In proteomic studies, separate experimental protocols have been necessary to identify proteins, determine their function, and predict their three-dimensional structure. In this study, a function-based separation of proteins was conceived to fractionate proteins prior to enzymatic digestion. In the initial demonstration of this technique, a DNA substrate was used to separate the DNA-binding proteins from the rest of the proteins in a lysate in order to identify protein function and to simplify the complex mixture of proteins. A total of 232 putative DNA-binding proteins and over 540 proteins in all were identified from E. coli. Hypothetical or unknown proteins were found, some of which bind to DNA. As a part of this demonstration, changes in protein expression caused by different environmental conditions (aerobic and anaerobic atmospheres) were observed. In a second demonstration, aimed at determining the three-dimensional structure of the DNA binding proteins, binding sites were blocked with oligonucleotides, and the modified proteins were purified, enzymatically digested, and subjected to tandem mass spectrometry. The amino acids in the DNA-binding domains of three proteins were determined. In a final application of function-based separation, DNA-binding proteins were digested with trypsin and the resulting peptides were separated using HPLC and subsequently analyzed using MALDI TOF/TOF and ESI Q-TOF instruments to study the complementary nature of the two ionization techniques, taking into account the differences between the mass analyzers. Based on the analysis of a large data set containing hundreds of peptides and thousands of individual amino acids, some of the currently held notions regarding the ionization processes were confirmed. ESI tends to favor the analysis of hydrophobic amino acids and peptides while MALDI is disposed toward mainly basic and aromatic species. These tendencies in ionization account in large part for the complementary nature of the peptides and proteins identified by the ESI and MALDI instruments and make it necessary to employ both types of instruments to gain the most information out of a given sample in a proteomics study. / Graduation date: 2004

In an attempt to elucidate the transcriptional mechanisms that regulate the expression of the human α2(I) procollagen gene, cis-acting DNA-elements within the proximal promoter were identified and their corresponding trans-acting factors characterised. The fibroblast cell lines used in this study had previously been transformed with either simian virus 40 (SVWI-38) or by γ-radiation (CT-1). The SVWI-38 fibroblasts do not produce any α2(I) collagen chains, whereas the CT-1 cell line produces normal type I collagen. Previous studies suggested that trans-acting factor(s) may be responsible for the inactivation of the α2(I) procollagen gene in SVWI-38 fibroblasts (Parker et. al. (1989) J. Biol. Chem 264, 7147-7152; Parker et. al. (1992) Nucleic Acids Res. 20, 5825-5830). In this study, the SVWI-38 proximal promoter (-350 to +54) was sequenced and shown to be normal, thereby ruling out any possibility that mutations within this region was responsible for inactivation of the gene.

