Structural features of halophilicity derived from the crystal structure of dihydrofolate reductase from the Dead Sea halophilic archaeon, Haloferax volcanii

AbstractBackground: The proteins of halophilic archaea require high salt concentrations both for stability and for activity, whereas they denature at low ionic strength. The structural basis for this phenomenon is not yet well understood. The crystal structure of dihydrofolate reductase (DHFR) from Haloferax volcanii (hv-DHFR) reported here provides the third example of a structure of a protein from a halophilic organism. The enzyme is considered moderately halophilic, as it retains activity and secondary structure at monovalent salt concentrations as low as 0.5 M.Results: The crystal structure of hv-DHFR has been determined at 2.6 å resolution and reveals the same overall fold as that of other DHFRs. The structure is in the apo state, with an open conformation of the active-site gully different from the open conformation seen in other DHFR structures. The unique feature of hv-DHFR is a shift of the α helix encompassing residues 46–51 and an accompanied altered conformation of the ensuing loop relative to other DHFRs. Analysis of the charge distribution, amino acid composition, packing and hydrogen-bonding pattern in hv-DHFR and its non-halophilic homologs has been performed.Conclusions: The moderately halophilic behavior of hv-DHFR is consistent with the lack of striking structural features expected to occur in extremely halophilic proteins. The most notable feature of halophilicity is the presence of clusters of non-interacting negatively charged residues. Such clusters are associated with unfavorable electrostatic energy at low salt concentrations, and may account for the instability of hv-DHFR at salt concentrations lower than 0.5 M. With respect to catalysis, the open conformation seen here is indicative of a conformational transition not reported previously. The impact of this conformation on function and/or halophilicity is unknown

Protein complex compositions predicted by structural similarity

Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain—porcine α–amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE ()

DBAli tools: mining the protein structure space

The DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions

Target selection and annotation for the structural genomics of the amidohydrolase and enolase superfamilies

To study the substrate specificity of enzymes, we use the amidohydrolase and enolase superfamilies as model systems; members of these superfamilies share a common TIM barrel fold and catalyze a wide range of chemical reactions. Here, we describe a collaboration between the Enzyme Specificity Consortium (ENSPEC) and the New York SGX Research Center for Structural Genomics (NYSGXRC) that aims to maximize the structural coverage of the amidohydrolase and enolase superfamilies. Using sequence- and structure-based protein comparisons, we first selected 535 target proteins from a variety of genomes for high-throughput structure determination by X-ray crystallography; 63 of these targets were not previously annotated as superfamily members. To date, 20 unique amidohydrolase and 41 unique enolase structures have been determined, increasing the fraction of sequences in the two superfamilies that can be modeled based on at least 30% sequence identity from 45% to 73%. We present case studies of proteins related to uronate isomerase (an amidohydrolase superfamily member) and mandelate racemase (an enolase superfamily member), to illustrate how this structure-focused approach can be used to generate hypotheses about sequence–structure–function relationships

From protein sequences to 3D-structures and beyond: the example of the UniProt Knowledgebase

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website (http://www.uniprot.org/). It also evokes precautions that are necessary for successful predictions and extrapolations

Is parental language sexually differentiated?

1771807Studia Anglica Posnaniensi

Der Fall aus der Perspektive der Fachdidaktik. Fachliche Lernprozesse als Ziel und Ausgangspunkt

Im Diskursraum von Erziehungswissenschaft und Fachdidaktik wird der Fallarbeit für die Förderung der Professionalität von (angehenden und praktizierenden) Lehrkräften gegenwärtig hohes Potenzial zugestanden. Auch in forschungsmethodischer Hinsicht erscheinen fallbezogene Zugriffe für die verschiedenen Arbeitsfelder ergiebig. [...] Gut beobachtbar ist mindestens in der Deutschdidaktik eine Praxis in Forschung und Lehre, wonach ein Fall als exemplarische Verdichtung eines fachlichen Lehr-/Lernprozesses bestimmt und eingesetzt wird, der im Rahmen gründlicher Fallanalysen für die wissenschaftliche Fachdidaktik erschlossen werden kann. Hier sehen die Autorinnen ein besonderes Potenzial fallorientierter Arbeit und entwerfen daher zunächst eine systematisch fachdidaktische Perspektive auf Fallarbeit, die sie anschließend anhand zweier Fälle konkretisieren. (DIPF/Orig.