Notwithstanding the challenges associated with different methods of peptide identification, other methods have been explored over the years. The complexity, size and computational challenges of peptide-based data sets calls for more intrusion into this sphere. By relying on the prior information about the average relative abundances of bond cleavages and the prior probability of any specific amino acid sequence, we refine an already developed Bayesian approach in identifying peptides. The likelihood function is improved by adding additional ions to the model and its size is driven by two overall goodness of fit measures. In the face of the complexities associated with our posterior density, a Markov chain Monte Carlo algorithm coupled with simulated annealing is used to simulate candidate choices from the posterior distribution of the peptide sequence, where the peptide with the largest posterior density is estimated as the true peptide.

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Protein prefractionation is a popular and effective strategy for improved MS analysis of complex proteome mixtures. A challenge of prefractionation is the even partitioning with high recovery of all components of the mixture, particularly hydrophobic proteins. This thesis assesses various proteome prefractionation platforms, with a goal of comprehensive proteome analysis. A more reliable dataset of 1136 S. cerevisiae transmembrane proteins was computationally generated, and used to assess two gel-based platforms (GeLC/MS and GELFrEE/MS). These platforms were determined to be comparable for proteome analysis. The requirement for high-throughput, automated fractionation demands a gel-free separation workflow. Here, a LC-based workflow was optimized, relying on SDS-assisted yeast extraction, organic solvent protein precipitation, and reversed phase separation in a formic acid/isopropanol solvent system. Though this workflow afforded improvements over conventional LC strategies to proteome fractionation, the gel-based platforms were demonstrated to be superior, in terms of their unbiased separation of hydrophobic vs hydrophilic proteins.

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Research presented in this dissertation falls into two parts: fragmentation mechanisms of peptide and fragmentation mechanism of amino acid derivatives. The study of peptide fragmentation may help to improve protein identification by incorporating the rules governing this process into search algorithms. This study elucidates the chemical 'rules' governing peptide dissociation. It is believed that these 'rules' can be incorporated into searching algorithms to achieve better protein identification. The present study focuses on the effects of different amino acids on fragmentation. Amino acids with a wide range of different chemical and physical properties are investigated, including amino acids with hydrophilic side chains, amino acids with aliphatic side chains and amino acids without side chains. It can be concluded from the present studies that the different amino acid properties have great influence on the peptide fragmentation and spectrum appearance.The study of fragmentation mechanisms of amino acid derivatives is another focus of this dissertation. Based on the fragmentation mechanism study, a quantification method was developed. The method can distinguish glutamine with 15N-label at N-terminal amine vs the side chain even if they have same molecular weight. Ammonia metabolism was successfully monitored by feeding mosquitoes with isotope-labeled compounds and subsequently measuring the amount of the labeled amino acids. This method demonstrates the power of mass spectrometry in metabolism studies.

京都大学 / 0048 / 新制・課程博士 / 博士(薬科学) / 甲第22651号 / 薬科博第123号 / 新制||薬科||13(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 石濱 泰, 教授 緒方 博之, 教授 馬見塚 拓, 教授 山下 富義 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

Protein engineering is a young dynamic discipline with great amount of potential practical applications. However, its success is primarily based on perfect knowledge and usage of all existing information about protein function and structure. To achieve that, protein engineering is supported by plenty of bioinformatic tools and analysis. The goal of this project is to create a new tool for protein engineering that would enable researchers to identificate related proteins with modified function in still growing biological databases. The tool is designed as an automated workflow of existing bioinformatic analyses that leads to identification of proteins with the same type of enzymatic function, but with slightly modified properties - primarily in terms of selectivity, reaction speed and stability.

>Magister Scientiae - MSc / Salt stress is a major abiotic stress that accounts for huge agricultural losses worldwide, which in turn threaten food security and sustainable agriculture. Salt triggers the excessive production of reactive oxygen species (ROS) which accumulate to levels that become toxic to plants, resulting in cell death and reduced plant growth. Part of the plant’s mechanisms to counteract ROS-induced cell death involves the scavenging ability of the antioxidant defense system to maintain redox homeostasis. Gallic acid (GA) is an antioxidant that has been shown to reduce salt-induced ROS in legume plants. However, its effects on wheat plants have not been elucidated. This study thus investigated the role of exogenous GA (250 μM) on the physiological responses and antioxidant system of wheat plants under salt stress (150 mM). In addition, this study also investigated how GA and salt stress influenced changes in the membrane proteome of wheat plants using LC-MS proteomic analysis. / 2022

Mass spectrometry-based proteomics excels at high-throughput identification of proteins expressed in complex biological samples. However, the technology struggles to identify low abundance proteins due to large amounts of redundant data acquired for high abundance proteins with little collected for low abundance proteins. To improve the identification sensitivity of these proteins, I designed a machine learning classifier that assesses protein identification confidence on-the-fly, during mass spectrometry analysis. Proteins deemed confidently identified are excluded from further analysis, saving mass spectrometry resources for lower abundance proteins. Simulating data from a HEK293 cell lysate mass spectrometry analysis, our algorithm uses 16.2% - 66.2% fewer mass spectrometry resources with a 2.6% - 39.5% drop in protein identifications. When applied to live mass spectrometry experiments, these saved resources will likely improve the overall protein identification sensitivity of the experiment, particularly for lower abundance proteins, and will therefore provide a better understanding of the cell’s biology.

No description available.