In order to identify Leishmania proteins which may be immunologically relevant or may play a role in interactions between Leishmania and its mammalian host, a Leishmania major genomic DNA library was constructed in the vector λgt11 and screened with antibodies raised to Leishmania major promastigote membranes. Two recombinant DNA clones were identified which encoded repetitive sequences (Clone 20 and Clone 39). Clone 20 encoded a repetitive peptide of 14 amino acids and clone 39 encoded an unrelated repetitive peptide of 10 amino acids. Analysis of one of these clones, Clone 20, indicated that there were two RNA transcripts of 9500 and 5200 nucleotides expressed which corresponded to this clone in Leishmania major and Leishmania donovani and this expression was not stage-specific. The results of genomic DNA analysis and isolation of additional clones encoding Clone 20 sequences indicated that there were two genes which corresponded to Clone 20 in both Leishmania major and Leishmania donovani and that these genes differed from one another with respect to the number of repeats which they contained. Antibodies against the fusion protein produced by Clone 20 recognized a series of Leishmania major proteins of apparent mol wt 250,000. Analysis of Clone 39 indicated that there was a single transcript of 7500 nucleotides expressed which corresponded to this clone in both Leishmania major and Leishmania donovani and that there was a single gene (or two identical genes) which encoded this transcript. The genomes of many protozoan parasites exhibit a high degree of plasticity with respect to chromosome size and number. The presence of highly repetitive regions within their DNA may be involved in maintaining this plasticity, allowing the parasite to evolve rapidly under selective pressure. Repetitive regions have been identified within many Plasmodia antigens and have been implicated in the ability of this parasite to evade the host immune system. The presence of Leishmania genes encoding proteins containing tandemly repeating peptides may indicate that these proteins play a similar role in evading the host immune system during the course of Leishmania infections. The possible evolution and functions of repetitive proteins in protozoan parasites is discussed. / Medicine, Faculty of / Medical Genetics, Department of / Graduate

A defining contribution of systems biology has been to reveal how cellular circuitry works to govern the state of a cell. Often, cell-state is determined by the activity of a small number of hyperconnected transcriptional regulators (TRs; e.g., transcription factors, (de)acetylases, (de)methylases, and other genes that act at the level of DNA to affect transcription). The activity of these TRs can be detected from the transcription of their targets, but doing so requires accurate gene regulatory networks (GRNs). The best way to construct GRNs is by combining computationally inferred networks with experimental perturbation data, but until recently this has not been feasible in human cells. As a first step in that direction, I undertook to perform a large-scale Transcriptional REgulator Knock-down (TREK), at two timepoints, in two cancer cell lines, at single-cell level, and to use the resulting data to improve our ability to infer the regulatory state of the cell. In all, I constructed regulons for over 900 TRs and described the dynamics both over time and across contexts. I have significantly improved our GRNs and, consequently, our ability to measure protein activity and identify cell-state regulators.

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.

Silks from araneoid spiders have become an active area of research for material scientists, biochemists, and molecular biologists. Mechanical properties of spider silk such as elasticity, tensile strength, and toughness make the manufacturing of silk for medical sutures, body armor, ropes and other synthetic material applications great possibilities. The difficulties of having a black widow spider farm to harvest silk, due to their cannibalistic nature, make recombinant expression of silk proteins a fundamental goal of spider silk research. In order to express silk fibers, cDNAs encoding the corresponding silk fiber products must first be isolated and identified. One of the first steps in gene identification relies on the identification of the proteins in the silk fibers. No previous study has demonstrated the molecular constituents of gumfooted lines. In the course of this research, the core fibroins in the gumfooted lines were identified to be members of the Major Ampullate Spidroin family (MaSp), using mass spectrometry. This research was the first to identify the core fibroins of the gumfooted lines. Novel peptide fragments from solubilized gumfooted lines were acquired from manual de novo MSIMS sequencing after in-gel tryptic digestion. These peptide fragments showed post-translational modifications consistent with glycosylation, which aligns with the reported chemical properties of glue proteins. Novel peptide sequences were also acquired from the attachment discs as well as novel scanning electron microscopy images and reveal, for the first time, the physical attributes and molecular properties of threads attached to the surface of an immobilized structure. This study was the first to identify the molecular constituents of the attachment discs.

