Chong Lin-Tat. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 76-78). / Abstracts in English and Chinese. / Chapter Chapter 1 --- introduction / Chapter 1.1 --- Severe acute respiratory syndrome Coronavirus (SARS CoV) --- p.13 / Figure 1.1 Genome organization and putative functional ORFs of SARS CoV --- p.14 / Chapter 1.2 --- SARS main protease / Chapter 1.2.1 --- Three dimensional structure --- p.15 / Figure 1.2 Ribbon illustration of the SARS-coronavirus main protease --- p.17 / Figure 1.3 Surface representations of P1 and P2 substrate-binding pocket of main protease --- p.18 / Chapter 1.2.2 --- Substrate specificities --- p.19 / Table 1.1. Eleven predicted cleavage sites of SARS CoV main protease --- p.21 / Chapter 1.3 --- Protein-based FRET assay system --- p.22 / Figure 1.4. The principle of fluorescent resonance energy transfer (FRET) --- p.24 / Chapter 1.4 --- Objectives --- p.25 / Chapter Chapter 2 --- Materials and Methods / Chapter 2.1 --- General Techniques / Chapter 2.1.1 --- Preparation and transformation of competent E. coli DH5a and23 BL21 (DE3)pLysS --- p.26 / Chapter 2.1.2 --- Minipreparation of plasmid DNA (Invitrogen) --- p.27 / Chapter 2.1.3 --- Spectrophotometric quantitation DNA --- p.28 / Chapter 2.1.4 --- Agarose gel electrophoresis / Chapter 2.1.5 --- Purification of DNA from agarose gel (Invitrogen) / Chapter 2.1.6 --- Restriction digestion of DNA fragments --- p.29 / Chapter 2.1.7 --- Ligation of DNA fragments into vector / Table 2.1. Standard recipe of ligation reaction --- p.30 / Chapter 2.1.8 --- SDS-PAGE electrophoresis --- p.31 / Table 2.2. Standard recipe of separating gel for SDS-PAGE --- p.32 / Table 2.3. Standard recipe of stacking gel for SDS-PAGE --- p.33 / Chapter 2.2 --- Sub-cloning and site-directed mutagenesis / Chapter 2.2.1 --- Sub-cloning of SARS Co V main protease --- p.34 / Chapter 2.2.2 --- Sub-cloning of Substrate / Chapter 2.2.3 --- Site-directed mutagenesis of substrate variant --- p.35 / Table 2.4 Primer sequence for generating substrate variants --- p.36 / Table 2.5. Standard recipe of Polymerase Chain Reaction (PCR) --- p.40 / Table 2.6. Polymerase Chain Reaction (PCR) profile --- p.41 / Chapter 2.3 --- Sample preparation / Chapter 2.3.1 --- Expression of recombinant proteins --- p.42 / SARS CoV main protease / Substrate and substrate variants / Chapter 2.3.2 --- Purification of recombinant proteins / SARS CoV main protease / Substrate and substrate variants / Chapter 2.4 --- Protein-based FRET kinetic analysis --- p.45 / Chapter 2.5 --- A model for substrate-enzyme binding by docking simulation --- p.46 / Chapter Chapter 3 --- Results / Chapter 3.1 --- Preparation of SARS CoV main protease and substrate / Chapter 3.1.1 --- Expression and purification of SARS main protease --- p.48 / Figure 3.1. Purification profile of SARS CoV main protease --- p.49 / Chapter 3.1.2 --- Expression and purification of substrate and substrate variants --- p.50 / Figure 3.2. Purification profile of substrate and substrate variants --- p.51 / Chapter 3.2 --- A novel protein-based FRET assay system was established / Chapter 3.2.1 --- "With the cleavage of active main protease, absorbance at 528nm dropped while signal at 485nm were slightly increased" --- p.52 / Figure 3.3. Absorbance at 528nm dropped and 485nm increased with the substrate hydrolysis --- p.53 / Chapter 3.2.2 --- FRET efficiency ratio (528/485) decreased over time --- p.54 / Figure 3.4. FRET efficiency ratio (528/485) decreased over time --- p.55 / Chapter 3.2.3 --- Comparable kcat/Km value of SARS CoV main protease was obtained --- p.56 / Figure 3.5. Catalytic parameter (kcat/ Km) was determined from the slope of straight Line --- p.57 / Chapter 