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Nucleotide-binding domains (NBDs), roughly 27 kDa in size, are conservative components of the large family of ABC (ATP-binding cassette) transporters, which includes importers, exporters, and receptors. NBDs or ABC-ATPases supply energy for the translocation of a vast variety of substrates across biological membranes. Despite their hydrophilic sequence, many NBDs tend to aggregate and precipitate in solution upon isolation from the complete transporter. The conditions stabilizing an extremely labile NBD component of the E.coli HlyA transporter, HlyB-NBD, were developed. As a result, the pure highly concentrated enzyme was protected from precipitation for months that allowed screening of the unlimited crystallization conditions in the presence of different substrates and performance of the reproducible functional assays. HlyB-NBD was characterized in regard to its uncoupled ATPase activity, oligomeric state, and stability in solution. Comparative analysis of protein stability and ATPase activity in various buffers suggested an inverse relationship between the two. Kinetic analysis of ATPase activity revealed ATP-induced protein dimerization. Gel-filtration experiments with the wild type protein and H662A-mutant of HlyB-NBD provided further evidence of protein dimerization in the presence of ATP. The crystal structures in post- and pre-hydrolysis nucleotide-bound states of HlyB-NBD were determined at 1.6Å and 2.5Å resolution, respectively. While the hydrolytically deficient H662A mutant of HlyB-NBD was crystallized as a stable dimer in the presence of ATP or ATP-Mg2+, with two nucleotide molecules sandwiched between the two monomers, the same protein was shown to be a monomer in the ADP-loaded state. The wild type protein failed to develop crystals with bound ATP, yet formed ADP-bound crystals identical to those of the H662A-mutant. The X-ray structures of HlyB-NBD in various states of the hydrolytic cycle and the functional studies of the enzyme have provided an opportunity to characterize enzyme-substrate complexes and protein-protein interactions between the NBD subunits in great detail. Comparison of the nucleotide-free, the ADP-, and the ATP-loaded states revealed oligomeric and conformational changes of the protein upon substrate binding and resulted in a molecular picture of the catalytic cycle. The correlated results of the structural and functional investigations of HlyB-NBD are discussed with relation to the mechanism of action of ABC transporters.
Group III presynaptic metabotropic glutamate receptors (mGluRs) play a central role in regulating presynaptic activity through G-protein effects on ion channels and signal transducing enzymes. Like all Class C G-protein coupled receptors, mGluR8 has an extended intracellular C-terminal domain (CTD) presumed to allow for modulation of downstream signaling. To elucidate the function and modulation of mGluR8, yeast two-hybrid screens of an adult rat brain cDNA library were performed with the CTDs of mGluR8a and 8b (mGluR8-C) as baits. Different components of the sumoylation cascade (ube2a, sumo-1, Pias1, Pias gamma and Pias xbeta) and some other proteins were identified as mGluR8 interacting proteins. Binding assays using recombinant GST-fusion proteins confirmed that Pias1 interacts not only with mGluR8-C, but all group III mGluR CTDs. Pias1 binding to mGluR8-C required a region N-terminally to a consensus sumoylation motif and was not affected by arginine substitution of the conserved lysine K882 within this motif. Co-transfection of fluorescently tagged mGluR8a-C, sumo-1 and enzymes of the sumoylation cascade into HEK 293 cells showed that mGluR8a-C can be sumoylated in cells. Arginine substitution of lysine K882 within the consensus sumoylation motif, but not of other conserved lysines within the CTD, abolished in vivo sumoylation. The results are consistent with post-translational sumoylation providing a novel mechanism of group III mGluR regulation.
