Thesis (MSc)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: In this study, the analysis of ammo acids usmg High Performance Liquid Chromatography (HPLC) with pre-column derivatisation was optimised. The derivatisation reagents include o-phthaldialdehyde (OPA), 9- fluorenylmethylchloroformate (FMOC) and iodoacetic acid (IDA). Detection was performed using UV and fluorescence in series. The developed method was utilised for the analysis and quantitation of amino acids in eighteen wines. The application of chemometric data evaluation was initiated. / AFRIKAANSE OPSOMMING: Hierdie ondersoek behels die optimisering van die aminosuuranalise deur gebruik te maak van Hoë Druk Vloeistof Chromatografie (HDVC) in kombinasie met pre-kolom derivatisering. Die derivativatiserings reagense sluit in o-phthaldialdehied (OPA), 9- fluorenielmetielchloroformaat (FMOC) en jodoasynsuur (IDA). Deteksie is gedoen deur gebruik gemaak van 'n ultraviolet (UV) en 'n fluorosensie detektor in serie. Die metode sodoende ontwikkel is gebruik vir die analise en kwantifisering van aminosure in agtien wyne. Die toepassing van chemometriese data evaluasie is ook geïnisieer.

by Chan, Kim Hung. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 76-89). / Abstract also in Chinese. / Chapter 1. --- INTRODUCION --- p.1 / Chapter 1.1 --- amino acid analysis by high performance liquid chromatography --- p.1 / Chapter 1.1.1 --- History and Development --- p.1 / Chapter 1.1.2 --- Separation mechanism --- p.2 / Chapter 1.1.3 --- Derivatization --- p.4 / Chapter 1.1.4 --- Dqproteinization --- p.8 / Chapter 1.1.5 --- Ion-exchange or Reversed-phase HPLC --- p.9 / Chapter 1.2 --- amino acid pattern in cancer patient --- p.11 / Chapter 1.2.1 --- Cancer cachexia --- p.11 / Chapter 1.2.2 --- Causes of cancer cachexia --- p.11 / Chapter 1.2.3 --- Cytokines --- p.12 / Chapter 1.2.4 --- Metabolic Alteration in cancer cachexia --- p.13 / Chapter 1.2.5 --- Amino Acid Studies --- p.14 / Chapter 1.3 --- methodology chosen --- p.19 / Chapter 1.4 --- patient sample chosen --- p.21 / Chapter 2. --- OBJECTIVES --- p.22 / Chapter 3. --- MATERIALS AND METHOD --- p.23 / Chapter 3.1 --- apparatus --- p.23 / Chapter 3.1.1 --- HPLC System --- p.23 / Chapter 3.1.2 --- Column --- p.23 / Chapter 3.1.3 --- Detector --- p.23 / Chapter 3.1.4 --- ChemStation --- p.24 / Chapter 3.2 --- reagents --- p.24 / Chapter 3.2.1 --- Reagent and Chemical source --- p.24 / Chapter 3.2.2 --- Mobile phase --- p.24 / Chapter 3.2.3 --- Derivatization Reagent --- p.25 / Chapter 3.2.4 --- Standard preparation --- p.26 / Chapter 3.2.5 --- Internal standard --- p.28 / Chapter 3.3 --- sample preparation --- p.28 / Chapter 3.4 --- chromatographic conditions --- p.29 / Chapter 3.4.1 --- Column Temperature --- p.29 / Chapter 3.4.2 --- Injector Program --- p.29 / Chapter 3.4.3 --- Time Table for gradient elution and flow program --- p.32 / Chapter 3.5.1 --- OP A and sample Ratio and Volume --- p.32 / Chapter 3.5.2 --- Derivatization Concentration --- p.33 / Chapter 3.5.3 --- Derivatization time --- p.33 / Chapter 3 6 --- analytical performance --- p.34 / Chapter 3.6.1 --- Linearity testing --- p.34 / Chapter 3.6.2 --- Recovery studies --- p.34 / Chapter 3.6.3 --- Precision --- p.34 / Chapter 3.6.4 --- Sample storage --- p.35 / Chapter 3.7 --- clinical sample studies --- p.35 / Chapter 3.8 --- statistical analysis --- p.36 / Chapter 4 --- RESULT --- p.37 / Chapter 4.1 --- chromatographic separation --- p.37 / Chapter 4.2 --- optimization --- p.40 / Chapter 4.2.1 --- OPA and sample Ratio and Volume --- p.40 / Chapter 4.2.2 --- Derivatization time --- p.43 / Chapter 4.2.3 --- OPA Concentration --- p.43 / Chapter 4.3 --- analytical performance --- p.46 / Chapter 4.3.1 --- Linearity --- p.46 / Chapter 4.3.2 --- Recovery studies --- p.46 / Chapter 4.3.3 --- Precision Studies --- p.50 / Chapter 4.3.4 --- Sample storage studies --- p.53 / Chapter 4.4 --- clinical sample study --- p.55 / Chapter 5. --- DISCUSSION --- p.64 / Chapter 5.1 --- analytical --- p.64 / Chapter 5.2 --- clinical --- p.71 / Chapter 5.2.1 --- Normal controls --- p.71 / Chapter 5.2.2 --- Colorectal Cancer --- p.71 / Chapter 5.2.3 --- Lung Cancer --- p.72 / Chapter 5.2.4 --- Nasopharyngeal Cancer --- p.73 / Chapter 5.2.5 --- Summary --- p.74 / Chapter 6. --- CONCLUSION --- p.75 / Chapter 7. --- REFERENCES --- p.75

