The Interaction of the Calcium Sensitiser Levosimendan with Cardiac Troponin C

Cardiac failure is one of the leading causes of mortality in developed countries. As life expectancies of the populations of these countries grow, the number of patients suffering from cardiac insufficiency also increase. Effective treatments including the use of calcium sensitisers are being sought. They cause a positive inodilatory effect on cardio-myocytes without deleterious effects (arrhythmias) resulting from increases in intracellular calcium concentration. Levosimendan is a novel calcium sensitiser that hasbeen proved to be a welltolerated and effective treatment for patients with severe decompensated heart failure. Cardiac troponin C (cTnC) is its target protein. However, there have been controversies about the interactions between levosimendan and cTnC. Some of these controversies have been addressed in this dissertation. Furthermore, studies on the calcium sensitising mechanism based on the interactions between levosimendan and cTnC as followed by nuclear magnetic resonance(NMR) are presented and discussed. Levosimendan was found to interact with bothdomains of the calcium-saturated cTnC in the absence of cardiac troponin I (cTnI). In the presence of cTnI, the C-domain binding site was blocked and levosimendan interacted only with the regulatory domain of cTnC. This interaction may have caused the observed calcium sensitising effect by priming the N-domain for cTnI binding thereby extending the lifetime of that complex. It is suggested that this is achieved by shifting the equilibrium between open and closed conformations.

A cell spot microarray method for high-throughput biology

High-throughput screening of cellular effects of RNA interference (RNAi) libraries is now being increasingly applied to explore the role of genes in specific cell biological processes and disease states. However, the technology is still limited to specialty laboratories, due to the requirements for robotic infrastructure, access to expensive reagent libraries, expertise in high-throughput screening assay development, standardization, data analysis and applications. In the future, alternative screening platforms will be required to expand functional large-scale experiments to include more RNAi constructs, allow combinatorial loss-of-function analyses (e.g. genegene or gene-drug interaction), gain-of-function screens, multi-parametric phenotypic readouts or comparative analysis of many different cell types. Such comprehensive perturbation of gene networks in cells will require a major increase in the flexibility of the screening platforms, throughput and reduction of costs. As an alternative for the conventional multi-well based high-throughput screening -platforms, here the development of a novel cell spot microarray method for production of high density siRNA reverse transfection arrays is described. The cell spot microarray platform is distinguished from the majority of other transfection cell microarray techniques by the spatially confined array layout that allow highly parallel screening of large-scale RNAi reagent libraries with assays otherwise difficult or not applicable to high-throughput screening. This study depicts the development of the cell spot microarray method along with biological application examples of high-content immunofluorescence and phenotype based cancer cell biological analyses focusing on the regulation of prostate cancer cell growth, maintenance of genomic integrity in breast cancer cells, and functional analysis of integrin protein-protein interactions in situ.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Lipase and omega-transaminase catalysis in preparation of alcohol and amine enantiomers

Enantiopure intermediates are of high value in drug synthesis. Biocatalysis alone or combined with chemical synthesis provides powerful tools to access enantiopure compounds. In biocatalysis, chemo-, regio- and enantioselectivity of enzymes are combined with their inherent environmentally benign nature. Enzymes can be applied in versatile chemical reactions with non-natural substrates under synthesis conditions. Immobilization of an enzyme is a crucial part of an efficient biocatalytic synthesis method. Successful immobilization enhances the catalytic performance of an enzyme and enables its reuse in successive reactions. This thesis demonstrates the feasibility of biocatalysis in the preparation of enantiopure secondary alcohols and primary amines. Viability and synthetic usability of the studied biocatalytic methods have been addressed throughout this thesis. Candida antarctica lipase B (CAL-B) catalyzed enantioselective O-acylation of racemic secondary alcohols was successfully incorporated with in situ racemization in the dynamic kinetic resolution, affording the (R)-esters in high yields and enantiopurities. Side reactions causing decrease in yield and enantiopurity were suppressed. CAL-B was also utilized in the solvent-free kinetic resolution of racemic primary amines. This method produced the enantiomers as (R)-amides and (S)-amines under ambient conditions. An in-house sol-gel entrapment increased the reusability of CAL-B. Arthrobacter sp. omega-transaminase was entrapped in sol-gel matrices to obtain a reusable catalyst for the preparation enantiopure primary amines in an aqueous medium. The obtained heterogeneous omega-transaminase catalyst enabled the enantiomeric enrichment of the racemic amines to their (S)-enantiomers. The synthetic usability of the sol-gel catalyst was demonstrated in five successive preparative kinetic resolutions.

Probing MST1 Kinase Sequence and Structure for Rational Drug Discovery (Targeting diabetes by blocking protein-protein interactions)

Apoptotic beta cell death is an underlying cause majorly for type I and to a lesser extent for type II diabetes. Recently, MST1 kinase was identified as a key apoptotic agent in diabetic condition. In this study, I have examined MST1 and closely related kinases namely, MST2, MST3 and MST4, aiming to tackle diabetes by exploring ways to selectively block MST1 kinase activity. The first investigation was directed towards evaluating possibilities of selectively blocking the ATP binding site of MST1 kinase that is essential for the activity of the enzymes. Structure and sequence analyses of this site however revealed a near absolute conservation between the MSTs and very few changes with other kinases. The observed residue variations also displayed similar physicochemical properties making it hard for selective inhibition of the enzyme. Second, possibilities for allosteric inhibition of the enzyme were evaluated. Analysis of the recognized allosteric site also posed the same problem as the MSTs shared almost all of the same residues. The third analysis was made on the SARAH domain, which is required for the dimerization and activation of MST1 and MST2 kinases. MST3 and MST4 lack this domain, hence selectivity against these two kinases can be achieved. Other proteins with SARAH domains such as the RASSF proteins were also examined. Their interaction with the MST1 SARAH domain were evaluated to mimic their binding pattern and design a peptide inhibitor that interferes with MST1 SARAH dimerization. In molecular simulations the RASSF5 SARAH domain was shown to strongly interact with the MST1 SARAH domain and possibly preventing MST1 SARAH dimerization. Based on this, the peptidic inhibitor was suggested to be based on the sequence of RASSF5 SARAH domain. Since the MST2 kinase also interacts with RASSF5 SARAH domain, absolute selectivity might not be achieved.