3.3 --- Main protease activity towards substrate variants at different substrate-binding sites (S2'-S2) --- p.58 / Table 3.1. Kinetic parameterrs of 76 substrate variants in descending order --- p.59 / Chapter 3.3.1 --- S2'substrate-binding site --- p.60 / Chapter 3.3.2 --- S1' substrate-b inding site / Chapter 3.3.3 --- S1 substrate-binding site / Chapter 3.3.4 --- S2 substrate-binding site / Figure 3.6. Kinetic analysis of some typical substrate variants against main protease --- p.62 / Figure 3.7. SDS-PAGE analysis of some typical substrate variants against main protease --- p.63 / Chapter Chapter 4 --- Discussion / Chapter 4.1 --- Quantitative and high-throughput analysis by protein-based FRET assay system --- p.64 / Chapter 4.2 --- Substrate specificities of SARS CoV main protease at S2'-S2 subsites / Chapter 4.2.1 --- β-strand conformation was preferred at S2，subsite / Chapter 4.2.2 --- Residues with small aliphatic side chain were preferred at S1 ´ة subsite --- p.65 / Chapter 4.2.3 --- "Glutamine at S1 subsite was absolutely conserved, but alternatives were disclosed" --- p.66 / Figure 4.1. Glutamine was not absolutely conserved in S1 subsite --- p.67 / Chapter 4.2.4 --- Hydrophilic residues were tolerated at S2 subsite --- p.68 / Figure 4.2. Hydrophilic residues were tolerated at S2 subsite --- p.70 / Table 4.1. Summary of types of residues preferred at individual subsites --- p.71 / Chapter 4.3 --- Predicted conformation of substrate towards SARS CoV main protease at S2' and S1' subsites --- p.72 / Figure 4.3. Small residues were preferred at S1´ة subsite and Val at S2' subsite was more favoured than the native one --- p.73 / Chapter Chapter 5 --- Summary --- p.74 / Chapter Chapter 6 --- Future work --- p.75 / References --- p.76

Tak-Chung Lau. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 190-198). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background Information --- p.4 / Chapter 1.2 --- Importance of the Problem --- p.6 / Chapter 1.3 --- Problem Definition and Proposed Algorithm Outline --- p.7 / Chapter 1.4 --- Simple Illustration --- p.10 / Chapter 1.5 --- Outline of the Thesis --- p.12 / Chapter 2 --- Survey --- p.14 / Chapter 2.1 --- Introduction --- p.14 / Chapter 2.2 --- Dynamic Programming (DP) --- p.15 / Chapter 2.2.1 --- Introduction --- p.15 / Chapter 2.2.2 --- Algorithm --- p.15 / Chapter 2.2.3 --- Example --- p.16 / Chapter 2.2.4 --- Complexity Analysis --- p.20 / Chapter 2.2.5 --- Summary --- p.21 / Chapter 2.3 --- General Alignment Tools --- p.21 / Chapter 2.4 --- K-Nearest Neighbor (KNN) --- p.22 / Chapter 2.4.1 --- Value of K --- p.22 / Chapter 2.4.2 --- Example --- p.23 / Chapter 2.4.3 --- Variations in KNN --- p.24 / Chapter 2.4.4 --- Summary --- p.24 / Chapter 2.5 --- Decision Tree --- p.25 / Chapter 2.5.1 --- General Information of Decision Tree --- p.25 / Chapter 2.5.2 --- Classification in Decision Tree --- p.26 / Chapter 2.5.3 --- Disadvantages in Decision Tree --- p.27 / Chapter 2.5.4 --- Comparison on Different Types of Trees --- p.28 / Chapter 2.5.5 --- Conclusion --- p.29 / Chapter 2.6 --- Hidden Markov Model (HMM) --- p.29 / Chapter 2.6.1 --- Markov Process --- p.29 / Chapter 2.6.2 --- Hidden Markov Model --- p.31 / Chapter 2.6.3 --- General Framework in HMM --- p.32 / Chapter 2.6.4 --- Example --- p.34 / Chapter 2.6.5 --- Drawbacks in HMM --- p.35 / Chapter 2.7 --- Chapter Summary --- p.36 / Chapter 3 --- Related Work --- p.37 / Chapter 3.1 --- Resonant Recognition Model (RRM) --- p.37 / Chapter 3.1.1 --- Introduction --- p.37 / Chapter 3.1.2 --- Encoding Stage --- p.39 / Chapter 3.1.3 --- Transformation Stage --- p.41 / Chapter 3.1.4 --- Evaluation Stage --- p.43 / Chapter 3.1.5 --- Important