My graduate thesis is on the "Structural studies of membrane transport proteins". Transporters are membrane proteins that have multiple membrane-spanning a-helices. They are dynamic and diverse proteins, undergoing a large conformational change and transporting wide range of susbtrates. Based on their energy source they can be classified into primary and secondary transport systems. Primary transport systems are driven by the use of chemical (ATP) or light energy, while secondary transporters utilize ion gradients to transport substrates. I began my PhD dissertation on secondary transporters by two-dimensional crystallization and electron crystallographic analysis and recently my focus also has shifted towards 3D crystallization. The following projects constitute my PhD thesis: 1) 2D crystallization of MjNhaP1 and pH induced structural change: MjNhaP1, a Na+/H+ antiporter that is regulated by pH has been implicated in homeostasis of H+ and Na+ in Methanococcus jannaschii, a hyperthermophilic archaeon that grows optimally at 85°C. MjNhaP1 was cloned and expressed in E. coli. Two-dimensional crystals were obtained from purified protein at pH4. Electron cryo-microscopy yielded an 8Å projection map. The map of MjNhaP1 shows elongated densities in the centre of the dimer and a cluster of density peaks on either side of the dimer core, indicative of a bundle of 4-6 membrane-spanning helices. The effect of pH on the structure of MjNhaP1was studied in situ in 2D crystals revealing a major change in density within the helix bundle relative to the dimer interface. This change occurred at pH6 and above. The two conformations at low and high pH most likely represent the closed and open states of the antiporter, respectively. This is the first instance where a conformational change associated with the regulation of a secondary transporter appears to map structurally. Reconstruction of 3D map and high-resolution structure by x-ray crystallography would be necessary to understand the mechanism of ion transport and regulation by pH. 2) 2D crystallization of Proline transporter: Proline transporter (PutP) from E.coli belongs the sodium-solute symporter family that includes disease related sodium dependent glucose and iodide transporter in humans. Sodium and proline are co-transported with a stoichiometry of 1:1. Purified PutP was reconstituted to yield 2D crystals that were hexagonal in nature. The 2D crystals had tendency to stack indicating their willingness to form 3D crystals. A projection map of PutP from negatively stained crystals showed trimeric arrangement of protein. Other members of the SSF family have been shown to be monomers. My analysis of oligomeric state of PutP in detergent by blue native gel indicates a monomer in detergent solution. It is likely that PutP can function as a monomer but at higher concentration and in lipid bilayer it tends to form trimer. 3) Oligomeric state and crystallization of carnitine transporter from E.coli: E.coli carnitine transporter (CaiT) belongs to the BCCT (Betaine, Carnitine and Choline) superfamily that transports molecules with quaternary amine groups. CaiT is predicted to span the membrane 12 times and acts as a L-carnitine/g-butyrobetaine exchanger. Unlike other members in this transporter family, it does not require an ion gradient and does not respond to osmotic stress. Over-expression of the protein yielded ~2mg of protein/L of culture. The structure and oligomeric state of the protein were analyzed in detergent and lipid bilayers. Blue native gel electrophoresis indicated that CaiT was a trimer in detergent solution. Gel filtration and cross-linking studies further support this. Reconstitution of CaiT into lipid bilayers resulted in 2D crystals. Analysis of negatively stained 2D crystals confirmed that CaiT is a trimer in the membrane. Initial 3D crystallization trials have been successful and currently, the crystals diffract to 6Å and are being improved. 4) Monomeric porin OmpG: OmpG is a bacterial outer membrane b-barrel protein. It is monomeric and its size (33kDa) places it as a prime candidate for a structural solution, using the recently developed method of solid state NMR (work in collaboration with Prof.Hartmut Oskinat, FMP, Berlin). A long-term aim would be to study porins as templates for designing nanopores, for DNA sequencing and identification. I have expressed OmpG in inclusion bodies and refolded at an efficiency of >90% into a functional form using detergent. OmpG was then crystallized by 2D crystallization yielding an 8Å projection map whose structure was similar to native protein. In addition, these crystals were used for structure determination by solid state NMR. An initial spectrum of heavy isotopically labeled OmpG has allowed identification of specific amino acid residues including threonine and proline. Additionally, I obtained 3D crystals in detergent that diffract to 5.5Å and are being improved.