Sudden changes in nutrient resources are common in the natural environment. Cells are able to adapt and propagate under changing environmental conditions by making adjustments in their cellular processes. These cellular adaptations involve genome-wide transcriptional reprogramming that results in the induction or repression of metabolic pathways. Specific enzymes are then synthesised and activated to maximise the use of the newly available nutrient sources. L-serine is one of the twenty proteinogenic amino acids, and can be synthesised in yeast by the glycolytic and gluconeogenic pathways when growing on fermentable or non-fermentable carbon sources or taken up from the environment when available. L-serine is metabolically linked to glycine and is a predominant donor of one-carbon units in one-carbon metabolism. L-serine is also a source of pyruvate and ammonia and contributes to other cellular processes including the biosynthesis of cysteine and phospholipids. Previous work has shown that yeast cells exhibit transcriptional induction of the one-carbon pathway and the genes involved in the synthesis of purine and methionine after the addition of 10 mM glycine. Here it is shown that addition of 10 mM L-serine did not, however, elicit the same transcriptional response. This is primarily due to differences in the uptake of glycine and L-serine in yeast. High concentrations of extracellular L-serine were required for yeast to show an increase in intracellular L-serine concentration of the magnitude required to trigger a noticeable cellular response. Despite L-serine and glycine being interconvertable via the SHMT isozymes and being a one-carbon donor, the genome-wide transcriptional response exhibited by cells in response to L-serine addition was markedly different to that seen for glycine. The predominant response to an increase in intracellular L-serine was the induction of the general amino acid control system and the CHA1 gene encoding the serine (threonine) dehydratase. Unlike glycine, addition of L-serine triggered only minor induction of the one-carbon pathway. A large portion of intracellular L-serine was converted to pyruvate and ammonia in the mitochondrion as the result of induction of CHA1. The high intracellular concentration of L-serine stimulated the cell to increase the production of oxaloacetate and to increase the biosynthesis of L-aspartate. Transient increases in the intracellular L-glutamate and L-glutamine were also observed after the addition of L-serine. The work presented in this study shows that large increase in the intracellular concentration of amino acid is required to trigger a significant transcriptional response. Yeast cells exhibit different transcriptional and metabolic responses to the addition of L-serine and glycine even though these two amino acids are closely metabolically linked. Addition of L-serine provokes the GAAC response, expression of the CHA1 gene and stimulates the biosynthesis of L-aspartate in yeast whereas addition of glycine induces the one-carbon pathway which leads to the biosynthesis of the purine nucleotides.

Sudden changes in nutrient resources are common in the natural environment. Cells are able to adapt and propagate under changing environmental conditions by making adjustments in their cellular processes. These cellular adaptations involve genome-wide transcriptional reprogramming that results in the induction or repression of metabolic pathways. Specific enzymes are then synthesised and activated to maximise the use of the newly available nutrient sources. L-serine is one of the twenty proteinogenic amino acids, and can be synthesised in yeast by the glycolytic and gluconeogenic pathways when growing on fermentable or non-fermentable carbon sources or taken up from the environment when available. L-serine is metabolically linked to glycine and is a predominant donor of one-carbon units in one-carbon metabolism. L-serine is also a source of pyruvate and ammonia and contributes to other cellular processes including the biosynthesis of cysteine and phospholipids. Previous work has shown that yeast cells exhibit transcriptional induction of the one-carbon pathway and the genes involved in the synthesis of purine and methionine after the addition of 10 mM glycine. Here it is shown that addition of 10 mM L-serine did not, however, elicit the same transcriptional response. This is primarily due to differences in the uptake of glycine and L-serine in yeast. High concentrations of extracellular L-serine were required for yeast to show an increase in intracellular L-serine concentration of the magnitude required to trigger a noticeable cellular response. Despite L-serine and glycine being interconvertable via the SHMT isozymes and being a one-carbon donor, the genome-wide transcriptional response exhibited by cells in response to L-serine addition was markedly different to that seen for glycine. The predominant response to an increase in intracellular L-serine was the induction of the general amino acid control system and the CHA1 gene encoding the serine (threonine) dehydratase. Unlike glycine, addition of L-serine triggered only minor induction of the one-carbon pathway. A large portion of intracellular L-serine was converted to pyruvate and ammonia in the mitochondrion as the result of induction of CHA1. The high intracellular concentration of L-serine stimulated the cell to increase the production of oxaloacetate and to increase the biosynthesis of L-aspartate. Transient increases in the intracellular L-glutamate and L-glutamine were also observed after the addition of L-serine. The work presented in this study shows that large increase in the intracellular concentration of amino acid is required to trigger a significant transcriptional response. Yeast cells exhibit different transcriptional and metabolic responses to the addition of L-serine and glycine even though these two amino acids are closely metabolically linked. Addition of L-serine provokes the GAAC response, expression of the CHA1 gene and stimulates the biosynthesis of L-aspartate in yeast whereas addition of glycine induces the one-carbon pathway which leads to the biosynthesis of the purine nucleotides.