Conclusion in RRM --- p.47 / Chapter 3.1.6 --- Summary --- p.48 / Chapter 3.2 --- Motivation --- p.49 / Chapter 3.2.1 --- Example --- p.51 / Chapter 3.3 --- Chapter Summary --- p.53 / Chapter 4 --- Group Classification --- p.54 / Chapter 4.1 --- Introduction --- p.54 / Chapter 4.2 --- Design --- p.55 / Chapter 4.2.1 --- Data Preprocessing --- p.55 / Chapter 4.2.2 --- Encoding Stage --- p.58 / Chapter 4.2.3 --- Transformation Stage --- p.63 / Chapter 4.2.4 --- Evaluation Stage --- p.64 / Chapter 4.2.5 --- Classification --- p.72 / Chapter 4.2.6 --- Summary --- p.75 / Chapter 4.3 --- Experimental Settings --- p.75 / Chapter 4.3.1 --- "Statistics from Database of Secondary Structure in Pro- teins (DSSP) [27], [54]" --- p.76 / Chapter 4.3.2 --- Parameters Used --- p.77 / Chapter 4.3.3 --- Experimental Procedure --- p.79 / Chapter 4.4 --- Experimental Results --- p.79 / Chapter 4.4.1 --- Reference Group - Neurotoxin --- p.80 / Chapter 4.4.2 --- Reference Group - Biotin --- p.82 / Chapter 4.4.3 --- Average Results of all the Groups --- p.84 / Chapter 4.4.4 --- Conclusion in Experimental Results --- p.88 / Chapter 4.5 --- Discussion --- p.89 / Chapter 4.5.1 --- Discussion on the Experimental Results --- p.89 / Chapter 4.5.2 --- Complexity Analysis --- p.94 / Chapter 4.5.3 --- Other Discussion --- p.99 / Chapter 4.6 --- Chapter Summary --- p.102 / Chapter 5 --- Individual Classification --- p.103 / Chapter 5.1 --- Design --- p.103 / Chapter 5.1.1 --- Group Profile Generation --- p.104 / Chapter 5.1.2 --- Preparation of Each Testing Examples --- p.104 / Chapter 5.2 --- Design with Clustering --- p.104 / Chapter 5.2.1 --- Motivation --- p.105 / Chapter 5.2.2 --- Data Exception --- p.105 / Chapter 5.2.3 --- Clustering Technique --- p.110 / Chapter 5.2.4 --- Classification --- p.116 / Chapter 5.3 --- Hybridization of Our Approach and Sequence Alignment --- p.116 / Chapter 5.3.1 --- AlignRemove and AlignChange --- p.117 / Chapter 5.3.2 --- Classification --- p.119 / Chapter 5.4 --- Experimental Settings --- p.120 / Chapter 5.4.1 --- Parameters Used --- p.120 / Chapter 5.4.2 --- Choosing of Protein Functional Groups --- p.121 / Chapter 5.5 --- Experimental Results --- p.122 / Chapter 5.5.1 --- Experimental Results Setup --- p.122 / Chapter 5.5.2 --- Receiver Operating Characteristics (ROC) Curves --- p.123 / Chapter 5.5.3 --- Interpretation of Comparison Results --- p.125 / Chapter 5.5.4 --- Area under the Curve --- p.138 / Chapter 5.5.5 --- Classification with KNN --- p.141 / Chapter 5.5.6 --- Three Types of KNN --- p.142 / Chapter 5.5.7 --- Results in Three Types of KNN --- p.143 / Chapter 5.6 --- Complexity Analysis --- p.144 / Chapter 5.6.1 --- Complexity in Individual Classification --- p.144 / Chapter 5.6.2 --- Complexity in Individual Clustering Classification --- p.146 / Chapter 5.6.3 --- Complexity of Individual Classification in DP --- p.148 / Chapter 5.6.4 --- Conclusion --- p.148 / Chapter 5.7 --- Discussion --- p.149 / Chapter 5.7.1 --- Domain Expert Opinions --- p.149 / Chapter 5.7.2 --- Choosing the Threshold --- p.149 / Chapter 5.7.3 --- Statistical Support in an Individual Protein --- p.150 / Chapter 5.7.4 --- Discussion on Clustering --- p.151 / Chapter 5.7.5 --- Poor Performance in Hybridization --- p.154 / Chapter 5.8 --- Chapter Summary --- p.155 / Chapter 6 --- Application --- p.157 / Chapter 6.1 --- Introduction --- p.157 / Chapter 6.1.1 --- Construct the Correlation Graph --- p.157 / Chapter 6.1.2 --- Minimum Spanning Tree (MST) --- p.161 / Chapter 6.2 --- Application in Group Classification --- p.164 / Chapter 6.2.1 --- Groups with Weak Relationship --- p.164 / Chapter 6.2.2 --- Groups with Strong Relationship --- p.166 / Chapter 6.3 --- Application in Individual Classification --- p.168 / Chapter 6.4 --- Chapter Summary --- p.171 / Chapter 7 --- Discussion on Other Analysis --- p.172 / Chapter 7.1 --- Distanced MLN Encoding Scheme --- p.172 / Chapter 7.2 --- Unique Encoding Method --- p.174 / Chapter 7.3 --- Protein with Multiple Functions? --- p.175 / Chapter 7.4 --- Discussion on Sequence Similarity --- p.176 / Chapter 7.5 --- Functional Blocks in Proteins --- p.177 / Chapter 7.6 --- Issues in DSSP --- p.178 / Chapter 7.7 --- Flexible Encoding --- p.179 / Chapter 7.8 --- Advantages over Dynamic Programming --- p.179 / Chapter 7.9 --- Novel Research Direction --- p.180 / Chapter 8 --- Future Works --- p.182 / Chapter 8.1 --- Improvement in Encoding Scheme --- p.182 / Chapter 8.2 --- Analysis on Primary Protein Sequences --- p.183 / Chapter 8.3 --- In Between Spectrum Scaling --- p.184 / Chapter 8.4 --- Improvement in Hybridization --- p.185 / Chapter 8.5 --- Fuzzy Threshold Boundaries --- p.185 / Chapter 8.6 --- Optimal Parameters Setting --- p.186 / Chapter 8.7 --- Generalization Tool --- p.187 / Chapter 9 --- Conclusion --- p.188 / Bibliography --- p.190 / Chapter A --- Fourier Transform --- p.199 / Chapter A.1 --- Introduction --- p.199 / Chapter A.2 --- Example --- p.201 / Chapter A.3 --- Physical Meaning of Fourier Transform --- p.201

Au Yeung, Wan Kin. / "August 2011." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 89-103). / Abstracts in English and Chinese. / Thesis committee --- p.i / Statement --- p.ii / Abstract --- p.iii / Acknowledgements --- p.v / General abbreviations --- p.vi / Abbreviations of chemicals --- p.vii / List of figures --- p.ix / List of figures in Appendix VI --- p.xii / List of tables --- p.xiv / Table of Contents --- p.xv / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The significance of studying rice disease resistance --- p.1 / Chapter 1.1.1 --- Economic importance of rice --- p.1 / Chapter 1.1.2 --- Diseases caused by pathogens virulent to rice --- p.1 / Chapter 1.1.2.1 --- Bacterial leaf blight diseases --- p.1 / Chapter 1.1.2.2 --- Fungal blast diseases --- p.2 / Chapter 1.1.3 --- Approach to enhance resistance of crops towards pathogens --- p.2 / Chapter 1.2 --- Literature review on plant immunity system --- p.3 / Chapter 1.2.1 --- Pathogen associated molecular patterns (PAMP) and PAMP -triggered immunity (PTI) --- p.4 / Chapter 1.2.2 --- Pathogen effectors and effector-triggered immunity (ETI) --- p.5 / Chapter 1.2.3 --- Roles of phytohormones in plant defense responses --- p.6 / Chapter 1.2.4 --- G protein signaling and plant defense responses --- p.9 / Chapter 1.3 --- Literature review on FK506 binding proteins (FKBPs) --- p.10 / Chapter 1.4 --- Background information of this study - origin of the clone chosen for study in this project --- p.11 / Chapter 1.5 --- Hypothesis and Objectives --- p.12 / Chapter Chapter 2 --- Materials and Methods --- p.13 / Chapter 2.1 --- Materials --- p.13 / Chapter 2.1.1 --- "Plants, bacterial strains and vectors" --- p.13 / Chapter 2.1.2 --- Chemicals and Regents --- p.18 / Chapter 2.1.3 --- Commercial kits --- p.18 / Chapter 2.1.4 --- Primers and Adaptors --- p.19 / Chapter 2.1.5 --- Equipments and facilities used --- p.23 / Chapter 2.1.6 --- "Buffer, solution, gel and medium" --- p.23 / Chapter 2.2 --- Methods --- p.24 / Chapter 2.2.1. --- Bacterial and yeast cultures --- p.24 / Chapter 2.2.2 --- Plant growth conditions and treatments --- p.25 / Chapter 2.2.2.1 --- Surface sterilization of J. thaliana seeds --- p.25 / Chapter 2.2.2.2 --- Environmental conditions of A. thaliana for germination of seeds and growing of seedlings --- p.26 / Chapter 2.2.2.3 --- Environmental conditions of A. thaliana