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The membrane proteins signal peptide peptidase, signal peptide peptidase like and presenilin are intramembrane aspartyl proteases located in the endoplasmic reticulum, plasma membrane and organelle. These membrane proteins are able to catalyze a hydrolytic reaction in a hydrophobic space. The downstream consequences of these reactions impact a variety of cellular functions such as cytokine production, inflammatory responses, embryogenesis, and immune system regulation. Additionally, the aspartyl proteases such as signal peptide peptidase and presenilin, a part of the γ-secretase complex, hydrolyze peptides leading to pathogen maturation and Alzheimer’s disease, respectively. Electron crystallography offers the unique aspect of studying membrane proteins in a near native state. Determining the structures of Haloarcula morismortui and Methanoculleus marisnigri JR1 signal peptide peptidases by electron crystallography may provide insight into how a hydrolysis reaction occurs in a hydrophobic environment and how the protein determines which transmembrane signal peptides to cleave. Additionally, structure determination may help answer questions regarding why human presenilin, part of the γ-secretase complex, incorrectly processes amyloid precursor protein into amyloid-beta peptides leading to Alzheimer’s disease. Such structural data may not only shed light on how amyloid precursor protein is processed but how other proteins are processed by signal peptide peptidase leading to immune responses, cell signaling, and pathogen maturation. In addition, structure-function data may have an impact on pharmaceutical drug designs that targets signal peptide peptidase, signal peptide peptidase like, and/or presenilin. To determine the structure of aspartyl proteases, two archaeal signal peptide peptidases were used for two-dimensional crystallization trials to be able to study their structure by electron crystallography. Haloarcula morismortui and Methanoculleus marisnigri JR1 signal peptide peptidases, both human signal peptide peptidase homologues, were recombinantly over-expressed and purified. During dialysis trials, various lipid-to-protein ratios, sodium chloride concentrations, temperatures, detergents and a variety of other variables were tested. Methanoculleus marisnigri JR1 signal peptide peptidase showed the most promising results in terms of crystallinity. Optimizing dialysis conditions, specifically narrowing the lipid to protein ratio, resulted in two-dimensional crystals. Ordered arrays measuring up to 200 nm x 200 nm were observed. These ordered arrays have been shown to be reproducible amongst multiple batches of purified Methanoculleus marisnigri JR1 signal peptide peptidase. Preliminary projection maps of negatively stained ordered arrays show unit cell dimensions of a = 178 Å, b = 160 Å, γ = 92.0 Å and a = 175 Å, b = 167 Å, γ = 92.0 Å. The monomer measurements are approximately 70 Å by 80 Å. This is the first time a signal peptide peptidase homologue has been crystallized by two-dimensional crystallization.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24528号 / 医博第4970号 / 新制||医||1065(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 伊藤 能永, 教授 羽賀 博典, 教授 河本 宏 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

License plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring, expressway toll system, electronic-police system, and security systems. In recent years, there has been a lot of research in license plate recognition, and many recognition systems have been proposed and used. But these systems have been developed for computers. In this project, we developed a mobile LPR system for Android Operating System (OS). LPR involves three main components: license plate detection, character segmentation and Optical Character Recognition (OCR). For License Plate Detection and character segmentation, we used JavaCV and OpenCV libraries. And for OCR, we used tesseract-ocr. We obtained very good results by using these libraries. We also stored records of license numbers in database and for that purpose SQLite has been used.