Protein-protein interactions within the plane of cellular membranes play a key role for many biological processes and in particular for transmembrane signaling. A prominent example is the ligand-induced crosslinking of cytokine receptors, where 3- dimensional cytokine binding followed by 2-dimensional interaction between the receptor subunits have been recognized to be important for regulating signaling specificity. The fundamental importance of such coupled interactions for cell-surface receptor activation has stimulated numerous theoretical studies, which have hardly been confirmed experimentally. An experimental approach to measure interactions and real time kinetics of type I interferon (IFN) induced assembly between interferon receptor subunits ifnar2 and ifnar1 on membrane was developed and determinants of the 2-dimensional interactions, such as dimensionality, size, valency, orientation, membrane fluidity and receptor density were quantitatively addressed The C-terminal decahistidine tagged extracellular domains (EC) of ifnar1 and ifnar2 were site- specifically tethered onto solid-supported fluid lipid membrane, which carried covalently attached chelator bis-nitrilotriacetic acid (bis-NTA) groups. Interactions on the lipid bilayer were detected with a novel solid phase detection technique, which allows simultaneous detection of ligand binding to a membrane anchored receptors and lateral interaction between them in the real time. This was achieved by combining two optical techniques: label-free reflectance interferometry (RIf) and total internal reflection fluorescence spectroscopy (TIRFS). Fluorescence signals, in the order of 10 fluorophores/µm2, were detected without substantial photobleaching. The sensitivity of the label-free interferometric detection was in the range of 10 pg/mm2. The crosstalk between the two signals was eliminated by means of spectral separation. Fluorescence was detected in the visible region and RIf was performed at 800 nm in the near infrared. Flow through conditions allowed to automate experiments and measure binding events as fast as ~ 5 s-1. Using this technique we have dissected the interactions involved in IFN-induced ifnar crosslinking. 2-dimensional association and dissociation rate constants were independently determined by tethering high stoichiometric excess of one of the receptor subunits and comparing dissociation of the labelled ligand away from the membrane in the absence and presence of the non-labelled high affinity competitor. Dissociation traces were fitted with the two-step dissociation model: the first step being the 2-dimensional separation of the ternary complex followed by the 3- dimensional ligand dissociation into solution. Label-free RIf detection allowed absolute parameterization of the 2-dimensional concentrations of the ifnar subunits on the membrane. The TIRFS signal provided high sensitivity of the ligand dissociation and was correlated against the RIf signal before fitting. These features of the detection system allowed us to parameterize the model, and the 2-dimensional association or dissociation rate constants were the only variables during the fitting. Another FRET based binding assay was developed to determine the 2- dimensional dissociation rate constant using a pulse-chase approach. The donor fluorescence from ifnar2-EC was quenched upon the ternary complex formation with the acceptor-labelled IFN and the nonlabelled ifnar1-EC. The equilibrium was perturbed by rapid tethering of substantial excess of the nonlabelled ifnar2-EC onto the membrane. The exchange of the labelled ifnar2-EC with the nonlabelled one was monitored as the decrease in the FRET signal with the 2-dimensional dissociation of ifnar2-EC from the ternary complex being the rate limiting step. Based on the several mutants and variants of the interacting proteins, the effect of different rate constants and receptor orientation on the 2-dimensional crosslinking dynamics was studied. We have identified several critical features of the 2- dimensional interactions on membranes, which cannot be readily concluded from the solution binding assays. The restricted rotation and the increased lifetime of the encounter complex due to high membrane viscosity are the main determinants of the 2-dimensional association. Tethering ifnar1-EC to the membrane via N-terminal decahistidine tag decreased the 2-dimensional association rate constant 4-5 fold. Electrostatic attraction and steering, the important mechanism to enhance association rate constant between the soluble proteins, are not pronounced for interactions on the membrane. Protein orientation due to membrane anchoring dominates over electrostatic effects and together with the increased lifetime of the encounter complex consequence that 2-dimensional association rate constants are quite similar and do not correlate with association rate constants in solution. The 2- dimensional dissociation rate constants were generally 2-5-fold lower compared to the corresponding 3-dimensional dissociation rate constants in solution. Possible explanations for this are that long lifetime of the encounter complex stabilizes the ternary complex or that membrane tethering affects the interaction diagram. In conclusion, combined TIRFS-RIf detection turn to be powerful and versatile technique to characterize protein-protein interactions on membranes.