for growing of plants --- p.26 / Chapter 2.2.2.4 --- Pathogen inoculation test of A. thaliana with Pst DC3000 --- p.27 / Chapter 2.2.3 --- Cloning and subcloning of OsFKBP 12 and OsUCCl --- p.27 / Chapter 2.2.3.1 --- Sub-cloning of OsFKBP12 to pGEX-4T-l and pMAL-c2 --- p.27 / Chapter 2.2.3.2 --- Cloning of OsUCCl to pGEX-4T-l --- p.29 / Chapter 2.2.4 --- "DNA, RNA and protein extractions" --- p.29 / Chapter 2.2.4.1 --- Plasmid extraction from bacterial cells --- p.29 / Chapter 2.2.4.2 --- Genomic DNA extraction from plant through CTAB method --- p.29 / Chapter 2.2.4.3 --- RNA extraction from plant tissues --- p.30 / Chapter 2.2.4.4 --- Protein extraction from plant tissues --- p.31 / Chapter 2.2.4.5 --- Fusion protein extraction from E. coli --- p.31 / Chapter 2.2.5 --- Western blot analyses --- p.32 / Chapter 2.2.5.1 --- Western blot analysis of GST tag and MBP tag fusion proteins --- p.32 / Chapter 2.2.5.2 --- Western blot analysis native OsYchFl proteins --- p.33 / Chapter 2.2.6 --- Real-time PCR study --- p.33 / Chapter 2.2.6.1 --- cDNA synthesis --- p.33 / Chapter 2.2.6.2 --- Real-time PCR --- p.34 / Chapter 2.2.7 --- Yeast two hybrid --- p.35 / Chapter 2.2.7.1 --- Screening of OsFKBP 12 interaction protein partners by yeast mating --- p.35 / Chapter 2.2.7.2 --- Identification of positive interacting protein partners by extracting DNA plasmid from yeast --- p.35 / Chapter 2.2.7.3 --- Re-transformation of pGBKTl-OsFKBP 12 with their interacting partner clones into yeast (AH 109) by co-transformation --- p.36 / Chapter 2.2.8 --- In vitro pull down assay of OsFKBP 12 with their putative protein interacting partner --- p.36 / Chapter 2.2.8.1 --- In vitro pull down of native OsYchFl by MBP-His-OsFKBP12 --- p.36 / Chapter 2.2.8.2 --- In vitro pull down of GST-AtYchF 1 by MBP-His-OsFKBP12 --- p.37 / Chapter 2.2.8.3 --- In vitro pull down of MBP-His-OsFKBP12 by GST-OsUCCl --- p.37 / Chapter 2.2.8.4 --- In vitro pull down of MBP-His-OsFKBP12 by GST-OsYchFl G domain --- p.38 / Chapter 2.2.9 --- GTPase assay ofOsYchF with OsFKBP12 --- p.38 / Chapter 2.3.0 --- Phylogenetic analysis and sequence alignment --- p.39 / Chapter Chapter 3 --- Results --- p.40 / Chapter 3.1 --- Identification of OsFKBP 12 encoding a FKBP (FK506 binding protein)-domain containing protein in Oryza sativa (rice) --- p.40 / Chapter 3.2 --- OsFKBP12 was down-regulated in the pathogen-inoculated Xal4 rice line CBB14 --- p.47 / Chapter 3.3 --- Ecotpic expression of OsFKBP 12 repressed the expression of defense marker genes in transgenic A. thaliana --- p.50 / Chapter 3.4 --- Expressing OsFKBP 12 in transgenic A. thaliana enhanced the susceptibility to the bacterial pathogen Pst DC3000 --- p.54 / Chapter 3.5 --- OsFKBP 12 protein interacted with a putative defense-related G-protein and a copper binding protein --- p.57 / Chapter 3.6 --- "OsFKBP 12 protein interacted with the G domain of defense-related G protein, OsYchFl" --- p.69 / Chapter 3.7 --- OsFKBP 12 protein enhanced the in vitro phosphate release of OsYchFl --- p.72 / Chapter Chapter 4 --- Discussion --- p.74 / Chapter 4.1 --- The identification and characterization of OsFKBP 12 --- p.74 / Chapter 4.2 --- Expression pattern of OsFKBP 12 upon biotic stress in bacterial blight resistant near isogenic line (NIL) --- p.75 / Chapter 4.3 --- OsFKBP 12 repressed the expression of SA-regulated defense marker genes when ectopically expressed in A. thaliana --- p.75 / Chapter 4.4 --- Ectopic expression of OsFKBP 12 enhanced susceptibility towards Pst DC3000 in transgenic A. thaliana --- p.76 / Chapter 4.5 --- The interacting partners of OsFKBP 12 in relation to plant defense response --- p.78 / Chapter 4.6 --- The specific biochemical interaction of OsFKBP 12 with OsYchFl --- p.80 / Chapter 4.7 --- Future perspectives --- p.85 / Chapter Chapter 5 --- Conclusion --- p.87 / References --- p.89 / Appendix --- p.104

