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Cheap electronic storage and Internet bandwidth has increased the amount of online data. Large quantities of metadata are created to manage this wealth of information. Methods to organize and structure metadata has led to the development of ontologies - data that is organized to describe the relation between elements. The creation of large ontologies has brought forth the need for ontology management strategies. Ontology alignment and merging techniques are standard operations for ontology management. Accurate ontology alignment methods are typically semi-automatic, meaning they require periodic user input. This becomes infeasible on large ontologies and the accuracy and efficiency drops significantly when these algorithms are forced to align without human interaction. Bioinformatics, for example, has seen the influx of large ontologies, such as signal pathway sets with thousands of elements or protein-protein interaction (PPI) databases with hundreds of thousands of elements. This drives the need for a reliable method of large-scale ontology alignment. Many bioinformatics ontologies contain references to domain ontologies - manually curated ontologies describing additional, general information about the terms in the ontologies. For example, more than 2/3 of proteins in PPI data sets contain at least one annotation to the domain ontology the Gene Ontology. We use the domain ontology references as features to compute similarity between elements. However, there are few efficient ways to compute similarity from structured features. We present a novel, automatic method for aligning ontologies based on such domain ontology features. Specifically, we use simulated annealing to reduce the complexity of the domain ontologys structure by finding approximate relevant clusters of elements. An intermediate step performs hierarchical clustering based on the similarity between elements of the ontology. Then the mapping between clusters across aligning ontologies is built. The final step builds an alignment between matched clusters. To evaluate our methods, we perform an alignment between Human (Homo Sapiens) and Yeast (Saccharomyces cerevisiae) signal pathways provided by the Reactome database. The results were compared against reliable homology studies of proteins. The final mapping produces alignments that are significantly more accurate than the traditional ontology alignment methods, without any human involvement.

1D protein sequences, 2D contact maps and 3D structures are three different representational levels of detail for proteins. Predicting protein 3D structures from their 1D sequences remains one of the complex challenges of bioinformatics. The "Divide and Conquer" principle is applied in our research to handle this challenge, by dividing it into two separate yet dependent subproblems, using a Case-Based Reasoning (CBR) approach. Firstly, 2D contact maps are predicted from their 1D protein sequences; secondly, 3D protein structures are then predicted from their predicted 2D contact maps. We focus on the problem of identifying common substructural patterns of protein contact maps, which could potentially be used as building blocks for a bottom-up approach for protein structure prediction. We further demonstrate how to improve identifying these patterns by combining both protein sequence and structural information. We assess the consistency and the efficiency of identifying common substructural patterns by conducting statistical analyses on several subsets of the experimental results with different sequence and structural information. / Thesis (Master, Computing) -- Queen's University, 2009-04-23 22:01:04.528

In the field of proteomic mass spectrometry, proteins can be sequenced by two independent yet complementary algorithms: de novo sequencing which uses no prior knowledge and database search which relies upon existing protein databases. In the case where an organism’s protein database is not available, the software Spider was developed in order to search sequence tags produced by de novo sequencing against a database from a related organism while accounting for both errors in the sequence tags and mutations. This thesis further develops Spider by using the concept of reconstruction in order to predict the real sequence by considering both the sequence tags and their matched homologous peptides. The significant value of these reconstructed sequences is demonstrated. Additionally, the runtime is greatly reduced and separated into independent caching and matching steps. This new approach allows for the development of an efficient algorithm for search. In addition, the algorithm’s output can be used for new applications. This is illustrated by a contribution to a complete protein sequencing application.

Cheap electronic storage and Internet bandwidth has increased the amount of online data. Large quantities of metadata are created to manage this wealth of information. Methods to organize and structure metadata has led to the development of ontologies - data that is organized to describe the relation between elements. The creation of large ontologies has brought forth the need for ontology management strategies. Ontology alignment and merging techniques are standard operations for ontology management. Accurate ontology alignment methods are typically semi-automatic, meaning they require periodic user input. This becomes infeasible on large ontologies and the accuracy and efficiency drops significantly when these algorithms are forced to align without human interaction. Bioinformatics, for example, has seen the influx of large ontologies, such as signal pathway sets with thousands of elements or protein-protein interaction (PPI) databases with hundreds of thousands of elements. This drives the need for a reliable method of large-scale ontology alignment. Many bioinformatics ontologies contain references to domain ontologies - manually curated ontologies describing additional, general information about the terms in the ontologies. For example, more than 2/3 of proteins in PPI data sets contain at least one annotation to the domain ontology the Gene Ontology. We use the domain ontology references as features to compute similarity between elements. However, there are few efficient ways to compute similarity from structured features. We present a novel, automatic method for aligning ontologies based on such domain ontology features. Specifically, we use simulated annealing to reduce the complexity of the domain ontologys structure by finding approximate relevant clusters of elements. An intermediate step performs hierarchical clustering based on the similarity between elements of the ontology. Then the mapping between clusters across aligning ontologies is built. The final step builds an alignment between matched clusters. To evaluate our methods, we perform an alignment between Human (Homo Sapiens) and Yeast (Saccharomyces cerevisiae) signal pathways provided by the Reactome database. The results were compared against reliable homology studies of proteins. The final mapping produces alignments that are significantly more accurate than the traditional ontology alignment methods, without any human involvement.