Virtual screening of potential bioactive substances using the support vector machine approach
(2005)
Die vorliegende Dissertation stellt eine kumulative Arbeit dar, die in insgesamt acht wissenschaftlichen Publikationen (fünf publiziert, zwei eingerichtet und eine in Vorbereitung) dargelegt ist. In diesem Forschungsprojekt wurden Anwendungen von maschinellem Lernen für das virtuelle Screening von Moleküldatenbanken durchgeführt. Das Ziel war primär die Einführung und Überprüfung des Support-Vector-Machine (SVM) Ansatzes für das virtuelle Screening nach potentiellen Wirkstoffkandidaten. In der Einleitung der Arbeit ist die Rolle des virtuellen Screenings im Wirkstoffdesign beschrieben. Methoden des virtuellen Screenings können fast in jedem Bereich der gesamten pharmazeutischen Forschung angewendet werden. Maschinelles Lernen kann einen Einsatz finden von der Auswahl der ersten Moleküle, der Optimierung der Leitstrukturen bis hin zur Vorhersage von ADMET (Absorption, Distribution, Metabolism, Toxicity) Eigenschaften. In Abschnitt 4.2 werden möglichen Verfahren dargestellt, die zur Beschreibung von chemischen Strukturen eingesetzt werden können, um diese Strukturen in ein Format zu bringen (Deskriptoren), das man als Eingabe für maschinelle Lernverfahren wie Neuronale Netze oder SVM nutzen kann. Der Fokus ist dabei auf diejenigen Verfahren gerichtet, die in der vorliegenden Arbeit verwendet wurden. Die meisten Methoden berechnen Deskriptoren, die nur auf der zweidimensionalen (2D) Struktur basieren. Standard-Beispiele hierfür sind physikochemische Eigenschaften, Atom- und Bindungsanzahl etc. (Abschnitt 4.2.1). CATS Deskriptoren, ein topologisches Pharmakophorkonzept, sind ebenfalls 2D-basiert (Abschnitt 4.2.2). Ein anderer Typ von Deskriptoren beschreibt Eigenschaften, die aus einem dreidimensionalen (3D) Molekülmodell abgeleitet werden. Der Erfolg dieser Beschreibung hangt sehr stark davon ab, wie repräsentativ die 3D-Konformation ist, die für die Berechnung des Deskriptors angewendet wurde. Eine weitere Beschreibung, die wir in unserer Arbeit eingesetzt haben, waren Fingerprints. In unserem Fall waren die verwendeten Fingerprints ungeeignet zum Trainieren von Neuronale Netzen, da der Fingerprintvektor zu viele Dimensionen (~ 10 hoch 5) hatte. Im Gegensatz dazu hat das Training von SVM mit Fingerprints funktioniert. SVM hat den Vorteil im Vergleich zu anderen Methoden, dass sie in sehr hochdimensionalen Räumen gut klassifizieren kann. Dieser Zusammenhang zwischen SVM und Fingerprints war eine Neuheit, und wurde von uns erstmalig in die Chemieinformatik eingeführt. In Abschnitt 4.3 fokussiere ich mich auf die SVM-Methode. Für fast alle Klassifikationsaufgaben in dieser Arbeit wurde der SVM-Ansatz verwendet. Ein Schwerpunkt der Dissertation lag auf der SVM-Methode. Wegen Platzbeschränkungen wurde in den beigefügten Veröffentlichungen auf eine detaillierte Beschreibung der SVM verzichtet. Aus diesem Grund wird in Abschnitt 4.3 eine vollständige Einführung in SVM gegeben. Darin enthalten ist eine vollständige Diskussion der SVM Theorie: optimale Hyperfläche, Soft-Margin-Hyperfläche, quadratische Programmierung als Technik, um diese optimale Hyperfläche zu finden. Abschnitt 4.3 enthält auch eine Diskussion von Kernel-Funktionen, welche die genaue Form der optimalen Hyperfläche bestimmen. In Abschnitt 4.4 ist eine Einleitung in verschiede Methoden gegeben, die wir für die Auswahl von Deskriptoren genutzt haben. In diesem Abschnitt wird der Unterschied zwischen einer „Filter“- und der „Wrapper“-basierten Auswahl von Deskriptoren herausgearbeitet. In Veröffentlichung 3 (Abschnitt 7.3) haben wir die Vorteile und Nachteile von Filter- und Wrapper-basierten Methoden im virtuellen Screening vergleichend dargestellt. Abschnitt 7 besteht aus den Publikationen, die unsere Forschungsergebnisse enthalten. Unsere erste Publikation (Veröffentlichung 1) war ein Übersichtsartikel (Abschnitt 7.1). In diesem Artikel haben wir einen Gesamtüberblick der Anwendungen von SVM in der Bio- und Chemieinformatik gegeben. Wir diskutieren Anwendungen von SVM für die Gen-Chip-Analyse, die DNASequenzanalyse und die Vorhersage von Proteinstrukturen und Proteininteraktionen. Wir haben auch Beispiele beschrieben, wo SVM für die Vorhersage der Lokalisation von Proteinen in der Zelle genutzt wurden. Es wird dabei deutlich, dass SVM im Bereich des virtuellen Screenings noch nicht verbreitet war. Um den Einsatz von SVM als Hauptmethode unserer Forschung zu begründen, haben wir in unserer nächsten Publikation (Veröffentlichung 2) (Abschnitt 7.2) einen detaillierten Vergleich zwischen SVM und verschiedenen neuronalen Netzen, die sich als eine Standardmethode im virtuellen Screening etabliert haben, durchgeführt. Verglichen wurde die Trennung von wirstoffartigen und nicht-wirkstoffartigen Molekülen („Druglikeness“-Vorhersage). Die SVM konnte 82% aller Moleküle richtig klassifizieren. Die Klassifizierung war zudem robuster als mit dreilagigen feedforward-ANN bei der Verwendung verschiedener Anzahlen an Hidden-Neuronen. In diesem Projekt haben wir verschiedene Deskriptoren zur Beschreibung