Wong, Ka Chun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 109-121). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgements --- p.iv / List of Figures --- p.ix / List of Tables --- p.xi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Objective --- p.2 / Chapter 1.3 --- Methodology --- p.2 / Chapter 1.4 --- Bioinforrnatics --- p.2 / Chapter 1.5 --- Computational Methods --- p.3 / Chapter 1.5.1 --- Evolutionary Algorithms --- p.3 / Chapter 1.5.2 --- Data Mining for TF-TFBS bindings --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Gene Transcription --- p.5 / Chapter 2.1.1 --- Protein-DNA Binding --- p.6 / Chapter 2.1.2 --- Existing Methods --- p.6 / Chapter 2.1.3 --- Related Databases --- p.8 / Chapter 2.1.3.1 --- TRANSFAC - Experimentally Determined Database --- p.8 / Chapter 2.1.3.2 --- cisRED - Computational Determined Database --- p.9 / Chapter 2.1.3.3 --- ORegAnno - Community Driven Database --- p.10 / Chapter 2.2 --- Evolutionary Algorithms --- p.13 / Chapter 2.2.1 --- Representation --- p.15 / Chapter 2.2.2 --- Parent Selection --- p.16 / Chapter 2.2.3 --- Crossover Operators --- p.17 / Chapter 2.2.4 --- Mutation Operators --- p.18 / Chapter 2.2.5 --- Survival Selection --- p.19 / Chapter 2.2.6 --- Termination Condition --- p.19 / Chapter 2.2.7 --- Discussion --- p.19 / Chapter 2.2.8 --- Examples --- p.19 / Chapter 2.2.8.1 --- Genetic Algorithm --- p.20 / Chapter 2.2.8.2 --- Genetic Programming --- p.21 / Chapter 2.2.8.3 --- Differential Evolution --- p.21 / Chapter 2.2.8.4 --- Evolution Strategy --- p.22 / Chapter 2.2.8.5 --- Swarm Intelligence --- p.23 / Chapter 2.3 --- Association Rule Mining --- p.24 / Chapter 2.3.1 --- Objective --- p.24 / Chapter 2.3.2 --- Apriori Algorithm --- p.24 / Chapter 2.3.3 --- Partition Algorithm --- p.25 / Chapter 2.3.4 --- DHP --- p.25 / Chapter 2.3.5 --- Sampling --- p.25 / Chapter 2.3.6 --- Frequent Pattern Tree --- p.26 / Chapter 3 --- Discovering Protein-DNA Binding Sequence Patterns Using Associa- tion Rule Mining --- p.27 / Chapter 3.1 --- Materials and Methods --- p.28 / Chapter 3.1.1 --- Association Rule Mining and Apriori Algorithm --- p.29 / Chapter 3.1.2 --- Discovering associated TF-TFBS sequence patterns --- p.29 / Chapter 3.1.3 --- "Data, Preparation" --- p.31 / Chapter 3.2 --- Results and Analysis --- p.34 / Chapter 3.2.1 --- Rules Discovered --- p.34 / Chapter 3.2.2 --- Quantitative Analysis --- p.36 / Chapter 3.2.3 --- Annotation Analysis --- p.37 / Chapter 3.2.4 --- Empirical Analysis --- p.37 / Chapter 3.2.5 --- Experimental Analysis --- p.38 / Chapter 3.3 --- Verifications --- p.41 / Chapter 3.3.1 --- Verification by PDB --- p.41 / Chapter 3.3.2 --- Verification by Homology Modeling --- p.45 / Chapter 3.3.3 --- Verification by Random Analysis --- p.45 / Chapter 3.4 --- Discussion --- p.49 / Chapter 4 --- Designing Evolutionary Algorithms for Multimodal Optimization --- p.50 / Chapter 4.1 --- Introduction --- p.50 / Chapter 4.2 --- Problem Definition --- p.51 / Chapter 4.2.1 --- Minimization --- p.51 / Chapter 4.2.2 --- Maximization --- p.51 / Chapter 4.3 --- An Evolutionary Algorithm with Species-specific Explosion for Multi- modal Optimization --- p.52 / Chapter 4.3.1 --- Background --- p.52 / Chapter 4.3.1.1 --- Species Conserving Genetic Algorithm --- p.52 / Chapter 4.3.2 --- Evolutionary Algorithm with Species-specific Explosion --- p.53 / Chapter 4.3.2.1 --- Species Identification --- p.53 / Chapter 4.3.2.2 --- Species Seed Delta Evaluation --- p.55 / Chapter 4.3.2.3 --- Stage Switching Condition --- p.56 / Chapter 4.3.2.4 --- Species-specific Explosion --- p.57 / Chapter 4.3.2.5 --- Calculate Explosion Weights --- p.59 / Chapter 4.3.3 --- Experiments --- p.59 / Chapter 4.3.3.1 --- Performance measurement --- p.60 / Chapter 4.3.3.2 --- Parameter settings --- p.61 / Chapter 4.3.3.3 --- Results --- p.61 / Chapter 4.3.4 --- Conclusion --- p.62 / Chapter 4.4 --- A. Crowding Genetic. Algorithm with Spatial Locality for Multimodal Op- timization --- p.64 / Chapter 4.4.1 --- Background --- p.64 / Chapter 4.4.1.1 --- Crowding Genetic Algorithm --- p.64 / Chapter 4.4.1.2 --- Locality of Reference --- p.64 / Chapter 4.4.2 --- Crowding Genetic Algorithm with Spatial Locality --- p.65 / Chapter 4.4.2.1 --- Motivation --- p.65 / Chapter 4.4.2.2 --- Offspring generation with spatial locality --- p.65 / Chapter 4.4.3 --- Experiments --- p.67 / Chapter 4.4.3.1 --- Performance measurements --- p.67 / Chapter 4.4.3.2 --- Parameter setting --- p.68 / Chapter 4.4.3.3 --- Results --- p.68 / Chapter 4.4.4 --- Conclusion --- p.68 / Chapter 5 --- Generalizing Protein-DNA Binding Sequence Representations and Learn- ing using an Evolutionary Algorithm for Multimodal Optimization --- p.70 / Chapter 5.1 --- Introduction and Background --- p.70 / Chapter 5.2 --- Problem Definition --- p.72 / Chapter 5.3 --- Crowding Genetic Algorithm with Spatial Locality --- p.72 / Chapter 5.3.1 --- Representation --- p.72 / Chapter 5.3.2 --- Crossover Operators --- p.73 / Chapter 5.3.3 --- Mutation Operators --- p.73 / Chapter 5.3.4 --- Fitness Function --- p.74 / Chapter 5.3.5 --- Distance Metric --- p.76 / Chapter 5.4 --- Experiments --- p.77 / Chapter 5.4.1 --- Parameter Setting --- p.77 / Chapter 5.4.2 --- Search Space Estimation --- p.78 / Chapter 5.4.3 --- Experimental Procedure --- p.78 / Chapter 5.4.4 --- Results and Analysis --- p.79 / Chapter 5.4.4.1 --- Generalization Analysis --- p.79 / Chapter 5.4.4.2 --- Verification By PDB --- p.86 / Chapter 5.5 --- Conclusion --- p.87 / Chapter 6 --- Predicting Protein Structures on a Lattice Model using an Evolution- ary Algorithm for Multimodal Optimization --- p.88 / Chapter 6.1 --- Introduction --- p.88 / Chapter 6.2 --- Problem Definition --- p.89 / Chapter 6.3 --- Representation --- p.90 / Chapter 6.4 --- Related Works --- p.91 / Chapter 6.5 --- Crowding Genetic Algorithm with Spatial Locality --- p.92 / Chapter 6.5.1 --- Motivation --- p.92 / Chapter 6.5.2 --- Customization --- p.92 / Chapter 6.5.2.1 --- Distance metrics --- p.92 / Chapter 6.5.2.2 --- Handling infeasible conformations --- p.93 / Chapter 6.6 --- Experiments --- p.94 / Chapter 6.6.1 --- Performance Metrics --- p.94 / Chapter 6.6.2 --- Parameter Settings --- p.94 / Chapter 6.6.3 --- Results --- p.94 / Chapter 6.7 --- Conclusion --- p.95 / Chapter 7 --- Conclusion and Future Work --- p.97 / Chapter 7.1 --- Thesis Contribution --- p.97 / Chapter 7.2 --- Fixture Work --- p.98 / Chapter A --- Appendix --- p.99 / Chapter A.1 --- Problem Definition in Chapter 3 --- p.107 / Bibliography --- p.109 / Author's Publications --- p.122

AG to A transition at position -76 in the promoter region of the apoAI gene was previously identified, and the A-76 has been shown to be associated with high apoAI levels. The functional significance of the point mutation was assessed by analysing the DNA-protein binding and promoter activities of the different alleles. This data would suggest that the point mutation alters the function of the apoAI promoter as gel retention assays revealed that the G fragment (-140 to +10) formed an extra DNA-protein complex compared to the A fragment (-140 to +10). Concurrent with the altered DNA-protein interaction between the G and the A fragments, the transcriptional activities of the apoAI gene were found to also be altered. CAT assays have indicated a 1.91 fold increase in promoter activity of the A fragment as compared to the G fragment (-256 to +397). The difference in promoter activity was, however, highly dependent on the particular fragment used, as no difference was observed between the alleles when a fragment {-256 to +68) was used. In this study elements were identified in the region +68 to +397 that causes a reduction in the promoter activity of the G allele by 3.6 fold, whilst reducing the A allele activity by 2 fold. This data would suggest that the point mutation functionally alters the apoAI promoter activity via its interaction with other sequences especially in the region +68 to +397.