α-actinin-4 is a non-muscle isoform of α-actinin that belongs to the spectrin superfamily. It comprises three functional regions: an N-terminal actin-binding region that consists of two calponin homology (CH) domains, a central region that consists of four copies of the spectrin-like repeat domain and a C-terminal calmodulin-like domain that is predicted to bind Ca²⁺. α-actinin-4 is organised as an antiparallel homodimer formed by the interaction of four spectrin-like repeats between the two monomers, giving a rod-like shape, with actin binding regions at both ends. α-actinin-4 is an abundant actin-bundling protein, which provides a direct link between actin filaments and integrins, and is believed to play an important role in stabilising cell shape and adhesion and regulating cell migration. It also acts as a tumor suppressor and influences the metastatic potential and invasiveness in human cancers. A cluster of three actin binding motifs have been identified in the CH domains (2X CH) from other members of the spectrin superfamily, utrophin and dystrophin. Two of them reside in the CH1 domain and the third resides in the first α-helix of the CH2 domain. In addition, a PIP2 binding site has been mapped on a region adjacent to actin-binding site-3. These observations imply the F-actin binding activity would be regulated by phosphoinositides. Five mutations of α-actinin-4, K122N, an alternative splice variant, K255E, T259I and S263P, have been reported to be involved in three human diseases, non-small lung cancer (NSCLC), small cell lung cancer (SCLC) and focal segmental glomerulosclerosis (FSGS). The mutation site within these mutants is located on the actin binding region. Therefore, the actin binding region is presumed to be associated with the progression of human disease. The aims of this thesis focused on the regulation of the F-actin binding activity of α-actinin-4 by phosphoinositides (PIP2 and PIP3), the calmodulin-like domain and Ca²⁺ , determination of the three-dimensional structure of the CH2 domain in solution and identification of the phosphoinositide binding site on the CH2 domain. In order to investigate the F-actin binding activity quantitatively, a novel in vitro F-actin binding assay (solid phase) was established to replace the semi-quantitative actin bundling assay. Using this novel solid phase F-actin binding assay, Ca²⁺ was shown to enhance the F-actin binding activity of α-actinin-4 in a concentration-dependent manner. The presence of 10 mM Ca²⁺ results in a two-fold increase in the F-actin binding activity. Both PIP2 and PIP3 inhibited the F-actin-binding activity of α-actinin-4 in a concentration-dependent manner with an approximate IC₅₀ of 75 and 45 μM, respectively. In order to characterise how phosphoinositides regulated the F-actin binding activity of α-actinin-4, the solution structure of α-actinin-4 CH2 domain was determined and the phosphoinositide binding residues within the CH2 domain were identified using NMR spectroscopy. The solution structure of α-actinin-4 CH2 domain contained six α-helices and was similar to that of other spectrin superfamily members. The strategy used in identification of the phosphoinositide binding site was an NMR-based 2D ¹H-¹⁵N HSQC ligand titration assay to replace the traditional semi-quantitative protein-lipid overlay assay. Using the NMR-based ligand titration assay, the recognition site for the inositol head group resides in residues Trp 172, Tyr 265 and His 266 and the binding region of acyl chains resides in the first α-helix structure which is one of the putative F-actin binding sites. In order to examine the interaction of phosphoinositides with this site, Y265A and H266E mutants of α-actinin-4 CH2 domain were generated using site-directed mutagenesis and verified the interaction with phosphoinositides and the inositol head group using an NMR-based ligand titration assay. These results confirmed the phosphoinositide binding site on the CH2 domain and residues, Tyr 265 and His 266, are critical for interacting with phosphoinositides. Wildtype and mutants (Y265A and H266E) of α-actinin-4 were expressed in mammalian cells as EGFP-fusion proteins. Wildtype α-actinin-4 was shown to be co-localised with focal adhesions and actin stress fibres. However, Y265A and H266E mutants of α-actinin-4 were co-localised with actin stress fibres but poorly co-localised with focal adhesions. Moreover, both Y265A and H266E mutants of α-actinin-4 were co-localised with actin in the cytoplasm rather than localised along the cell membrane after EGF stimulation for 30 minutes. These results suggested that PIP2 assists the co-localisation of α-actinin-4 with focal adhesions. Taken together, the results described in this thesis concluded that Ca²⁺ enhanced the F-actin binding activity of α-actinin-4 in vitro. However, phosphoinositides (PIP2 or PIP3) inhibited the F-actin binding activity in vitro. Moreover, the results described in this thesis provided a phosphoinositide binding site on α-actinin-4 CH2 domain. Binding to PIP2 is important to the localisation of α-actinin-4 in focal adhesions. / Thesis (Ph.D.) -- University of Adelaide, School of Molecular and Biomedical Science, 2008

Bibliography: leaves 94-108. / xi, 108, [88] leaves, [28] leaves of plates : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The aim of this study is to characterize plastid DNA sequence homologies within higher plant nuclear genomes. It is concluded that integrated within the tobacco nuclear genome are multiple copies of large (ie. in excess of 18 kbp), contigous fracts of plastid DNA. The presence of large tracts of plastid DNA in the tobacco nuclear genome contrasts the arrangement of such sequences in the nuclear genomes of other studied plant species. / Thesis (Ph.D.)--University of Adelaide, Dept. of Genetics, 1993

Thesis (Ph.D.)--McMaster University, 2002. / Adviser: Manfred Kolster. Includes bibliographical references. Also available via World Wide Web.

Thesis (Ph.D.)--McMaster University, 2002. / Adviser: Manfred Kolster. Includes bibliographical references. Also available via World Wide Web.

Thesis (Ph.D.)--McMaster University (Canada), 1994. / Source: Dissertation Abstracts International, Volume: 56-01, Section: B, page: 0272. Director: M. Kolster.