der Moleküle berechnet: Ghose-Crippen Fragmentdeskriptoren [86], physikochemische Eigenschaften [9] und topologische Pharmacophore (CATS) [10]. Die Entwicklung von weiteren Verfahren, die auf dem SVM-Konzept aufbauen, haben wir in den Publikationen in den Abschnitten 7.3 und 7.8 beschrieben. Veröffentlichung 3 stellt die Entwicklung einer neuen SVM-basierten Methode zur Auswahl von relevanten Deskriptoren für eine bestimmte Aktivität dar. Eingesetzt wurden die gleichen Deskriptoren wie in dem oben beschriebenen Projekt. Als charakteristische Molekülgruppen haben wir verschiedene Untermengen der COBRA Datenbank ausgewählt: 195 Thrombin Inhibitoren, 226 Kinase Inhibitoren und 227 Faktor Xa Inhibitoren. Es ist uns gelungen, die Anzahl der Deskriptoren von ursprünglich 407 auf ungefähr 50 zu verringern ohne signifikant an Klassifizierungsgenauigkeit zu verlieren. Unsere Methode haben wir mit einer Standardmethode für diese Anwendung verglichen, der Kolmogorov-Smirnov Statistik. Die SVM-basierte Methode erwies sich hierbei in jedem betrachteten Fall als besser als die Vergleichsmethoden hinsichtlich der Vorhersagegenauigkeit bei der gleichen Anzahl an Deskriptoren. Eine ausführliche Beschreibung ist in Abschnitt 4.4 gegeben. Dort sind auch verschiedene „Wrapper“ für die Deskriptoren-Auswahl beschrieben. Veröffentlichung 8 beschreibt die Anwendung von aktivem Lernen mit SVM. Die Idee des aktiven Lernens liegt in der Auswahl von Molekülen für das Lernverfahren aus dem Bereich an der Grenze der verschiedenen zu unterscheidenden Molekülklassen. Auf diese Weise kann die lokale Klassifikation verbessert werden. Die folgenden Gruppen von Moleküle wurden genutzt: ACE (Angiotensin converting enzyme), COX2 (Cyclooxygenase 2), CRF (Corticotropin releasing factor) Antagonisten, DPP (Dipeptidylpeptidase) IV, HIV (Human immunodeficiency virus) protease, Nuclear Receptors, NK (Neurokinin receptors), PPAR (peroxisome proliferator-activated receptor), Thrombin, GPCR und Matrix Metalloproteinasen. Aktives Lernen konnte die Leistungsfähigkeit des virtuellen Screenings verbessern, wie sich in dieser retrospektiven Studie zeigte. Es bleibt abzuwarten, ob sich das Verfahren durchsetzen wird, denn trotzt des Gewinns an Vorhersagegenauigkeit ist es aufgrund des mehrfachen SVMTrainings aufwändig. Die Publikationen aus den Abschnitten 7.5, 7.6 und 7.7 (Veröffentlichungen 5-7) zeigen praktische Anwendungen unserer SVM-Methoden im Wirkstoffdesign in Kombination mit anderen Verfahren, wie der Ähnlichkeitssuche und neuronalen Netzen zur Eigenschaftsvorhersage. In zwei Fällen haben wir mit dem Verfahren neuartige Liganden für COX-2 (cyclooxygenase 2) und dopamine D3/D2 Rezeptoren gefunden. Wir konnten somit klar zeigen, dass SVM-Methoden für das virtuelle Screening von Substanzdatensammlungen sinnvoll eingesetzt werden können. Es wurde im Rahmen der Arbeit auch ein schnelles Verfahren zur Erzeugung großer kombinatorischer Molekülbibliotheken entwickelt, welches auf der SMILES Notation aufbaut. Im frühen Stadium des Wirstoffdesigns ist es wichtig, eine möglichst „diverse“ Gruppe von Molekülen zu testen. Es gibt verschiedene etablierte Methoden, die eine solche Untermenge auswählen können. Wir haben eine neue Methode entwickelt, die genauer als die bekannte MaxMin-Methode sein sollte. Als erster Schritt wurde die „Probability Density Estimation“ (PDE) für die verfügbaren Moleküle berechnet. [78] Dafür haben wir jedes Molekül mit Deskriptoren beschrieben und die PDE im N-dimensionalen Deskriptorraum berechnet. Die Moleküle wurde mit dem Metropolis Algorithmus ausgewählt. [87] Die Idee liegt darin, wenige Moleküle aus den Bereichen mit hoher Dichte auszuwählen und mehr Moleküle aus den Bereichen mit niedriger Dichte. Die erhaltenen Ergebnisse wiesen jedoch auf zwei Nachteile hin. Erstens wurden Moleküle mit unrealistischen Deskriptorwerten ausgewählt und zweitens war unser Algorithmus zu langsam. Dieser Aspekt der Arbeit wurde daher nicht weiter verfolgt. In Veröffentlichung 6 (Abschnitt 7.6) haben wir in Zusammenarbeit mit der Molecular-Modeling Gruppe von Aventis-Pharma Deutschland (Frankfurt) einen SVM-basierten ADME Filter zur Früherkennung von CYP 2C9 Liganden entwickelt. Dieser nichtlineare SVM-Filter erreichte eine signifikant höhere Vorhersagegenauigkeit (q2 = 0.48) als ein auf den gleichen Daten entwickelten PLS-Modell (q2 = 0.34). Es wurden hierbei Dreipunkt-Pharmakophordeskriptoren eingesetzt, die auf einem dreidimensionalen Molekülmodell aufbauen. Eines der wichtigen Probleme im computerbasierten Wirkstoffdesign ist die Auswahl einer geeigneten Konformation für ein Molekül. Wir haben versucht, SVM auf dieses Problem anzuwenden. Der Trainingdatensatz wurde dazu mit jeweils mehreren Konformationen pro Molekül angereichert und ein SVM Modell gerechnet. Es wurden anschließend die Konformationen mit den am schlechtesten vorhergesagten IC50 Wert aussortiert. Die verbliebenen gemäß dem SVM-Modell bevorzugten Konformationen waren jedoch unrealistisch. Dieses Ergebnis zeigt Grenzen des SVM-Ansatzes auf. Wir glauben jedoch, dass weitere Forschung auf diesem Gebiet zu besseren Ergebnissen führen kann.