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

Thesis (MScMedSc (Biomedical Sciences. Molecular Biology and Human Genetics. Medical Biochemistry))--University of Stellenbosch, 2007. / Connexins (Cx) are major proteins of gap junctions, dynamic pores mediating the relay of ions and metabolites between cells. Cxs 40, 43 and 45 are the predominant cardiac isoforms and their distinct distribution raises questions about their functional differences. Their cytoplasmic (C)-terminal domains are involved in protein-protein interactions. Furthermore, mutations in the myotonic dystrophy protein kinase (DMPK)-causative gene are associated with disruptions in cardiac conduction similar to that described for Cx knock-out mice. DMPK is a Cx43 ligand, raising the possibility that defects in Cx40 ligands may be involved in the development of cardiac conduction disturbances. We hypothesised that delineation of the protein ligands of the C-termini of Cx40 and of Cx45 (parallel study conducted by N Nxumalo) would help elucidate their functional roles. Yeast-two-hybrid methodology was used to identify putative Cx40 ligands. Primers were designed to amplify the C-terminus-encoding domain of the human Cx40 gene (Cx40), the DNA product was cloned into the pGBKT7 vector which was used to screen a cardiac cDNA library in Saccharomyces cerevisiae. Successive selection stages reduced the number of putative Cx40 ligand-containing colonies (preys) from 324 to 33. The DNA sequences of the 33 ligands were subjected to BLAST-searches and internet database literature searches to assign identity and function and to exclude false positive ligands based on subcellular location and function. Eleven plausible ligands were identified: cysteine-rich protein 2 (CRP2), beta-actin (ACTB), creatine kinase, muscle type (CKM), myosin, heavy polypeptide 7 (MYH7), mucolipin1 (MCOLN1), voltage-dependent anion channel 2 (VDAC2), aldehyde dehydrogenase 2 (ALDH2), DEAH box polypeptide 30 (DHX30), NADH dehydrogenase, 6, (NDUFA6), prosaposin (PSAP) and filamin A (FLNA). Cxs 40 and 45 showed differences in the classes of proteins with which they interacted; the majority of putative Cx40 interactors were cytoplasmic proteins, while Cx45 interactors were mitochondrial proteins. These results suggest that Cxs 40 and 45 are not only functionally different, but may also have different cellular distributions. Further analyses of these protein interactions will shed light on the independent roles of Cxs 40 and 45.

