Quantum Hall effect in 2 dimensional GaInAs structure

Transport properties of GaAs / δ – Mn / GaAs / InxGa1-xAs / GaAs structure with Mn δ – layer, which is separated from InxGa1-xAs quantum well (QW) by 3 nm thick GaAs spacer was investigated. This structure with high mobility was characterized by X-ray difractometry and reflectometry. Transport and electrical properties of the structure were measured by using Pulsed Magnetic Field System (PMFS). During investigation of the Shubnikov – de Haas and the Hall effects the main parameters of QW structure such as cyclotron mass, Fermi level, g – factor, Dingle temperature and concentration of holes were estimated. Obtained results show high quality of the prepared structure. However, anomalous Hall effect at temperatures 2.09 K, 3 K, 4.2 K is not clearly observed. Attempts to identify magnetic moment were made. For this purpose the polarity of the filed was changed to the opposite at each shot. As a result hysteresis loop was not observed in the magnetic field dependences of the anomalous Hall resistivity.This can be attributed to the imperfection of the experimental setup.

The Structure and Function of αI Domains in Collagen Receptor and Leukocyte Integrins

Integrins are heterodimeric, signaling transmembrane adhesion receptors that connect the intracellular actin microfilaments to the extracellular matrix composed of collagens and other matrix molecules. Bidirectional signaling is mediated via drastic conformational changes in integrins. These changes also occur in the integrin αI domains, which are responsible for ligand binding by collagen receptor and leukocyte specific integrins. Like intact integrins, soluble αI domains exist in the closed, low affinity form and in the open, high affinity form, and so it is possible to use isolated αI domains to study the factors and mechanisms involved in integrin activation/deactivation. Integrins are found in all mammalian tissues and cells, where they play crucial roles in growth, migration, defense mechanisms and apoptosis. Integrins are involved in many human diseases, such as inflammatory, cardiovascular and metastatic diseases, and so plenty of effort has been invested into developing integrin specific drugs. Humans have 24 different integrins, four of which are collagen receptor (α1β1, α2β1, α10β1, α11β1) and five leukocyte specific integrins (αLβ2, αMβ2, αXβ2, αDβ2, αEβ7). These two integrin groups are quite unselective having both primary and secondary ligands. This work presents the first systematic studies performed on these integrin groups to find out how integrin activation affects ligand binding and selectivity. These kinds of studies are important not only for understanding the partially overlapping functions of integrins, but also for drug development. In general, our results indicated that selectivity in ligand recognition is greatly reduced upon integrin activation. Interestingly, in some cases the ligand binding properties of integrins have been shown to be cell type specific. The reason for this is not known, but our observations suggest that cell types with a higher integrin activation state have lower ligand selectivity, and vice versa. Furthermore, we solved the three-dimensional structure for the activated form of the collagen receptor α1I domain. This structure revealed a novel intermediate conformation not previously seen with any other integrin αI domain. This is the first 3D structure for an activated collagen receptor αI domain without ligand. Based on the differences between the open and closed conformation of the αI domain we set structural criteria for a search for effective collagen receptor drugs. By docking a large number of molecules into the closed conformation of the α2I domain we discovered two polyketides, which best fulfilled the set structural criteria, and by cell adhesion studies we showed them to be specific inhibitors of the collagen receptor integrins.

From protein structure to function with bioinformatics

It has long been known that amino acids are the building blocks for proteins and govern their folding into specific three-dimensional structures. However, the details of this process are still unknown and represent one of the main problems in structural bioinformatics, which is a highly active research area with the focus on the prediction of three-dimensional structure and its relationship to protein function. The protein structure prediction procedure encompasses several different steps from searches and analyses of sequences and structures, through sequence alignment to the creation of the structural model. Careful evaluation and analysis ultimately results in a hypothetical structure, which can be used to study biological phenomena in, for example, research at the molecular level, biotechnology and especially in drug discovery and development. In this thesis, the structures of five proteins were modeled with templatebased methods, which use proteins with known structures (templates) to model related or structurally similar proteins. The resulting models were an important asset for the interpretation and explanation of biological phenomena, such as amino acids and interaction networks that are essential for the function and/or ligand specificity of the studied proteins. The five proteins represent different case studies with their own challenges like varying template availability, which resulted in a different structure prediction process. This thesis presents the techniques and considerations, which should be taken into account in the modeling procedure to overcome limitations and produce a hypothetical and reliable three-dimensional structure. As each project shows, the reliability is highly dependent on the extensive incorporation of experimental data or known literature and, although experimental verification of in silico results is always desirable to increase the reliability, the presented projects show that also the experimental studies can greatly benefit from structural models. With the help of in silico studies, the experiments can be targeted and precisely designed, thereby saving both money and time. As the programs used in structural bioinformatics are constantly improved and the range of templates increases through structural genomics efforts, the mutual benefits between in silico and experimental studies become even more prominent. Hence, reliable models for protein three-dimensional structures achieved through careful planning and thoughtful executions are, and will continue to be, valuable and indispensable sources for structural information to be combined with functional data.

Structural studies on enzymes of biotechnological and biomedical interest

Structural studies of proteins aim at elucidating the atomic details of molecular interactions in biological processes of living organisms. These studies are particularly important in understanding structure, function and evolution of proteins and in defining their roles in complex biological settings. Furthermore, structural studies can be used for the development of novel properties in biomolecules of environmental, industrial and medical importance. X-ray crystallography is an invaluable tool to obtain accurate and precise information about the structure of proteins at the atomic level. Glutathione transferases (GSTs) are amongst the most versatile enzymes in nature. They are able to catalyze a wide variety of conjugation reactions between glutathione (GSH) and non-polar components containing an electrophilic carbon, nitrogen or sulphur atom. Plant GSTs from the Tau class (a poorly characterized class) play an important role in the detoxification of xenobiotics and stress tolerance. Structural studies were performed on a Tau class fluorodifen-inducible glutathione transferase from Glycine max (GmGSTU4-4) complexed with GSH (2.7 Å) and a product analogue Nb-GSH (1.7 Å). The three-dimensional structure of the GmGSTU4-4-GSH complex revealed that GSH binds in different conformations in the two subunits of the dimer: in an ionized form in one subunit and a non-ionized form in the second subunit. Only the ionized form of the substrate may lead to the formation of a catalytically competent complex. Structural comparison between the GSH and Nb-GSH bound complexes revealed significant differences with respect to the hydrogen-bonding, electrostatic interaction pattern, the upper part of -helix H4 and the C-terminus of the enzyme. These differences indicate an intrasubunit modulation between the G-and Hsites suggesting an induced-fit mechanism of xenobiotic substrate binding. A novel binding site on the surface of the enzyme was also revealed. Bacterial type-II L-asparaginases are used in the treatment of haematopoietic diseases such as acute lymphoblastic leukaemia (ALL) and lymphomas due to their ability to catalyze the conversion of L-asparagine to L-aspartate and ammonia. Escherichia coli and Erwinia chrysanthemi asparaginases are employed for the treatment of ALL for over 30 years. However, serious side-effects affecting the liver and pancreas have been observed due to the intrinsic glutaminase activity of the administered enzymes. Structural studies on Helicobacter pylori L-asparaginase (HpA) were carried out in an effort to discover novel L-asparaginases with potential chemotherapeutic utility in ALL treatment. Detailed analysis of the active site geometry revealed structurally significant differences between HpA and other Lasparaginases that may be important for the biological activities of the enzyme and could be further exploited in protein engineering efforts.

Methods for the analysis of two dimensional measurement data of paper web

This work is devoted to the problem of reconstructing the basis weight structure at paper web with black{box techniques. The data that is analyzed comes from a real paper machine and is collected by an o®-line scanner. The principal mathematical tool used in this work is Autoregressive Moving Average (ARMA) modelling. When coupled with the Discrete Fourier Transform (DFT), it gives a very flexible and interesting tool for analyzing properties of the paper web. Both ARMA and DFT are independently used to represent the given signal in a simplified version of our algorithm, but the final goal is to combine the two together. Ljung-Box Q-statistic lack-of-fit test combined with the Root Mean Squared Error coefficient gives a tool to separate significant signals from noise.

Structure learning of context-specific graphical models

Abstract The ultimate problem considered in this thesis is modeling a high-dimensional joint distribution over a set of discrete variables. For this purpose, we consider classes of context-specific graphical models and the main emphasis is on learning the structure of such models from data. Traditional graphical models compactly represent a joint distribution through a factorization justi ed by statements of conditional independence which are encoded by a graph structure. Context-speci c independence is a natural generalization of conditional independence that only holds in a certain context, speci ed by the conditioning variables. We introduce context-speci c generalizations of both Bayesian networks and Markov networks by including statements of context-specific independence which can be encoded as a part of the model structures. For the purpose of learning context-speci c model structures from data, we derive score functions, based on results from Bayesian statistics, by which the plausibility of a structure is assessed. To identify high-scoring structures, we construct stochastic and deterministic search algorithms designed to exploit the structural decomposition of our score functions. Numerical experiments on synthetic and real-world data show that the increased exibility of context-specific structures can more accurately emulate the dependence structure among the variables and thereby improve the predictive accuracy of the models.