Telomerase is a highly regulated ribonucleoprotein complex that stabilizes eukaryotic genomes by replenishing telomeric repeats on chromosome ends. Defects in telomerase RNP components involving the catalytic subunit TERT or the RNA template TER lead to stem cell-related diseases such as dyskeratosis congenita and idiopathic pulmonary fibrosis, while inappropriate telomerase expression is a rate-limiting step in carcinogenesis. In this study we report the discovery of a novel negative regulatory mechanism for telomerase that stems from duplication and diversification of key components of the telomerase RNP in the flowering plant Arabidopsis thaliana. We show that Arabidopsis encodes three distinct TERs: TER1, TER2 and a processed form of TER2 termed TER2S. Although all three RNAs can serve as templates for telomerase in vitro, in vivo they have different expression patterns, assemble into distinct RNPs with different protein binding partners, and play opposing roles in telomere maintenance. The TER1 RNP is analogous to the telomerase enzyme previously described in other eukaryotes, but the TER2 RNP is a negative regulator of telomerase activity and telomere maintenance in vivo. Furthermore, we demonstrate that the Protection Of Telomeres (POT1) paralogs in Arabidopsis (POT1a, POT1b and POT1c) are novel TER binding proteins. This finding is striking because in yeast and vertebrates, POT1 is an essential component of the telomere capping complex and functions to distinguish the chromosome terminus from a double-strand break. Thus, our data argue that Arabidopsis POT1 proteins have migrated off of the chromosome terminus and onto the telomerase RNP, indicating that duplication and diversification of Arabidopsis telomerase may be the end result of the co-evolution of the TER and POT1 RNP components. Additionally, given the dire consequences of misregulating telomerase in human cells, our discovery of a novel negative regulatory mechanism for telomerase in plants strongly suggests that additional modes of telomerase control remain to be elucidated in vertebrates.

The protein fold recognition problem is one of the important topics in biophysics. It is believed that the primary structure of a protein is helpful to drawing its three-dimensional (3D) structure. Given a target protein (a sequence of amino acids), the protein fold recognition problem is to decide which fold group of some protein structure database the target protein belongs to. Since more than two fold groups are to be located in this problem, it is a multi-class classification problem. Recently, many researchers have solved this problem by using the popular machine learning tools, such as neural networks (NN) and support vector machines (SVM). In this thesis, we use the SVM tool to solve this problem. Our strategy is to find out the effective features which can be used as an efficient guide to the classification problem. We build the feature preference table to help us to find out effective feature combinations quickly. We take 27 well-known fold groups in SCOP (Structural Classification of Proteins) as our data set. Our experimental results show that our method achieves the overall prediction accuracy of 61.4%, which is better than the previous method (56.5%). With the same feature combinations, our prediction accuracy is also higher than the previous results. These results show that our method is indeed effective for the fold recognition problem.

Natural proteins can evolve new three-dimensional structures through mutations in their amino acid sequence. For protein families that exhibit such structural diversity, a major challenge is to understand the scope and nature of structure variation and its relationship to sequence evolution. The Cordes laboratory has begun using transitive homology-based methods to identify, target and structurally characterize natural sequences intermediate between pairs of proteins with distant sequence similarity and different structures. As a proof of principle, this dissertation describes structural studies of two proteins in different families as separate case studies, one involving secondary structure evolution and the other involving topological rearrangement. In the first case, crystallography was applied to solve the structure of a sequence intermediate identified through transitive homology analysis of the Cro transcription regulator family. Comparison with another member resulted in finding two proteins with significant sequence similarity yet different secondary structure compositions and folds. In the second case, transitive homology analysis was applied to look at two members of the insect salivary lipocalins, one with the canonical sequential all-antiparallel β-barrel topology, and another with a unique strand-swapped topology. Three sequence intermediate members were found that each have direct sequence similarity to both topologically distinct relatives. Targeting these sequence intermediate members for structural characterization by NMR led to assignment of the canonical lipocalin topology for one intermediate. The results from these two cases indicate that structurally diverse families may contain members with similar sequences but different folds. As such, transitive homology mapping offers a method to identify and target those members for structural characterization.