Many Gram-positive and Gram-negative bacteria produce an additional thick layer of carbohydrate polymers on the cell wall surface. These capsules (capsular polysaccharides; CPS) play critical roles in interactions between bacteria and their environments (Whitfield, 2006). This is especially important in infection processes since for both Gram-negative and Gram-positive pathogens CPS is the point of first contact with the host immune system (Whitfield, 2006). However, the details of CPS biosynthesis and assembly mechanisms are still unclear. Therefore, we embarked on structural and kinetic studies of the proteins Wzc, Wza and Wzb/ Cps4B from the Wzy-dependent pathway, as well as the protein WbdD from the ATP-binding cassette (ABC) transporter dependent system. Full-length Wzc failed to crystallise due to the presence of large disordered regions and the overall difficulty of membrane protein crystallisation. A truncated version of Wzc (1-480) without the C-terminal tyrosine kinase domain was crystallised and diffracted to 15 Å in house. A previous study suggested Wza and Wzc form a functional complex (Whitfield, 2006), so Wza was also studied. Since the full-length Wza structure is available (C. Dong et al., 2006), Pulsed electron–electron double resonance spectroscopy (PELDOR) was used to study the conformational change. The PELDOR spectroscopy distance fingerprint of Wza was determined. These data also confirmed that PELDOR is a powerful tool to study large, highly symmetrical membrane proteins and can be used to study other complex membrane protein systems, such as ion channels or transporters. The crystal structure of Wzb the cognate phosphatase of Wzc was determined to 2.2 Å. Also Cps4B, which is a functional homologue of Wzb but has a completely unrelated sequence, was crystallised in two crystal forms. Form I and II Cps4B crystals diffracted to 2.8 Å and 1.9 Å resolution in house, respectively. The full-length WbdD failed to crystallise due to the presence of large disordered regions. Therefore, a shorter construct, WbdD₅₅₆ (1-556) was cloned and crystallised. The structure was determined to 2.2 Å. WbdD is a bifunctional enzyme consisting of a methyltransferase (MTase) and a kinase domain. In order to better understand the function of this protein, a variety of techniques were used, such as the ADP-Glo kinase assay, Nuclear magnetic resonance (NMR) spectroscopy, small angle X-ray scattering (SAXS) and X-ray crystallography. The various findings in the current projects provide meaningful insights towards a better understanding of the CPS biosynthesis and assembly mechanisms, which may contribute to a more intensive study identifying inhibitors and beginning to unravel the mechanism of chain length regulation.

Proteins play an important role in cells, as the morphology, function and activities of the cell depend on the proteins they express. The key to understanding how different proteins function lies in an understanding of the molecular structure. The overall aim of this thesis was the determination of the structure of DWNN domains. This thesis described the preparation of samples of human DWNN suitable for structural analysis by nuclear magnetic resonance spectroscopy (NMR), as well as NMR analysis.

Two swine kidney proteins (PI 4.8 and 5.8) both possessing 9-prostaglandin ketoreductase (9-PGKR) and 15-hydroxyprostaglandin dehydrogenase (15-PGDH) activities were purified to homogeneity. Purification increased specific activities in parallel. Molecular weight, subunit size, amino acid composition, coenzyme and substrate specificity and antigenicity of both proteins were similar. Gel filtration and SDS-polyacrylamide gel electrophoresis molecular weights of 29,500 and 29,000, respectively, suggested a single subunit. Although a variety of prostaglandins served as substrates, the best for 15-PGDH was PGB, while PGA_1-GSH showed the lowest Km for 9-PGKR. Rabbit antibody against the PI 5.8 protein crossreacted with both purified renal enzymes and with extracts from rat spleen, lung, heart, aorta, and liver.

