental issues arising in conjunction with the cultivation of GM HR crops. Since the ‘Farm-scale Evaluations', it is clear that consequences for farmland biodiversity can be expected. The objective of this project was to identify relevant indicator species for the long-term impact of GM HR maize cultivation and the application of their corresponding non-selective herbicides, glyphosate and glufosinate. In this article, we describe the outcome of a modified Event Tree Analysis, essentially a funnel-like procedure allowing to reduce the large number of potentially affected non-target species to those with greatest ecological relevance and highest risk to be adversely affected based on a number of ecological criteria. This procedure allowed us to identify a total of 21 weed-Lepidoptera associations that we proposed for post release monitoring of GM HR maize in German

Probabilistic protein homology modeling

Searching sequence databases and building 3D models for proteins are important tasks for biologists. When the structure of a query protein is given, its function can be inferred. However, experimental methods for structure prediction are both expensive and time consuming. Fully automatic homology modeling refers to building a 3D model for a query sequence from an alignment to related homologous proteins with known structure (templates) by a computer. Current prediction servers can provide accurate models within a few hours to days. Our group has developed HHpred, which is one of the top performing structure prediction servers in the field. In general, homology based structure modeling consists of four steps: (1) finding homologous templates in a database, (2) selecting and (3) aligning templates to the query, (4) building a 3D model based on the alignment. In part one of this thesis, we will present improvements of step (2) and (4). Specifically, homology modeling has been shown to work best when multiple templates are selected instead of only a single one. Yet, current servers are using rather ad-hoc approaches to combine information from multiple templates. We provide a rigorous statistical framework for multi-template homology modeling. Given an alignment, we employ Modeller to calculate the most probable structure for a query. The 3D model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions. We find that the query’s atomic distance restraints can be accurately described by two-component Gaussian mixtures. Moreover, we derive statistical weights to quantify the redundancy among related templates. This allows us to apply the standard rules of probability theory to combine restraints from several templates. Together with a heuristic template selection strategy, we have implemented this approach within HHpred and could significantly improve model quality. Furthermore, we took part in CASP, a community wide competition for structure prediction, where we were ranked first in template based modeling and, at the same time, were more than 450 times faster than all other top servers. Homology modeling heavily relies on detecting and correctly aligning templates to the query sequence (step (1) and (3) from above). But remote homologies are difficult to detect and hard to align on a pure sequence level. Hence, modern tools are based on profiles instead of sequences. A profile summarizes the evolutionary history of a given sequence and consists of position specific amino acid probabilities for each residue. In addition to the similarity score between profile columns, most methods use extra terms that compare 1D structural properties such as secondary structure or solvent accessibility. These can be predicted from local profile windows. In the second part of this thesis, we develop a new score that is independent of any predefined structural property. For this purpose, we learn a library of 32 profile patterns that are most conserved in alignments of remotely homologous, structurally aligned proteins. Each so called “context state” in the library consists of a 13-residue sequence profile. We integrate the new context score into our Hmm-Hmm alignment tool HHsearch and improve especially the sensitivity and precision of difficult pairwise alignments significantly. Taken together, we introduced probabilistic methods to improve all four main steps in homology based structure prediction

A photoredox catalysed Heck reaction via hole transfer from a Ru(ii)-bis(terpyridine) complex to graphene oxide

The attachment of homoleptic Ru bis-terpy complexes on graphene oxide significantly improved the photocatalytic activity of the complexes. These straightforward complexes were applied as photocatalysts in a Heck reaction. Due to covalent functionalization on graphene oxide, which functions as an electron reservoir, excellent yields were obtained. DFT investigations of the charge redistribution revealed efficient hole transfer from the excited Ru unit towards the graphene oxide

Forschung für eine naturgerechte Landwirtschaft

Eine Wende hin zu einer naturgerechten Landwirtschaft setzt voraus, dass auch in der Agrarforschung neue Akzente gesetzt werden. Eine Denkschrift* bringt auf den Punkt, was sich in Forschung, Lehre und Ausbildung ändern muss. Die Langfassung dieser Denkschrift, die in diesem Beitrag zusammengefasst wird, kann beim Bundesamt für Naturschutz, Konstantinstr. 110, D-53179 Bonn, Tel. 0228 - 8491 0, Fax - 8491 200, E-Mail [email protected], bezogen oder im Internet unter www.bfn.de/10/ eingesehen und unterzeichnet werden

Acute paranoid psychosis as sole clinical presentation of hepatic artery thrombosis after living donor liver transplantation

<p>Abstract</p> <p>Background</p> <p>Hepatic artery thrombosis is a devastating complication after orthotopic liver transplantation often requiring revascularization or re-transplantation. It is associated with considerably increased morbidity and mortality. Acute cognitive dysfunction such as delirium or acute psychosis may occur after major surgery and may be associated with the advent of surgical complications.</p> <p>Case presentation</p> <p>Here we describe a case of hepatic artery thrombosis after living-donor liver transplantation which was not preceded by signs of liver failure but rather by an episode of acute psychosis. After re-transplantation the patient recovered without sequelae.</p> <p>Conclusion</p> <p>This case highlights the need to remain cautious when psychiatric disorders occur in patients after liver transplantation. The diagnostic procedures should not be restricted to medical or neurological causes of psychosis alone but should also focus vascular complications related to orthotopic liver transplantation.</p

Immunization with lytic polysaccharide monooxygenase CbpD induces protective immunity against Pseudomonas aeruginosa pneumonia

publishedVersio

The \u3cem\u3eChlamydomonas\u3c/em\u3e Genome Reveals the Evolution of Key Animal and Plant Functions

Chlamydomonas reinhardtii is a unicellular green alga whose lineage diverged from land plants over 1 billion years ago. It is a model system for studying chloroplast-based photosynthesis, as well as the structure, assembly, and function of eukaryotic flagella (cilia), which were inherited from the common ancestor of plants and animals, but lost in land plants. We sequenced the ∼120-megabase nuclear genome of Chlamydomonas and performed comparative phylogenomic analyses, identifying genes encoding uncharacterized proteins that are likely associated with the function and biogenesis of chloroplasts or eukaryotic flagella. Analyses of the Chlamydomonas genome advance our understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella