Computing the persistent homology of range images with alpha shapes

Laser scanning is becoming an increasingly popular method for measuring 3D objects in industrial design. Laser scanners produce a cloud of 3D points. For CAD software to be able to use such data, however, this point cloud needs to be turned into a vector format. A popular way to do this is to triangulate the assumed surface of the point cloud using alpha shapes. Alpha shapes start from the convex hull of the point cloud and gradually refine it towards the true surface of the object. Often it is nontrivial to decide when to stop this refinement. One criterion for this is to do so when the homology of the object stops changing. This is known as the persistent homology of the object. The goal of this thesis is to develop a way to compute the homology of a given point cloud when processed with alpha shapes, and to infer from it when the persistent homology has been achieved. Practically, the computation of such a characteristic of the target might be applied to power line tower span analysis.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Novel CO2 Regulated Proteins in Synechocystis PCC 6803

The large biodiversity of cyanobacteria together with the increasing genomics and proteomics metadata provide novel information for finding new commercially valuable metabolites. With the advent of global warming, there is growing interest in the processes that results in efficient CO2 capture through the use of photosynthetic microorganisms such as cyanobacteria. This requires a detailed knowledge of how cyanobacteria respond to the ambient CO2. My study was aimed at understanding the changes in the protein profile of the model organism, Synechocystis PCC 6803 towards the varying CO₂ level. In order to achieve this goal I have employed modern proteomics tools such as iTRAQ and DIGE, recombinant DNA techniques to construct different mutants in cyanobacteria and biophysical methods to study the photosynthetic properties. The proteomics study revealed several novel proteins, apart from the well characterized proteins involved in carbon concentrating mechanisms (CCMs), that were upregulated upon shift of the cells from high CO₂ concentration (3%) to that in air level (0.039%). The unknown proteins, Slr0006 and flavodiiron proteins (FDPs) Sll0217-Flv4 and Sll0219-Flv2, were selected for further characterization. Although slr0006 was substantially upregulated under C_i limiting conditions, inactivation of the gene did not result in any visual phenotype under various environmental conditions indicating that this protein is not essential for cell survival. However, quantitative proteomics showed the induction of novel plasmid and chromosome encoded proteins in deltaslr0006 under air level CO₂ conditions. The expression of the slr0006 gene was found to be strictly dependent on active photosynthetic electron transfer. Slr0006 contains conserved dsRNA binding domain that belongs to the Sua5/YrdC/YciO protein family. Structural modelling of Slr0006 showed an alpha/beta twisted open-sheet structure and a positively charged cavity, indicating a possible binding site for RNA. The 3D model and the co-localization of Slr0006 with ribosomal subunits suggest that it might play a role in translation or ribosome biogenesis. On the other hand, deletions in the sll0217-sll218- sll0219 operon resulted in enhanced photodamage of PSII and distorted energy transfer from phycobilisome (PBS) to PSII, suggesting a dynamic photoprotection role of the operon. Constructed homology models also suggest efficient electron transfer in heterodimeric Flv2/Flv4, apparently involved in PSII photoprotection. Both Slr0006 and FDPs exhibited several common features, including negative regulation by NdhR and ambiguous cellular localization when subjected to different concentrations of divalent ions. This strong association with the membranes remained undisturbed even in the presence of detergent or high salt. My finding brings ample information on three novel proteins and their functions towards carbon limitation. Nevertheless, many pathways and related proteins remain unexplored. The comprehensive understanding of the acclimation processes in cyanobacteria towards varying environmental CO₂ levels will help to uncover adaptive mechanisms in other organisms, including higher plants.

Prediction of 3D structure and ligand-binding properties : ABC transporters and flavodiiron proteins from Synechocystis sp. strain PCC 6803

Cyanobacteria are unicellular, non-nitrogen-fixing prokaryotes, which perform photosynthesis similarly as higher plants. The cyanobacterium Synechocystis sp. strain PCC 6803 is used as a model organism in photosynthesis research. My research described herein aims at understanding the function of the photosynthetic machinery and how it responds to changes in the environment. Detailed knowledge of the regulation of photosynthesis in cyanobacteria can be utilized for biotechnological purposes, for example in the harnessing of solar energy for biofuel production. In photosynthesis, iron participates in electron transfer. Here, we focused on iron transport in Synechocystis sp. strain PCC 6803 and particularly on the environmental regulation of the genes encoding the FutA2BC ferric iron transporter, which belongs to the ABC transporter family. A homology model built for the ATP-binding subunit FutC indicates that it has a functional ATPbinding site as well as conserved interactions with the channel-forming subunit FutB in the transporter complex. Polyamines are important for the cell proliferation, differentiation and apoptosis in prokaryotic and eukaryotic cells. In plants, polyamines have special roles in stress response and in plant survival. The polyamine metabolism in cyanobacteria in response to environmental stress is of interest in research on stress tolerance of higher plants. In this thesis, the potd gene encoding an polyamine transporter subunit from Synechocystis sp. strain PCC 6803 was characterized for the first time. A homology model built for PotD protein indicated that it has capability of binding polyamines, with the preference for spermidine. Furthermore, in order to investigate the structural features of the substrate specificity, polyamines were docked into the binding site. Spermidine was positioned very similarly in Synechocystis PotD as in the template structure and had most favorable interactions of the docked polyamines. Based on the homology model, experimental work was conducted, which confirmed the binding preference. Flavodiiron proteins (Flv) are enzymes, which protect the cell against toxicity of oxygen and/or nitric oxide by reduction. In this thesis, we present a novel type of photoprotection mechanism in cyanobacteria by the heterodimer of Flv2/Flv4. The constructed homology model of Flv2/Flv4 suggests a functional heterodimer capable of rapid electron transfer. The unknown protein sll0218, encoded by the flv2-flv4 operon, is assumed to facilitate the interaction of the Flv2/Flv4 heterodimer and energy transfer between the phycobilisome and PSII. Flv2/Flv4 provides an alternative electron transfer pathway and functions as an electron sink in PSII electron transfer.

Formin proteins in normal tissues and cancer

The actin cytoskeleton is a dynamic structure that determines cell shape. Actin turnover is mandatory for migration in normal and malignant cells. In epithelial cancers invasion is frequently accompanied by epithelial to mesenchymal transition (EMT). In EMT, cancer cells acquire a migratory phenotype through transcriptional reprogramming. EMT requires substantial re-organization of actin. During the past decade, new actin regulating proteins have been discovered. Among these are members of the formin family. To study formin expression in tissues and cells, antibodies for detection of formin proteins FMNL1 (Formin-like protein 1), FMNL2 (Formin-like protein 2) and FHOD1 (Formin homology 2 domain containing protein 1) were used. The expression of formins was characterized in normal tissues and selected cancers using immunohistochemistry. The functional roles of formins were studied in cancer cell lines. We found that FMNL2 is widely expressed. It is a filopodial component in cultured melanoma cells. In clinical melanoma, FMNL2 expression has prognostic significance. FHOD1 is a formin expressed in mesenchymal cell types. FHOD1 expression is increased in oral squamous cell carcinoma (SCC) EMT. Importantly, FHOD1 participates in invasion of cultured oral SCC cells. FMNL1 expression is low in normal epithelia, but high in leukocytes and smooth muscle cells. Expression of FMNL1 can be found in carcinoma; we detected FMNL1 expressing cells in basal type of breast cancer. Our results indicate that formins are differentially expressed in normal tissues and that their expression may shift in cancer. Functionally FMNL2 and FHOD1 participate in processes related to cancer progression. Studying formins is increasingly important since they are potential drug targets.

VAP-1 as drug target in inflammation : structural features determining ligand interactions

Vascular adhesion protein-1 (VAP-1), which belongs to the copper amine oxidases (CAOs), is a validated drug target in inflammatory diseases. Inhibition of VAP-1 blocks the leukocyte trafficking to sites of inflammation and alleviates inflammatory reactions. In this study, a novel set of potent pyridazinone inhibitors is presented together with their X-ray structure complexes with VAP-1. The crystal structure of serum VAP-1 (sVAP-1) revealed an imidazole binding site in the active site channel and, analogously, the pyridazinone inhibitors were designed to bind into the channel. This is the first time human VAP-1 has been crystallized with a reversible inhibitor and the structures reveal detailed information of the binding mode on the atomic level. Similarly to some earlier studied inhibitors of human VAP-1, the designed pyridazinone inhibitors bind rodent VAP-1 with a lower affinity than human VAP-1. Therefore, we made homology models of rodent VAP-1 and compared human and rodent enzymes to determine differences that might affect the inhibitor binding. The comparison of the crystal structures of the human VAP-1 and the mouse VAP-1 homology model revealed key differences important for the species specific binding properties. In general, the channel in mouse VAP-1 is more narrow and polar than the channel in human VAP-1, which is wider and more hydrophobic. The differences are located in the channel leading to the active site, as well as, in the entrance to the active site channel. The information obtained from these studies is of great importance for the development and design of drugs blocking the activity of human VAP-1, as rodents are often used for in vivo testing of candidate drugs. In order to gain more insight into the selective binding properties of the different CAOs in one species a comprehensive evolutionary study of mammalian CAOs was performed. We found that CAOs can be classified into sub-families according to the residues X1 and X2 of the Thr/Ser-X1-X2-Asn-Tyr-Asp active site motif. In the phylogenetic tree, CAOs group into diamine oxidase, retina specific amine oxidase and VAP-1/serum amine oxidase clades based on the residue in the position X2. We also found that VAP-1 and SAO can be further differentiated based on the residue in the position X1. This is the first large-scale comparison of CAO sequences, which explains some of the reasons for the unique substrate specificities within the CAO family.