Investigation of sequence features of hinge-bending regions in proteins with domain movements using kernel logistic regression

Background: Hinge-bending movements in proteins comprising two or more domains form a large class of functional movements. Hinge-bending regions demarcate protein domains and collectively control the domain movement. Consequently, the ability to recognise sequence features of hinge-bending regions and to be able to predict them from sequence alone would benefit various areas of protein research. For example, an understanding of how the sequence features of these regions relate to dynamic properties in multi-domain proteins would aid in the rational design of linkers in therapeutic fusion proteins. Results: The DynDom database of protein domain movements comprises sequences annotated to indicate whether the amino acid residue is located within a hinge-bending region or within an intradomain region. Using statistical methods and Kernel Logistic Regression (KLR) models, this data was used to determine sequence features that favour or disfavour hinge-bending regions. This is a difficult classification problem as the number of negative cases (intradomain residues) is much larger than the number of positive cases (hinge residues). The statistical methods and the KLR models both show that cysteine has the lowest propensity for hinge-bending regions and proline has the highest, even though it is the most rigid amino acid. As hinge-bending regions have been previously shown to occur frequently at the terminal regions of the secondary structures, the propensity for proline at these regions is likely due to its tendency to break secondary structures. The KLR models also indicate that isoleucine may act as a domain-capping residue. We have found that a quadratic KLR model outperforms a linear KLR model and that improvement in performance occurs up to very long window lengths (eighty residues) indicating long-range correlations. Conclusion: In contrast to the only other approach that focused solely on interdomain hinge-bending regions, the method provides a modest and statistically significant improvement over a random classifier. An explanation of the KLR results is that in the prediction of hinge-bending regions a long-range correlation is at play between a small number amino acids that either favour or disfavour hinge-bending regions. The resulting sequence-based prediction tool, HingeSeek, is available to run through a webserver at hingeseek.cmp.uea.ac.uk

The Early Modern Colonial State in Asia: Private Agency and Family Networks in the English East India Company

This thesis studies the formation of the early modern colonial state in Asia. Through an exploration of the English East India Company, it examines the dynamics which shaped political authority, colonial governance and the performance of state power in the seventeenth and eighteenth centuries. Specifically, the following research argues that a process of political decentralisation took place within the Company. This was driven by the pursuit of ‘private interests’ on behalf of the Company’s servants in Asia, who, as a result neglected, resisted or subverted the ‘public interests’ of their masters in London. Key to this reconfiguration of power were the family networks established by Company servants between Europe and Asia, and across Asia itself in this period. As constructs of exchange, circulation and movement, family networks allowed Company servants to exercise considerable political agency, distinct from metropolitan authorities. In so doing, they transformed the political landscape around them, laying the foundations of the early modern colonial state through a process of private state formation from the turn of the eighteenth century onwards

The motivation of junior high school pupils to learn English in provincial Indonesia

The purpose of this work is to explore the motivation of young Indonesians to learn English over the first two years of formal study in a provincial junior high school. The national education system has always struggled to produce competent users of the language, yet the country's need for such graduates is never greater than at the beginning of the 21" century as it responds to the social, economic and political challenges of globalization. Meanwhile motivation has always been recognised as an important factor in language learning success, but recent work has stressed its complexity and changeability over time and in particular contexts, encouraging the possibility of new discoveries in this academically unexplored territory. Defining motivation as a dynamic constellation of contextually sensitive cognitions and affects stimulating individuals to learn, the study adopted a mixed method strategy, using questionnaires at beginning and end of the 20-month research period to track motivational trends across the whole school year group (n = 195) and developing indepth portraits of 12 individuals through interview and classroom observation at three points. The eight school English teachers were also interviewed at the beginning. Results showed a very high level of motivation to learn English, reflected in much autonomous learning of the language outside of school. Although there was evidence of dissatisfaction with aspects of school English lessons, this motivation was largely sustained throughout the period under study and appeared to contribute to significant gains in competence in the language among some learners. It is argued that this motivation derives its strength from identification processes, nurtured and developed through social interaction at home and in the community, which encouraged many young Indonesians in this context to view English as integral to their future lives. 'Me study strongly suggests that understanding differences in the way learners identify with the language is an important direction for future research into L2 motivation in general. Understanding how schools and teachers promote or challenge pupils' L2 identities could lead to improvements in language pedagog