Electrostatic interaction has long been proposed to be an important factor for stabilizing protein. Charge-charge interaction may especially be important to the thermostability of protein, as having more surface electrostatic interactions is one of the common structural features found in thermophilic proteins when compared to their mesophilic homologues. In order to quantitatively investigate the electrostatic contribution to protein stability, two complementary approaches, namely the double mutant cycle approach and pKa shift approach, were carried out. / In the double mutant cycle approach, the coupling free energies of two salt bridges (E6/R92 and K46/E62) and one a long range ion pair (E90/R92) were estimated by using circular dichroism, to find out the thermodynamic parameters of the protein model Thermococcus celer L30e and its charge-to-neutral mutants. It was found that the coupling free energy was temperature independent and was about 3 kJ mol-1 per salt bridge. By using a novel analysis of double mutant cycle of DeltaC p, it was also found that the interaction of salt bridge plays an important role in the reduction of DeltaCp. The temperature independency of coupling free energy and the effect of reducing DeltaCp could explain the general observation very well that thermophilic proteins have highly up-shifted protein stability curves is due to its elevated electrostatic interactions when compared with their mesophilic homologs. / In the pKa shift approach, the native state pKa values of acidic residues were obtained by fitting the side chain carboxyl 13C chemical shifts to microscopic model or global fitting of titrational event (GloFTE), whereas the denatured state pKa values were obtained by conventional pH titration of terminal protected 5-residue glycine-based model peptide. It was found that the surface charge-charge interactions, either attractive or repulsive, were strong and complicated because of the high surface charge density of T. celer L30e. However, the fact that most of the acidic residues have significantly downshifted native state pK a values indicated the surface charge distribution of T. celer L30e is optimized for stabilizing the protein. In addition, we have shown that temperature has negligible effect on pKa values in both native state and denatured state, therefore temperature can only marginally amplify the stabilizing effect in linear manner. / To overcome the unwanted crystallization problem of wild-type T. celer L30e in the low ionic strength neutral pH NMR conditions, which were essential for the pKa shift approach, a quintuple Arg-to-Lys variant was designed to dramatically improve the crystalline solubility, while the surface charges, as well as the structural, thermodynamic, and electrostatic properties, were conserved. It has also shown that electrostatic interaction played a critical role in crystallization at low ionic strength conditions, and arginine residue was especially important in crystal packing because of its high ability of forming salt bridges and hydrogen bonds. / Wild-type T. celer L30e has also shown to have no observable residual structure in the guanidine HC1-induced denatured state, indicating that denatured state of T. celer L30e should not have large effect on the overall protein stability. / Chan, Chi Ho. / Adviser: Kam Bo Wong. / Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 202-218). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