In the present study possible sources and pathways of the gasoline additive methyl tertiary-butyl ether (MTBE) in the aquatic environment in Germany were investigated. The objective of the present study was to clarify some of the questions raised by a previous study on the MTBE situation in Germany. In the USA and Europe 12 million t and 3 million t of MTBE, respectively, are used as gasoline additive. The detection of MTBE in the aquatic environment and the potential risk for drinking water resources led to a phase-out of MTBE as gasoline additive in single states of the USA. Meanwhile there is also an ongoing discussion about the substitution of MTBE in Europe and Germany. The annual usage of MTBE in Germany is about 600,000 t. However, compared to the USA, significant less data exists on the occurrence of MTBE in the aquatic environment in Europe. Because of its physico-chemical properties, MTBE readily vaporizes from gasoline, is water soluble, adsorbs only weakly to the underground matrix and is largely persistent to biological degradation. The toxicity of MTBE remains to be completely investigated, but MTBE in drinking water has low taste- and odor thresholds of 20-40 microgram/L. The present study was conducted by collecting water samples and analyzing them for their MTBE concentrations through a combination of headspace-solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS). The detection limit was 10 ng/L. The method was successfully tested in the framework of an interlaboratory study and showed recoveries of reference values of 89% (74 ng/L) and 104% (256 ng/L). The relative standard deviations were 12% and 6%. The investigation of 83 water samples from 50 community water systems (CWSs) in Germany revealed a detection frequency of 40% and a concentration range of 17-712 ng/L. The detection of MTBE in the drinking water samples could be explained by a groundwater pollution and the pathway river - riverbank filtration - waterworks. Rivers are important drinking water sources. MTBE is emitted into rivers through a variety of sources. In the present study, potential point sources were investigated, i.e. MTBE production sites/refineries/tank farms and groundwater pollutions. For this purpose, the spatial distribution of MTBE in three German rivers with the named potential emission sources located close to the rivers was investigated by analyzing 49 corresponding river water samples. The influence of the potential emission sources groundwater pollution and refinery/tank farm was successfully demonstrated in certain parts of the River Saale and the River Rhine. Increasing MTBE concentrations from 24 ng/L to 379 ng/L and from 73 ng/L to 5 microgram/L, respectively, could be observed in the parts investigated in these two rivers. The identification of such emission sources is important for future modeling. Further sources of MTBE emission into surface water are industrial (non-petrochemical) and municipal sewage plant effluents. In the present study long-term monitoring of water from the River Main (n=67 samples), precipitation (n=89) and industrial (n=34) and municipal sewage plant effluents (n=66) was conducted. The comparison of the data sets revealed that maximum MTBE concentrations in the River Main of up to 1 microgram/L were most possibly due to single industrial effluents with MTBE concentrations of up to 28 microgram/L (measured in this study). The average MTBE content of 66 ng/L in the River Main most probably originated from municipal sewage plant effluents and further industrial effluents. Background concentrations of <30 ng/L could be related to the direct atmospheric input via precipitation. A certain aspect of the atmospheric MTBE input is represented by the input of MTBE into river water or groundwater through snow. In the present study 43 snow samples from 13 different locations were analyzed for their MTBE content. MTBE could be detected in 65% of the urban and rural samples. The concentrations ranged from 11-613 ng/L and were higher than the concentrations in rainwater samples formerly analyzed. Furthermore, a temperature dependency and wash-out effects could be observed. The atmospheric input of MTBE was in part also visible in the analyzed groundwater samples (n=170). The detection frequencies in non-urban and urban wells were 24% and 63%, respectively. The median concentrations were 177 ng/L and 57 ng/L. In wells located in the vicinity of sites with gasoline contaminated groundwater, MTBE concentrations of up to 42 mg/L could be observed. The MTBE emission sources and the different pathways of MTBE in the aquatic environment demonstrated in the present study and other works raise the question whether the use of MTBE in a bulk product like gasoline should be continued in the future. Currently, possible substitutes like ethyl tertiary-butyl ether (ETBE) or ethanol are being discussed.