The Livingstone Range anticlinorium (LRA) is a long (>65 km) narrow (<5 km) structural culmination that coincides with a major hanging-wall ramp across which the Livingstone thrust cuts ~1000 m up-section eastward from a regional décollement in the upper part of Devonian Palliser Formation to another regional décollement within Jurassic Fernie Formation. The presence of Precambrian basement fluids prior to thrusting and folding is recorded in the LRA by deformed jasper+/-fluorite+/-sphalerite veins, and adjacent haloes of altered dolomitic host rock with high 87Sr/86Sr ratios (0.7094 to 0.7100) relative to most host rocks. Basement fluids are a possible source for anomalously radiogenic strontium that occurs in the diagenetically altered Paleozoic carbonate rocks in the LRA and throughout the Western Canada Sedimentary Basin, but underlying thick shale strata such as the Exshaw Formation are also a possible source. The earliest stages of thrusting deformation involved the development of distinctive chevron-style, flexural-slip thrust-propagation folds that have conspicuous blind thrust faults along their hinge zones. The hinge-zone thrust system of the Centre Peak anticline consists of a series of stacked detachment thrusts, each of which emerges from a different zone of interbed slip in the backlimb of the anticline and deflects the hinge zone eastward. Each successively higher detachment thrust dies out in the hinge zone at approximately the same stratigraphic level at which an overlying detachment thrust fault emerges from a bedding detachment zone in the backlimb. Fluid flow during thrust-propagation folding is recorded by dolomite+/-calcite veins with isotopic compositions that are similar to those of host rocks. Fluid flow occurred along faults related to thrust-propagation folding, and also along many tear faults and larger thrust faults. Veins in these fault zones have slightly higher 87Sr/86Sr ratios relative to adjacent host rocks and are interpreted to have formed from a mixture of formation fluids and hotter basement fluids in a rock-dominated system. Oxygen isotope thermometry of four syn-folding veins indicates they precipitated at anomalously high temperatures (>250°C). The youngest episode of fluid flow along thrust faults and tear faults is recorded by calcite veins with very low δ18O values (-18 to -9‰ PDB), which are interpreted to have precipitated along faults that were active while the LRA was being transported eastward and elevated by underlying thrust faults, and cooled by infiltrating meteoric water. The conduits along which significant meteoric fluid circulation occurred are marked by visibly altered host rocks that have anomalously low δ18O values and slightly lower δ13C values relative to most host rocks. Rapid cooling due to deep infiltration of meteoric water into the shallow brittle surface of the deforming earth is almost certainly not restricted to thrust and fold belts, nor is its thermal effect necessarily restricted to the upper few kilometers. This model for fluid flow has significant implications for predicting thermal conditions in deep metamorphic rocks that lie beneath the brittle crust, the most obvious effect being to push down the brittle/ductile transition zone, which would enhance even deeper meteoric fluid circulation and cause the deflection of underlying isotherms. / Thesis (Ph.D, Geological Sciences & Geological Engineering) -- Queen's University, 2007-11-01 15:25:23.854

Thesis (M. Sc.)--School of Geosciences, Faculty of Science, University of Sydney, 2004. / Bibliography: leaves 80-87.

Thesis (M. Sc.)--University of Sydney, 2004. / Title from title screen (viewed 14 May 2008). Submitted in fulfilment of the requirements for the degree of Master of Science to the School of Geosciences, Faculty of Science. Includes bibliographical references. Also available in print form.

Protein structure prediction is one of the most important and difficult problems in computational molecular biology. Unlike sequence-only comparison, protein fold recognition based on machine learning algorithms attempts to detect similarities between protein structures which might not be accompanied with any significant sequence similarity. It takes advantage of the information from structural and physic properties beyond sequence information. In this thesis, we present a novel classifier on protein fold recognition, using AdaBoost algorithm that hybrids to k Nearest Neighbor classifier. The experiment framework consists of two tasks: (i) carry out cross validation within the training dataset, and (ii) test on unseen validation dataset, in which 90% of the proteins have less than 25% sequence identity in training samples. Our result yields 64.7% successful rate in classifying independent validation dataset into 27 types of protein folds. Our experiments on the task of protein folding recognition prove the merit of this approach, as it shows that AdaBoost strategy coupling with weak learning classifiers lead to improved and robust performance of 64.7% accuracy versus 61.2% accuracy in published literatures using identical sample sets, feature representation, and class labels.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24504号 / 医博第4946号 / 新制||医||1064(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 溝脇 尚志, 教授 浅野 雅秀, 教授 鈴木 実 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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