In attempts to explore the usefulness of the newly introduced electron capture dissociation (ECD) mass spectrometry for structural analysis of peptides/proteins, different fundamental aspects of the ECD method were investigated using a combination of controlled experiments and high-level theoretical calculations. The relative propensity for dissociation (RPD) of different amino acid residues were extracted from a series of ECD experiments using a common peptide model of RGGGXGGGR, where X was varied systematically among 20 common amino acid residues. Although polar and aromatic amino acid residues were found to behave differently, there exists a fair correlation between the experimental RPD values of aliphatic amino acid residues and the corresponding calculated hydrogen atom affinities of the nearby carbonyl groups. The existence of this correlation reinforces the importance of "hot hydrogen-atom model". From the same set of experiments, the side chain loss reactions of the reduced precursor ions and the zn+• species were extracted. To account for the observed secondary fragments, several generalized dissociation pathways were proposed. The energetics of these dissociation pathways were evaluated theoretically with truncated peptide models using ab initio and DFT calculations; and the kinetics of several competitive reactions were evaluated using Rice-Ramsberger-Kassel-Marcus (RRKM) calculations. / The effect of charge carriers on ECD of peptides/proteins was also studied. Peptides charged through protonation of different basic amino acid residues were found to give ECD spectra of different complexities. The formation of b-/y- and atypical internal fragment ions in peptides with histidines (and lysine, to a lesser extent) as proton carriers was attributed to the higher electron-proton recombination energy as revealed from the energy cycle diagram. Peptides charged through attachment of divalent metal ions were found to give very different ECD spectra. It was believed that typical c/z • fragments were formed from neutralization reactions involving electron-proton recombination; whereas a/b/y fragments were formed from reaction involving electron-metal ion recombination. The preference of recombination channels was somehow related to the electronic configurations of the divalent metal ions. / Fung Yi Man. / "July 2006." / Adviser: T. W. Chan. / Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5254. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 180-185). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Bibliography: leaves 138-158. / x, 158 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This study identified and investigated the properties, functions and patterns of accumulation of prominent berry proteins associated with white wine haze. Detailed analysis was conducted on two PR-like proteins of V. vinifera, VVPR-4a and VVTL1. In vitro fungal growth inhibition assays suggested that berry PR-like proteins may play an important role in plant defence, particularly against fungal attack. Results of this study also have future implications for controlling the ripening process of grapes. / Thesis (Ph.D.)--University of Adelaide, Dept. of Horticulture, Viticulture and Oenology, 1999

Bibliography: leaves 138-158. This study identified and investigated the properties, functions and patterns of accumulation of prominent berry proteins associated with white wine haze. Detailed analysis was conducted on two PR-like proteins of V. vinifera, VVPR-4a and VVTL1. In vitro fungal growth inhibition assays suggested that berry PR-like proteins may play an important role in plant defence, particularly against fungal attack. Results of this study also have future implications for controlling the ripening process of grapes.

