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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.
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.
The NO/cGMP pathway inhibits Rap1 activation in human platelets via cGMP-dependent protein kinase I
(2005)
The NO/cGMP signalling pathway strongly inhibits agonist-induced platelet aggregation. However, the molecular mechanisms involved are not completely defined.We have studied NO/cGMP effects on the activity of Rap1, an abundant guanine-nucleotidebinding protein in platelets. Rap1-GTP levels were reduced by NO-donors and activators of NO-sensitive soluble guanylyl cyclase. Four lines of evidence suggest that NO/cGMP effects are mediated by cGMP-dependent protein kinase (cGKI): (i) Rap1 inhibition correlated with cGKI activity as measured by the phosphorylation state ofVASP, an established substrate of cGKI, (ii) 8-pCPT-cGMP, a membrane permeable cGMP-analog and activator of cGKI, completely blocked Rap1 activation, (iii) Rp- 8pCPT-cGMPS, a cGKI inhibitor, reversed NO effects and (iv) expression of cGKI in cGKI-deficient megakaryocytes inhibited Rap1 activation. NO/cGMP/cGKI effects were independent of the type of stimulus used for Rap1 activation.Thrombin-,ADPand collagen-induced formation of Rap1-GTP in platelets as well as turbulence-induced Rap1 activation in megakaryocytes were inhibited. Furthermore, cGKI inhibited ADP-induced Rap1 activation induced by the G a i -coupled P2Y12 receptor alone, i.e. independently of effects on Ca2+-signalling. From these studies we conclude that NO/cGMP inhibit Rap1 activation in human platelets and that this effect is mediated by cGKI. Since Rap1 controls the function of integrin a IIbß 3 , we propose that Rap1 inhibition might play a central role in the anti-aggregatory actions of NO/cGMP.
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.
The 5'-terminal cloverleaf (CL)-like RNA structures are essential for the initiation of positive- and negative-strand RNA synthesis of entero- and rhinoviruses. SLD is the cognate RNA ligand of the viral proteinase 3C (3Cpro), which is an indispensable component of the viral replication initiation complex. The structure of an 18mer RNA representing the apical stem and the cGUUAg D-loop of SLD from the first 5'-CL of BEV1 was determined in solution to a root-mean-square deviation (r.m.s.d.) (all heavy atoms) of 0.59 A (PDB 1Z30). The first (antiG) and last (synA) nucleotide of the D-loop forms a novel ‘pseudo base pair’ without direct hydrogen bonds. The backbone conformation and the base-stacking pattern of the cGUUAg-loop, however, are highly similar to that of the coxsackieviral uCACGg D-loop (PDB 1RFR) and of the stable cUUCGg tetraloop (PDB 1F7Y) but surprisingly dissimilar to the structure of a cGUAAg stable tetraloop (PDB 1MSY), even though the cGUUAg BEV D-loop and the cGUAAg tetraloop differ by 1 nt only. Together with the presented binding data, these findings provide independent experimental evidence for our model [O. Ohlenschläger, J. Wöhnert, E. Bucci, S. Seitz, S. Häfner, R. Ramachandran, R. Zell and M. Görlach (2004) Structure, 12, 237–248] that the proteinase 3Cpro recognizes structure rather than sequence.
In order to further understand how DNA polymerases discriminate against incorrect dNTPs, we synthesized two sets of dNTP analogues and tested them as substrates for DNA polymerase a (pol alpha) and Klenow fragment (exo-) of DNA polymerase I (Escherichia coli ). One set of analogues was designed to test the importance of the electronic nature of the base. The bases consisted of a benzimidazole ring with one or two exocyclic substituent(s) that are either electron-donating (methyl and methoxy) or electronwithdrawing (trifluoromethyl and dinitro). Both pol a and Klenow fragment exhibit a remarkable inability to discriminate against these analogues as compared to their ability to discriminate against incorrect natural dNTPs. Neither polymerase shows any distinct electronic or steric preferences for analogue incorporation. The other set of analogues, designed to examine the importance of hydrophobicity in dNTP incorporation, consists of a set of four regioisomers of trifluoromethyl benzimidazole. Whereas pol a and Klenow fragment exhibited minimal discrimination against the 5- and 6-regioisomers, they discriminated much more effectively against the 4- and 7-regioisomers. Since all four of these analogues will have similar hydrophobicity and stacking ability, these data indicate that hydrophobicity and stacking ability alone cannot account for the inability of pol a and Klenow fragment to discriminate against unnatural bases. After incorporation, however, both sets of analogues were not efficiently elongated. These results suggest that factors other than hydrophobicity, sterics and electronics govern the incorporation of dNTPs into DNA by pol {alpha} and Klenow fragment.
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.
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.
Background: The flavin in its FMN and FAD forms is a versatile cofactor that is involved in catalysis of most disparate types of biological reactions. These include redox reactions such as dehydrogenations, activation of dioxygen, electron transfer, bioluminescence, blue light reception, photobiochemistry (as in photolyases), redox signaling etc. Recently, hitherto unrecognized types of biological reactions have been uncovered that do not involve redox shuffles, and might involve the reduced form of the flavin as a catalyst. The present work addresses properties of reduced flavin relevant in this context. Results: N(5)-H exchange reactions of the flavin reduced form and its pH dependence were studied using the 15N-NMR-signals of 15N-enriched, reduced flavin in the pH range from 5 to 12. The chemical shifts of the N(3) and N(5) resonances are not affected to a relevant extent in this pH range. This contrasts with the multiplicity of the N(5)-resonance, which strongly depends on pH. It is a doublet between pH 8.45 and 10.25 that coalesces into a singlet at lower and higher pH values. From the line width of the 15N(5) signal the pH-dependent rate of hydrogen exchange was deduced. The multiplicity of the 15N(5) signal and the proton exchange rates are little dependent on the buffer system used. Conclusion: The exchange rates allow an estimation of the pKa value of N(5)-H deprotonation in reduced flavin to be ≥ 20. This value imposes specific constraints for mechanisms of flavoprotein catalysis based on this process. On the other hand the pK ≈ 4 for N(5)-H protonation (to form N(5)+-H2) would be consistent with a role of N(5)-H as a base.