Barnase as a model for the denatured state of proteins

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaf 19).Protein folding is one of the major thrusts of biochemical research today. It is the study of how proteins fold into their tertiary structure based solely on their sequence of amino acids. It was once believed that no interactions were made in the denatured state and it contributes to the stability of the native state. It has become apparent that new techniques are needed to probe the denatured state and learn about its properties. It was proposed to use chemical modification of cysteine mutant of Barnase to disrupt the hydrophobic core and denature the protein under physiological conditions. Then a series of size exclusion chromatography experiments would then be carried out to characterize the extent to which the protein unfolds. These experiments would be done in increasing osmolyte concentration, which will cause an open conformation to become more compact while an already compact conformation will change very little. Unfortunately, high Barnase yields could not be obtained with the first method of preparation and work was delayed significantly while a new method was investigated. A new expression vector and protocol was obtained from Bob Hartley at the NIG and implemented successfully two weeks ago. Work is ongoing now that problems with the expression system have been solved

Supramolecular complexes containing pyridine N-oxides.

In the first section, the coordination chemistry of a new, divergent, N,O ligand was investigated. Several metal complexes of 4,4 \u27-bipyridine N-monoxide, 2a, were characterised by X-ray crystallography, including CuI, Cu II, PdII, CdII, HgII, and EuIII. The observed non-covalent interactions and coordinative preferences of 2a were contrasted with those of 4,4\u27 -bipyridine and 4,4\u27-bipyridine N,N \u27-dioxide. Each of the compounds has a unique molecular topology that can be applied to the generation of ordered, pre-designed solids. Examples of this methodology are given with respect to the CuI, Cu II, and HgII metal complexes. Chapter three is concerned with the design and synthesis of multi-dimensional, polyrotaxane architectures. A new [2]pseudorotaxane, 3b ⊂ DB24C8, was used as a divergent ligand to connect metal nodes into extended coordination frameworks. Three distinct polyrotaxane networks were synthesised, one two-dimensional net containing CdII in which one-dimensional polyrotaxane strands are pillared in the second dimension by 3b. Two different three-dimensional topologies were generated using five different lanthanide cations. Structures containing SmIII, Eu III, GdIII, TbIII are isomorphous and adopt an alpha-polonium type lattice. The slightly smaller lanthanide, Yb III, generates the previously unreported 3 4,6 6 three-dimensional net. The final chapter describes the integration of an electrostatic component into the formation of [2]pseudorotaxanes. A derivative of DB24C8, 4d , was synthesised in which there are pendant-SO3 - groups on each benzo ring. The association constants of several threads were measured with 4d. It was shown that through the introduction of an electrostatic contribution to the recognition process that [2]pseudorotaxanes could be formed in a competitive solvent such as acetic acid. The X-ray crystal structure of 3b ⊂ 4d is presented and confirms the interpenetrated nature of the [2]pseudorotaxane.Dept. of Chemistry and Biochemistry. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .H64. Source: Masters Abstracts International, Volume: 43-03, page: 0851. Adviser: S. J. Loeb. Thesis (M.Sc.)--University of Windsor (Canada), 2004

KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition

KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them

Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP Symposium On Applied Computing (SAC 2019