Genomic analysis is a new approach for the characterization and investigation of novel genes, gene clusters, the function of uncharacterized proteins, and genetic diversity in microorganisms. These approaches are important for the study of chlamydiae, a system in which several genomes have been sequenced but in which techniques for genetic manipulation are not available. The objective of this thesis is to combine computer-based analysis of chiamydial inclusion membrane proteins (Incs) with cellular and molecular biological analysis of the bacteria. Three different experimental lines of investigation were examined, focusing on Incs of C. trachomatis and C. pneumoniae. Chlamydiae are obligate intracellular bacteria that develop within a nonacidified membrane bound vacuole termed an inclusion. Putative Inc proteins of C. trachomatis and C. pneumoniae were identified from genomic analysis and a unique structural motif. Selected putative Inc proteins are shown to localize to the inclusion membrane. Chiamydia trachomatis variants with unusual multiple-lobed, nonfusogenic, inclusion were identified from a large scale serotyping study. Fluorescence microscopy showed that IncA, a chiamydial protein localized to the inclusion membrane, was undetectable on non-fusogenic inclusions of these variants. Sequence analysis of incA from non-fusogenic variant isolates revealed a defective incA in most of the variants. Some variants lack not only IncA on the inclusion membrane but also CT223p, an additional Inc protein. However, no correlation between the absence of CT223p and distinctive inclusion phenotype was identified. Nucleotide sequence analysis revealed sequence variations of C. trachomatis incA and CT223 in some variant and wild type isolates. Comparative analyses of the three recently published C. pneumoniae genomes have led to the identification of a novel gene cluster named the CPn1O54 gene family. Each member of this family encodes a polypeptide with a hydrophobic domain characteristic of proteins localized to the inclusion membrane. These studies provided evidence that gene variation might occur within this single collection of paralogous genes. Collectively, the variability within this gene family may modulate either phase or antigenic variation, and subsequent physiologic diversity, within a C. pneumoniae population. These studies demonstrate the genetic diversity of Inc proteins and candidate Inc proteins, within and among the different chiamydial species. This work sets the stage for further investigations of the structure and function of this set of proteins that are likely critical to chlamydial intracellular growth. / Graduation date: 2003

Graduation date: 1992

In this thesis, we have advanced a set of distinct bioinformatic and computational tools to address the structure and function of proteins. Using data mining of the protein data bank (PDB), we have collected statistics connecting the propensity between the protein sequence and the secondary structure. This new tool has enabled us to evaluate new structures as well as a family of structures. A comparison of the wild type staphylococcal nuclease to various mutants using the proposed tool has indicated long-range conformational deviations spatially distant from the mutation point. The energetics of protein unfolding has been studied in terms of the forces observed in molecular dynamics simulations. An adaptive integration of the steered molecular dynamics is proposed to reduce ground state dominance by the rare low energy trajectories on the estimated free energy profile. The proposed adaptive algorithm is utilized to reproduce the potential of mean force of the stretching of decaalanine in vacuum at lower computational cost. It is then used to construct the potential of mean force of this transition in solvent for the first time as to observe the hydration effect on the helix-coil transformation. Adaptive steered molecular dynamics is also implemented to obtain the free energy change during the unfolding of neuropeptide Y and to confirm that the monomeric form of neuropeptide Y adopts halical-hairpin like pancreatic-polypeptide fold.