Methodological improvements for the analysis of domain movements in large biomolecular complexes

Domain movements play a prominent role in the function of many biomolecules such as the ribosome and F0F1-ATP synthase. As more structures of large biomolecules in different functional states become available as experimental techniques for structure determination advance, there is a need to develop methods to understand the conformational changes that occur. DynDom and DynDom3D were developed to analyse two structures of a biomolecule for domain movements. They both used an original method for domain recognition based on clustering of “rotation vectors”. Here we introduce significant improvements in both the methodology and implementation of a tool for the analysis of domain movements in large multimeric biomolecules. The main improvement is in the recognition of domains by using all six degrees of freedom required to describe the movement of a rigid body. This is achieved by way of Chasles’ theorem in which a rigid-body movement can be described as a screw movement about a unique axis. Thus clustering now includes, in addition to rotation vector data, screw-axis location data and axial climb data. This improves both the sensitivity of domain recognition and performance. A further improvement is the recognition and annotation of interdomain bending regions, something not done for multimeric biomolecules in DynDom3D. This is significant as it is these regions that collectively control the domain movement. The new stand-alone, platform-independent implementation, DynDom6D, can analyse biomolecules comprising protein, DNA and RNA, and employs an alignment method to automatically achieve the required equivalence of atoms in the two structures

Improved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias

Protein-protein interactions drive many biological processes, including the detection of phytopathogens by plants' R-Proteins and cell surface receptors. Many machine learning studies have attempted to predict protein-protein interactions but performance is highly dependent on training data; models have been shown to accurately predict interactions when the proteins involved are included in the training data, but achieve consistently poorer results when applied to previously unseen proteins. In addition, models that are trained using proteins that take part in multiple interactions can suffer from representation bias, where predictions are driven not by learned biological features but by learning of the structure of the interaction dataset. We present a method for extracting unique pairs from an interaction dataset, generating non-redundant paired data for unbiased machine learning. After applying the method to datasets containing _Arabidopsis thaliana_ and pathogen effector interations, we developed a convolutional neural network model capable of learning and predicting interactions from Chaos Game Representations of proteins' coding genes

CRISPR/Cas9-mediated gene manipulation to create single-amino-acid-substituted and floxed mice with a cloning-free method.

Clustered regulatory interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technology is a powerful tool to manipulate the genome with extraordinary simplicity and speed. To generate genetically modified animals, CRISPR/Cas9-mediated genome editing is typically accomplished by microinjection of a mixture of Cas9 DNA/mRNA and single-guide RNA (sgRNA) into zygotes. However, sgRNAs used for this approach require manipulation via molecular cloning as well as in vitro transcription. Beyond these complexities, most mutants obtained with this traditional approach are genetically mosaic, yielding several types of cells with different genetic mutations. Recently, a growing body of studies has utilized commercially available Cas9 protein together with sgRNA and a targeting construct to introduce desired mutations. Here, we report a cloning-free method to target the mouse genome by pronuclear injection of a commercial Cas9 protein:crRNA:tracrRNA:single-strand oligodeoxynucleotide (ssODN) complex into mouse zygotes. As illustration of this method, we report the successful generation of global gene-knockout, single-amino-acid-substituted, as well as floxed mice that can be used for conditional gene-targeting. These models were produced with high efficiency to generate non-mosaic mutant mice with a high germline transmission rate

Applications of machine learning to problems in biomolecular function and dynamics

Biomolecules such as proteins and nucleic acids are involved in all biological processes. As they take part in these processes, biomolecules often undergo motions and changes in their conformation that are related to their function. This thesis presents research into and development of methods to support the study of the dynamics of these changes and their relationship to the biomolecular function. Due to the scale of the structures and speed of the changes, common methods of determining (or “solving”) the structures of biomolecules cannot capture the change in conformation. Detail of the changes must be extrapolated from changes observed between multiple solved states of the same structure. We present a novel method of visualising potential motions of atoms comprising biomolecules, estimated from solved structures at the start and end of the trajectory. Comparisons show that our method produces atomic coordinates that pass closer to known intermediates than those produced by similar existing methods. Our visualisations treat each atom as an individual body, but the conformational changes of proteins can be broken down into the motions of “dynamic domains”, which are sections of proteins that move semi-rigidly, controlled by flexible hinge bending regions. Tools such as the DynDom program identify and analyse the motions of these dynamic domains displayed between pairs of solved structures. We designed and developed DynDom6D, a new version of the DynDom program for very large macromolecules that assigns atoms to domains or hinge bending regions using 6-dimensional k-means clustering