Malaria poses the greatest threat of all parasites to human life. Current vaccines and efficacious drugs are available however their use is limited due to toxicity, emergence of drug resistance, and cost. The discovery of an alternative pathway of isoprenoid biosynthesis, the non-mevalonate pathway, within the malarial parasite has resulted in development of novel anti-malarial drugs. 1-Deoxy-D-xylulose-5-phosphate (DOXP) reductoisomerase, the second enzyme in this pathway, is responsible for the synthesis of 2-C-methyl-D-erythritol 4-phosphate (MEP) in an intramolecular rearrangement step followed by a reduction process involving NADPH as a hydrogen donor and divalent cations as co-factors. Fosmidomycin and FR900098 have been identified as inhibitors of DOXP reductoisomerase. However, they lack clinical efficacy. In this investigation recombinant DOXP reductoisomerase from Escherichia coli (EcDXR) and Plasmodium falciparum (pfDXR) were biochemically characterized as potential targets for inhibition. (His)6-EcDXR was successfully purified using nickel-chelate affinity chromatography with a specific activity of 1.77 μmoles/min/mg and Km value 282 μM. Utilizing multiple sequence alignment, previous structural data predictions and homology modeling approaches, critical active site amino acid residues were identified and their role in the catalytic activity investigated utilizing site-directed mutagenesis techniques. We have shown evidence that suggests that Trp212 and Met214 interact to maintain the active site architecture and hydrophobic interactions necessary for substrate binding, cofactor binding and enzyme activity. Replacement of Trp212 with Tyr, Phe, and Leu reduced specific activity relative to EcDXR. EcDXR(W212F) and EcDXR(W212Y) had an increased Km relative to EcDXR indicative of loss in affinity toward DOXP, whereas EcDXR(W212L) had a lower Km of ~8 μM indicative of increased affinity for DOXP. The W212L substitution possibly removed contacts necessary for full catalytic activity, but could be considered a non-disruptive substitution in that it maintained active site architecture sufficient for DOXP reductoisomerase activity. EcDXR(M214I) had 36-fold reduced enzyme activity relative to EcDXR, while its Km (~8 μM) was found to be lower than that of EcDXR. This suggested that the M214I substitution had maintained (perhaps improved) substrate and active site architecture, but may have perturbed interactions with NADPH. Rational drug design strategies and docking methods have been utilized in the development of furan derivatives as DOXP reductoisomerase inhibitors, and the synthesis of phosphorylated derivatives (5) and (6) has been achieved. Future inhibitor studies using these novel potential DOXP reductoisomerase inhibitors may lead to the development of effective anti-malarial drug candidates.

The problem with which this research was done was that of applying the IBM360 computer to the analysis of waveforms from a Beckman model 120C liquid chromatograph. Software to interpret these waveforms was written in the PLl language. For a control run, input to the computer consisted of a digital tape containing the raw results of the chromatograph run. Output consisted of several graphs and charts giving the results of the analysis. In addition, punched output was provided which gave the name of each amino acid, its elution time and color constant. These punched cards were then input to the computer as input to the experimental run, along with the raw data on the digital tape. From the known amounts of amino acids in the control run and the ratio of control to experimental peak area, the amino acids of the unknown were quantified. The resulting programs provided a complete and easy to use solution to the problem of chromatographic data analysis.