Biophysical investigation of the ligand-induced assembling of the human type I interferon receptor
(2005)
Type I interferons (IFNs) elicit antiviral, antiproliferative and immunmodulatory responses through binding to a shared receptor consisting of the transmembrane proteins ifnar1 and ifnar2. Differential signaling by different interferons – in particular IFNalpha´s and IFNbeta – suggest different modes of receptor engagement. In this work either single ligand-receptor interactions or the formation of the extracellular part of a signaling complex were investigated referring to thermodynamics, kinetics, stoichiometry and structural organization. Initially an expression and purification strategy for the extracellular domain of ifnar1 (ifnar1-EC) using Sf9 insect cells yielding in mg amounts of glycosylated protein was established. Using reflectometric interference spectroscopy (RIfS) the interactions between IFNalpha2/beta and ifnar1-EC and ifnar2-EC was studied in order to understand the individual energetic contributions within the ternary complex. For IFNalpha2 a Kd of 5 µM for the interaction with ifnar1-EC was determined. Substantially tighter binding of IFNbeta with both ifnar2-EC and ifnar1-EC compared to IFNalpha2 was observed. For neither IFNalpha2 nor IFNbeta stabilization of the complex with ifnar1-EC in presence of soluble ifnar2-EC was detectable. In addition, no direct interaction between ifnar2 and ifnar1 was could be shown. Thus, stem-stem interactions between the extracellular domains of ifnar1 and ifnar2 do not seem to play a role for ternary complex formation. Furthermore, ligand-induced cross-talk between ifnar1-EC and ifnar2-EC being tethered onto solid-supported, fluid lipid bilayers was investigated by RIfS and total internal reflection fluorescence spectroscopy. A very stable binding of IFNalpha2 at high receptor surface concentrations was observed with an apparent kd approximately 200-times lower than for ifnar2-EC alone. This apparent kd was strongly dependent on the surface concentration of the receptor components, suggesting kinetic rather than static stabilization, which was corroborated by competition experiments. These results indicate that signaling is activated by transient cross-talk between ifnar1 and ifnar2, which is by several orders of magnitude more efficiently engaged by IFNbeta than by IFNalpha2. With respect to differential recognition of different IFNs ifnar1-EC was dissected into sub-fragments containing different of the four Ig-like domains. The appropriate folding and glycosylation of these proteins, also purified in mg amounts were confirmed by SDS-PAGE, size exclusion chromatography and CD-spectroscopy. Surprisingly, only one construct containing all three N-terminal Ig-like domains was active in terms of ligand binding, indicating that these domains were required. Competitive binding of IFNalpha2 and IFNbeta to both this fragment and ifnar1-EC was demonstrated. Cellular binding assays with different fragments, however, highlight the key role of the membrane-proximal Ig-like domain for the formation of an in situ IFN-receptor complex and the ensuing signal activation. Even substitution with Ig-like domains from homologous cytokine receptors did not restore high-affinity ligand binding. Receptor assembling analysis on supported lipid bilayer revealed that appropriate orientation of the receptor is required, which is controlled by the membrane-proximal Ig-domain. All results indicate that differential signalling is encoded by the efficiency of signalling complex formation, which is controlled by the binding affinity of IFNs to the extracellular domains of ifnar1 and 2.
Mitochondial NADH:ubiquinone oxidoreductase (complex I) the largest multiprotein enzyme of the respiratory chain, catalyses the transfer of two electrons from NADH to ubiquinone, coupled to the translocation of four protons across the membrane. In addition to the 14 strictly conserved central subunits it contains a variable number of accessory subunits. At present, the best characterized enzyme is complex I from bovine heart with a molecular mass of about 980 kDa and 32 accessory proteins. In this study, the subunit composition of mitochondrial complex I from the aerobic yeast Y. lipolytica has been analysed by a combination of proteomic and genomic approaches. The sequences of 37 complex I subunits were identified. The sum of their individual molecular masses (about 930 kDa) was consistent with the native molecular weight of approximately 900 kDa for Y. lipolytica complex I obtained by BN-PAGE. A genomic analysis with Y. lipolytica and other eukaryotic databases to search for homologues of complex I subunits revealed 31 conserved proteins among the examined species. A novel protein named “X” was found in purified Y. lipolytica complex I by MALDI-MS. This protein exhibits homology to the thiosulfate sulfurtransferase enzyme referred to as rhodanese. The finding of a rhodanese-like protein in isolated complex I of Y. lipolytica allows to assume a special regulatory mechanism of complex I activity through control of the status of its iron-sulfur clusters. The second part of this study was aimed at investigating the possible role of one of these extra subunits, 39 kDa (NUEM) subunit which is related to the SDRs-enzyme family. The members of this family function in different redox and isomerization reactions and contain a conserved NAD(P)H-binding site. It was proposed that the 39 kDa subunit may be involved in a biosynthetic pathway, but the role of this subunit in complex I is unknown. In contrast to the situation in N. crassa, deletion of the 39 kDa encoding gene in Y. lipolytica led to the absence of fully assembled complex I. This result might indicate a different pathway of complex I assembly in both organisms. Several site-directed mutations were generated in the nucleotide binding motif. These had either no effect on enzyme activity and NADPH binding, or prevented complex I assembly. Mutations of arginine-65 that is located at the end of the second b-strand and responsible for selective interaction with the 2’-phosphate group of NADPH retained complex I activity in mitochondrial membranes but the affinity for the cofactor was markedly decreased. Purification of complex I from mutants resulted in decrease or loss of ubiquinone reductase activity. It is very likely that replacement of R65 not only led to a decrease in affinity for NADPH but also caused instability of the enzyme due to steric changes in the 39 kDa subunit. These data indicate that NADPH bound to the 39 kDa subunit (NUEM) is not essential for complex I activity, but probably involved in complex I assembly in Y. lipolytica.