The Intersection of Materialitiy and Mattering: Acrylic Paint and Montreal Hard-Edge Painting

Set in Montreal with the advent of Hard-Edge painting and the invention of acrylic paint, this thesis takes a critical look at the relationship between materiality and mattering. The Hard-Edge painters, Guido Molinari, Claude Tousignant and Yves Gaucher undertook a new medium to express their new way of seeing and encountering the world and they did so in a way that was unique from other contemporaneous abstract painters. And that new medium, acrylic paint, contributed to their vision and capability to do so. Examining Hard-Edge paint through the vantage of materiality and materialist frameworks, and considering the role of acrylic paint on a new level, takes advantage of the opportunity to confront the presumption of overt statements of authorial completion and destabilizes the assumption of their statement being solely artist-determined. Unlike some contemporary art, whose materiality is more obviously fluid, Hard-Edge paintings look like they are stable, but they are not. If object-stability is demonstrated to be untrue of Hard-Edge painting, then we have an opportunity to examine the entirety of art’s history through a new lens. This thesis proposes a new way of understanding the relationship between making, material, epistemology and ontology and proposes that when we understand Hard-Edge painting as object-fluid, we then understand the breadth of art history as a mattering whose “objects” play less of a primary role and whose materiality is instrumental. If making cannot be defined by the ideation of the artist then neither can the “object” that is made. Examining the influence of acrylic paint over this one instance of making in Montreal in the late modern period contributes to an understanding of making and continued mattering that has consequences for Hard-Edge painting, which matters to the late modern epoch of a city and its people and their aspirations, and also to how we classify, sort, narrate, and perpetuate our art. If we continue to put Hard-Edge painting into a finite category we undermine what it is capable of doing now. This thesis highlights the consequence of understanding artworks as the products of ideas and thus materially (object-) stable. I demonstrate that they are object-fluid and that this understanding will help us understand art’s history differently and be better art stewards

Mechanical properties of BeO-(UTh)O2 dispersion fuels.

Dispersions of (UTh)02 in BeO were fabricated using (UTh)02 particles made from mixed powders of U02 and Th02 or by co-precipitation of U02 and Th02. The behaviour of the dispersions was studied by measuring the modulus of rupture and Young's modulus. Modulus of rupture measurements were made by four-point bending of cylindrical specimens, and Young's modulus was calculated from the deflection of rectangular beams. The out-of-pile control specimens for two irradiation experiments, X121 and X105, were also tested. Examination of matching fracture faces showed that cracks passed through (UTh)02 particles, indicating that they were bonded to the matrix. The results were compared with previous results on BeO-Th02 dispersions. The strength of dispersions of (UTh)02 in BeO decreased with increasing (UTh)02 particle size and increasing (UTh)02 concentration. For dispersions of 1.7 v/o (UTh)02 made from mixed oxides in BeO, the strength increased with increasing temperature while for dispersions of (UTh)02 made by co-precipitation, the strength remained unchanged with temperature up to 500ºC and decreased as the temperature was raised to 1000ºC, Young's modulus decreased with increasing (UTh)02 concentration. Young's modulus of 1.7 v/o (UTh)Oa dispersions remained unchanged up to about 600ºC} then decreased slightly as the temperature was raised to 1000ºC, Increasing the (UTnJOa concentration from 1.7 to 20 v/o displaced the curve to lower values. In general, the behaviour of dispersions of (UTh)02 in BeO was similar to that of ThOa in BeO. The differences in behaviour between dispersions of (UTh)Oa made from mixed oxides and by co-precipitation, observed in modulus of rupture tests, were attributed to variations in matrix grain size caused by iron cexamination of the co-precipitated (UThJOg. It was concluded that to obtain maximum strength, the particle size of the (UThJOg should not exceed 5jj., and the particles should contain no impurity which could cause grain growth in the beryllia matrix