A₂bu composed 20-40% of total free amino acids in flatpea tissues and constituted 2-4% of the tissue dry weight. Higher concentrations of A₂bu were found in the leaves than in the roots. A₂bu concentration in leaves and stems increased slightly with plant age. Higher nitrogen availability increased the content of A₂bu in flatpea, a response accompanied by an increase in the contents of soluble protein and other nitrogenous compounds. When exogenous nitrogen supply was decreased, A₂bu levels decreased significantly. Rhizobium infection had no effect on the A₂bu production by flatpea. Ammonium was toxic to flatpea growth. Together with typical toxic symptoms, A₂bu elevation was observed in flatpea plants fed with ammonium. Water-deficit stress also elevated A₂bu content of flatpea. The elevation of A₂bu concentration in aerial tissues of flatpea under stress may not be high enough to decrease the value of flatpea as a forage. 4-Aminobutyric acid (Abu), homoserine (Hse), and asparagine (Asn) were the other major free amino acids in flatpea. As with A₂bu, levels of Hse were higher in the leaves than in the roots. The opposite was true for Abu and Asn. The concentration of Abu in the stems increased consistently with plant age. In response to stress conditions, Abu accumulated in flatpea, especially in stems and roots. Asn was the most prevalent free amino acid in the roots of flatpea. Asn levels in roots increased with plant age and accounted for the greatest portion of the increase in the free amino acid pool in the roots of plants subjected to the water stress or supplied with nitrogen in the form of ammonium ions. Levels of Hse in flatpea were changed little in response to the experimental treatments. Relative amounts of major amino acids in flatpea changed with respect to plant organs and experimental factors. If expressed as ratios, the resulting values suggest metabolic relationships. / Ph. D. / incomplete_metadata

The goal of the following thesis research was to develop a systematic approach for the refinement of low-resolution protein models, as a part of the protein structure prediction procedure. Significant progress has been made in the field of protein structure prediction and the contemporary methods are able to assemble correct topology for a large fraction of protein domains. But such approximate models are often not detailed enough for some important applications, including studies of reaction mechanisms, functional annotation, drug design or virtual ligand screening. The development of a method that could bring those structures closer to the native is then of great importance. The minimal requirements for a potential that can refine protein structures is the existence of a correlation between the energy with native similarity and the scoring of the native structure as being lowest in energy. Extensive tests of the contemporary all-atom physics-based force fields were conducted to assess their applicability for refinement. The tests revealed flatness of such potentials and enabled the identification of the key problems in the current approaches. Guided by these results, the optimization of the AMBER (ff03) force field was performed that aimed at creating a funnel shape of the potential, with the native structure at the global minimum. Such shape should facilitate the conformational search during refinement and drive it towards the native conformation. Adjusting the relative weights of particular energy components, and adding an explicit hydrogen bond potential significantly improved the average correlation coefficient of the energy with native similarity (from 0.25 for the original ff03 potential to 0.65 for the optimized force field). The fraction of proteins for which the native structure had lowest energy increased from 0.22 to 0.90. The new, optimized potential was subsequently used to refine protein models of various native-similarity. The test employed 47 proteins and 100 decoy structures per protein. When the lowest energy structure from each trajectory was compared with the starting decoy, we observed structural improvement for 70% of the models on average. Such an unprecedented result of a systematic refinement is extremely promising in the context of high-resolution structure prediction.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Intrinsically disordered proteins (IDPs) are flexible proteins without defined 3D structures. Studies show that IDPs are abundant in nature and actively involved in numerous biological processes. Two crucial subjects in the study of IDPs lie in analyzing IDPs’ functions and identifying them. We thus carried out three projects to better understand IDPs. In the 1st project, we propose a method that separates IDPs into different function groups. We used the approach of CH-CDF plot, which is based the combined use of two predictors and subclassifies proteins into 4 groups: structured, mixed, disordered, and rare. Studies show different structural biases for each group. The mixed class has more order-promoting residues and more ordered regions than the disordered class. In addition, the disordered class is highly active in mitosis-related processes among others. Meanwhile, the mixed class is highly associated with signaling pathways, where having both ordered and disordered regions could possibly be important. The 2nd project is about identifying if an unknown protein is entirely disordered. One of the earliest predictors for this purpose, the charge-hydropathy plot (C-H plot), exploited the charge and hydropathy features of the protein. Not only is this algorithm simple yet powerful, its input parameters, charge and hydropathy, are informative and readily interpretable. We found that using different hydropathy scales significantly affects the prediction accuracy. Therefore, we sought to identify a new hydropathy scale that optimizes the prediction. This new scale achieves an accuracy of 91%, a significant improvement over the original 79%. In our 3rd project, we developed a per-residue C-H IDP predictor, in which three hydropathy scales are optimized individually. This is to account for the amino acid composition differences in three regions of a protein sequence (N, C terminus and internal). We then combined them into a single per-residue predictor that achieves an accuracy of 74% for per-residue predictions for proteins containing long IDP regions.