Calcium-activated potassium channels are fundamental regulators of neuron excitability. SK channels are activated by an intracellular increase of Ca++ (such as occurs during an action potential). They have a small single channel conductance (less than 20pS) and show no voltage dependence of activation. To date, there are only a few examples of high-resolution structures of eukaryotic membrane proteins. All of them were purified from natural sources. Since no abundant natural sources of eukaryotic K+ channels are available we overexpressed rSK2 in order to produce the quantities necessary for structural analysis. Unfortunately the Pichia pastoris expression system did not yield sufficient amount of pure protein, mainly because most of the protein was retained by in the ER and was only partially soluble. Subsequently, two constructs were expressed: SK2-FCYENE (containing a specific sequence that promotes surface expression), and SK2-q-CaM a concatamer of SK2 and calmodulin. Although these proved an improvement in terms of solubilisation, little improvement was found in terms of amounts of purified material obtained. For this reason we tested the Semliki Forest virus expression system, since the protein is expressed in a mammalian system where we hoped that it would be trafficked in the same way as in vivo. Using this system it was possible to express rSK2 and solubilise it with several detergents and to achieve much better purification. However, the levels were still not sufficient for high-resolution structural studies, although sufficient for single particle electron microscopy analysis.
Molecular dynamics (MD) simulation serves as an important and widely used computational tool to study molecular systems at an atomic resolution. No experimental technique is capable of generating a complete description of the dynamical structure of the biomolecules in their native solution environment. MD simulations allow us to study the dynamics and structure of the system and, moreover, helps in the interpretation of experimental observations. MD simulation was first introduced and applied by Alder and Wainwright in 1957 \cite{Alder57}. However, the first MD simulation of a macromolecule of biological interest was published 28 years ago \cite{McCammon77}. The simulation was concerned with the bovine pancreatic trypsin inhibitor (BPTI) protein, which has served as the hydrogen molecule'' of protein dynamics because of its small size, high stability, and relatively accurate X-ray structure available in 1977 \cite{Deisenhofer75}. This method is now widely used to tackle larger and more complex biological systems \cite{Groot01,Roux02} and has been facilitated by the development of fast and efficient methods for treating the long-range electrostatic interactions \cite{Essmann95}, the availability of faster parallel computers, and the continuous development of empirical molecular mechanical force fields \cite{Langley98,Cheatham99,Foloppe00}. It took several years until the first MD simulations of nucleic acid systems were performed \cite{Levitt83,Tidor83,Prabhakaran83,Nilsson86}. These investigations, which were also performed in vacuo, clearly demonstrated the importance of proper handling of electrostatics in a highly charged nucleic acid system, and different approaches, such as reduction of the phosphate charges and addition of hydrated counterions, have been applied to remedy this shortcoming and to maintain stable DNA structures. A few years later, the first MD simulation of a DNA molecule, including explicit water molecules and counterions was published \cite{Seibel85}. Various MD simulations on fully solvated RNA molecules with explicit inclusion of mobile ions indicated the importance of proper treatment of the environment of highly charged nucleic acids \cite{Lee95,Zichi95,Auffinger97,Auffinger99}. Given the central roles of RNA in the life of cells, it is important to understand the mechanism by which RNA forms three dimensional structures endowed with properties such as catalysis, ligand binding, and recognition of proteins. Furthermore, the increasing awareness of the essential role of RNA in controlling viral replication and in bacterial protein synthesis emphazises the potential of ribonucleicacids as targets for developing new antibacterial and new antiviral drugs. Driven by fruitful collaborations in the Sonderforschungsbereich RNA-Ligand interactions" the model RNA systems in this study include various RNA tetraloops and HIV-1 TAR RNA. For the latter system, the binding sites of heteroaromatic compounds have been studied employing automated docking calculations \cite{Goodsell90}. The results show that it is possible to use this tool to dock small rigid ligands to an RNA molecule, while large and flexible molecules are clearly problematic. The main part of this work is focused on MD simulations of RNA tetraloops.