Chu Ling Hon Matthew. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 201-223). / Abstracts in English and Chinese. / Declaration --- p.i / Thesis/Assessment Committee --- p.ii / Abstract --- p.iii / 摘要 --- p.vi / Acknowledgements --- p.viii / General abbreviations --- p.xi / Abbreviations of chemicals --- p.xv / Table of Contents --- p.xvi / List of Figures --- p.xxiii / List of tables --- p.xxviii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Severe Acute Respiratory Syndrome (SARS) - Three Years in Review --- p.1 / Chapter 1.1.1 --- Epidemiology --- p.1 / Chapter 1.1.2 --- Clinical presentation --- p.3 / Chapter 1.1.3 --- Diagnostic tests --- p.5 / Chapter 1.2 --- Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) --- p.7 / Chapter 1.2.1 --- SARS - Identification of the etiological agent --- p.7 / Chapter 1.2.2 --- The coronaviruses --- p.9 / Chapter 1.2.3 --- The genome organization of SARS-CoV --- p.11 / Chapter 1.2.4 --- The life cycle of SARS-CoV --- p.13 / Chapter 1.3 --- Spike Glycoprotein (S protein) of SARS-CoV --- p.15 / Chapter 1.3.1 --- SARS-CoV S protein --- p.15 / Chapter 1.3.2 --- S protein-driven infection --- p.17 / Chapter 1.4 --- SARS-CoV S Protein Fusion Core --- p.22 / Chapter 1.4.1 --- Heptad repeat and coiled coil --- p.22 / Chapter 1.4.2 --- The six-helix coiled coil bundle structure --- p.25 / Chapter 1.5 --- 3C-like Protease (3CLpro) of SARS-CoV --- p.28 / Chapter 1.5.1 --- Extensive proteolytic processing of replicase polyproteins --- p.28 / Chapter 1.5.2 --- SARS-CoV 3CLpro --- p.30 / Chapter 1.5.3 --- Substrate Specificity of SARS-CoV 3CLpro --- p.31 / Chapter 1.6 --- SARS Drug Development --- p.32 / Chapter 1.6.1 --- Drug targets of SARS-CoV --- p.32 / Chapter 1.6.2 --- Current anti-SARS drugs --- p.36 / Chapter 1.7 --- Project Objectives --- p.39 / Chapter 1.7.1 --- Characterization of SARS-CoV S protein fusion core --- p.39 / Chapter 1.7.2 --- Characterization of SARS-CoV 3CLpr0 substrate specificity --- p.40 / Chapter 2 --- Materials and Methods --- p.42 / Chapter 2.1 --- Characterization of SARS-CoV S Protein Fusion Core --- p.42 / Chapter 2.1.1 --- Bioinformatics analyses of heptad repeat regions of SARS- CoV S protein --- p.42 / Chapter 2.1.2 --- Recombinant protein approach --- p.43 / Chapter 2.1.2.1 --- Plasmids construction --- p.43 / Chapter 2.1.2.2 --- Protein expression and purification --- p.52 / Chapter 2.1.2.3 --- Amino acid analysis --- p.57 / Chapter 2.1.2.4 --- GST-pulldown experiment --- p.58 / Chapter 2.1.2.5 --- Laser light scattering --- p.61 / Chapter 2.1.2.6 --- Size-exclusion chromatography --- p.62 / Chapter 2.1.2.7 --- Circular dichroism spectroscopy --- p.62 / Chapter 2.1.3 --- Synthetic peptide approach --- p.64 / Chapter 2.1.3.1 --- Peptide synthesis --- p.64 / Chapter 2.1.3.2 --- Native polyacrylamide gel electrophoresis --- p.65 / Chapter 2.1.3.3 --- Size-exclusion high-performance liquid chromato-graphy --- p.66 / Chapter 2.1.3.4 --- Laser light scattering --- p.66 / Chapter 2.1.3.5 --- Circular dichroism spectroscopy --- p.67 / Chapter 2.2 --- Identification of SARS-CoV Entry Inhibitors --- p.70 / Chapter 2.2.1 --- HIV-luc/SARS pseudotyped virus entry inhibition assay --- p.70 / Chapter 2.2.2 --- Recombinant protein- and synthetic peptide-based biophysical assays --- p.74 / Chapter 2.2.3 --- Molecular modeling --- p.75 / Chapter 2.3 --- Characterization of SARS-CoV 3CLpro Substrate Specificity --- p.79 / Chapter 2.3.1 --- Protein expression and purification --- p.79 / Chapter 2.3.2 --- """Cartridge replacement"" solid-phase peptide synthesis" --- p.80 / Chapter 2.3.3 --- Peptide cleavage assay and mass spectrometric analysis --- p.83 / Chapter 3 --- Results --- p.84 / Chapter 3.1 --- Characterization of SARS-CoV S Protein Fusion Core --- p.84 / Chapter 3.1.1 --- Bioinformatics analyses of heptad repeat regions of SARS- CoV S protein --- p.84 / Chapter 3.1.2 --- Recombinant protein approach --- p.87 / Chapter 3.1.2.1 --- "Plasmids construction of pET-28a-His6-HRl, pGEX-6P-l-HR2 and pGEX-6P-l-2-Helix" --- p.87 / Chapter 3.1.2.2 --- Protein expression and purification --- p.92 / Chapter 3.1.2.3 --- GST-pulldown experiment --- p.101 / Chapter 3.1.2.4 --- Laser light scattering --- p.103 / Chapter 3.1.2.5 --- Size-exclusion chromatography --- p.105 / Chapter 3.1.2.6 --- Circular dichroism spectroscopy --- p.107 / Chapter 3.1.3 --- Synthetic peptide approach --- p.112 / Chapter 3.1.3.1 --- Peptide synthesis --- p.112 / Chapter 3.1.3.2 --- Native polyacrylamide gel electrophoresis --- p.116 / Chapter 3.1.3.3 --- Size-exclusion high-performance liquid chromatography --- p.117 / Chapter 3.1.3.4 --- Laser light scattering --- p.122 / Chapter 3.1.3.5 --- Circular dichroism spectroscopy --- p.124 / Chapter 3.2 --- Identification of SARS-CoV Entry Inhibitors --- p.129 / Chapter 3.2.1 --- HIV-luc/SARS pseudotyped virus entry inhibition assay --- p.129 / Chapter 3.2.2 --- Recombinant protein- and synthetic peptide-based biophysical assays --- p.131 / Chapter 3.2.3 --- Molecular modeling --- p.135 / Chapter 3.3 --- Characterization of SARS-CoV 3CLpro Substrate Specificity --- p.141 / Chapter 3.3.1 --- Protein expression and purification --- p.141 / Chapter 3.3.2 --- Substrate specificity preference of SARS-CoV 3CLpr0 --- p.142 / Chapter 3.3.3 --- "Primary and secondary screening using the ""cartridge replacement strategy""" --- p.142 / Chapter 4 --- Discussion --- p.149 / Chapter 4.1 --- Characterization of SARS-CoV S Protein Fusion Core --- p.149 / Chapter 4.1.1 --- Design of recombinant proteins and synthetic peptides of HR regions --- p.149 / Chapter 4.1.2 --- Recombinant protein approach --- p.151 / Chapter 4.1.3 --- Synthetic peptide approach --- p.153 / Chapter 4.1.4 --- Summary of the present and previous studies in the SARS-CoV S protein fusion core --- p.157 / Chapter 4.2 --- Identification of SARS-CoV Entry Inhibitors --- p.167 / Chapter 4.2.1 --- HIV-luc/SARS pseudotyped virus entry inhibition assay --- p.167 / Chapter 4.2.2 --- Identification of peptide inhibitors --- p.168 / Chapter 4.2.3 --- Identification of small molecule inhibitors --- p.172 / Chapter 4.3 --- Characterization of SARS-CoV 3CLpro Substrate Specificity --- p.183 / Chapter 4.3.1 --- A comprehensive overview of the substrate specificity of SARS-CoV 3CLpro --- p.184 / Chapter 4.3.2 --- The development of the rapid and high-throughput screening strategy for protease substrate specificity --- p.188 / Appendix --- p.191 / Chapter I. --- Nucleotide Sequence of S protein of SARS-CoV --- p.191 / Chapter II. --- Protein Sequence of S protein of SARS-CoV --- p.194 / Chapter III. --- Protein Sequence of 3CLpro of SARS-CoV --- p.195 / Chapter IV. --- Vector maps --- p.196 / Chapter 1. --- Vector map and MCS of pET-28a --- p.196 / Chapter 2. --- Vector map and MCS of pGEX-6P-l --- p.197 / Chapter V. --- Electrophoresis markers --- p.198 / Chapter 1. --- GeneRuler´ёØ 1 kb DNA Ladder --- p.198 / Chapter 2. --- GeneRuler´ёØ 100bp DNA Ladder --- p.198 / Chapter 3. --- High-range Rainbow Molecular Weight Markers --- p.199 / Chapter 4. --- Low-range Rainbow Molecular Weight Markers --- p.199 / Chapter VI. --- SDS-PAGE gel preparation protocol --- p.200 / References --- p.201