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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/48980

Title: 石蓴蛋白質之分離與特徵化分析及利用生物資訊工具預測活性胜肽
Isolation and Characterization of Proteins from Green Algae (Ulva lactuca) and Prediction of Bioactive Peptides Using Bioinformatics Tools
Authors: Elfida Saputri
艾菲達
Contributors: NTOU:Department of Food Science
國立臺灣海洋大學:食品科學系
Keywords: 石蓴;活性胜肽;生物資訊工具;DPP-IV抑制劑;ACE 抑制劑
Ulva lactuca;bioactive peptide;bioinformatics;DPP-IV inhibitors;ACE inhibitors
Date: 2016
Issue Date: 2018-08-22T06:40:05Z
Abstract: 大型藻類根據其顏色一般可以區分為褐藻 (Phaeophyceae)、紅藻 (Rhodopyceae)以及綠藻 (Chloropyceae)。近年來,隨著植物蛋白的需求上升,大型藻類被認為是良好的植物蛋白來源。在本研究中選擇石蓴 (Ulva lactuca) 作為實驗素材,因為其在綠藻當中具有相對高含量的蛋白質 (3.45-29.00%, dry basis) 。本研究之目的為評估從石蓴萃取蛋白質的方法、利用聚丙烯醯胺膠體電泳 (SDS-PAGE) 與蛋白質體學技術鑑定分子量特徵以及使用生物資訊工具 (UniProt/KB與BIOPEP資料庫) 預測活性胜肽及其潛在生物活性。本研究中採用二種方法從綠藻中萃取蛋白質:水溶液或鹼溶液 (0.1M NaOH) 萃取後,以10% 三氯醋酸/丙酮溶液沉澱蛋白質。鹼溶液萃取法 (17.29 ± 4.38%) 相較於水溶液萃取法 (14.77 ± 6.86%) 具有最高的產率。SDS-PAGE鑑定出分子量為34.6 kDa (A1) 、23.7 kDa (A2) 、10 kDa (A3) 和13.4 kDa (B1) 的蛋白質次單元。電泳結果中的band A1、band A2和band B1與已被發表的石蓴蛋白質序列相似;(A1) elongation factor Tu (分子量:32.7 kDa)、(A2) ferritin (分子量:22.2 kDa)、(B1) cytochrome b6/f complex subunit IV (分子量:12.8 kDa)。接著以BIOPEP資料庫進行in silico分析並預測具有生理活性的活性胜肽。預測結果顯示石蓴蛋白具有最高的dipeptidyl peptidase (DPP)-IV抑制活性胜肽頻率 (occurrence frequency) 其次為angiotensin- converting enzyme (ACE) 抑制活性、抗氧化、抗健忘、抗血栓以及一些抗菌和神經胜肽 (neuropeptide)。以BIOPEP的“enzyme action tools”功能進行27種蛋白酶的in silico水解,其結果顯示以ficain (elongation factor Tu), papain (ferritin) 和 proteinase K (cytochrome b6/f complex subunit IV) 進行水解,相較於其他蛋白酶能夠釋放相當多預測的活性胜肽。
Based on pigmentation, macroalgae can be classified as brown algae (Phaeophyceae), red algae (Rhodopyceae) and green algae (Chloropyceae). Recently, macroalgae are regarded as great potential sources of plant proteins with increasing need. In this study, green algae Ulva lactuca was selected due to its protein content (3.45–29.00 %, dry basis), which is moderately high in green algae group. The objectives were to evaluate the protein extraction methods in Ulva lactuca, to identify molecular characteristic using polyacrylamide gel electrophoresis (SDS–PAGE) and proteomics techniques, to predict bioactive peptides and the potential biological activities in using bioinformatics tools (UniProt/KB and BIOPEP database). In order to isolate the protein fraction from green algae, two extraction methods were performed: aqueous and alkaline (0.1 M NaOH) extraction, and precipitate using 10% TCA/acetone. Alkaline extraction had the highest yield (17.29 ± 4.38%) than aqueous extraction (14.77 ± 6.86%). With SDS-PAGE characterization, molecular weight (MW) of protein subunits were identified as follows: 34.6 kDa (A1), 23.7 kDa (A2), 10 kDa (A3) and 13.4 kDa (B1). The protein bands A1, A2 and B1 were comparable with reported Ulva sp protein sequence; (A1) elongation factor Tu (MW: 32.7 kDa), (A2) ferritin (MW: 22.2 kDa) and (B1) cytochrome b6/f complex subunit IV (MW: 12.8 kDa) protein. The subsequent in silico analysis was carried out using BIOPEP database to predict bioactive peptides with reported biological activities. The predictive results showed that the Ulva lactuca proteins have the highest occurrence frequency of potential bioactivities in dipeptidyl peptidase (DPP)-IV inhibitors, followed by angiotensin converting enzyme (ACE) inhibitors, antioxidative, antiamnestic, antithrombotic, and a small amount of antibacterial and neuropeptide. In silico hydrolysis using “BIOPEP” enzyme action tools” with 27 proteases revealed that hydrolysis with ficain (elongation factor Tu), papain (ferritin) and proteinase K (cytochrome b6/f complex subunit IV) released greater numbers of predicted bioactive peptides compared to others proteases.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010232071.id
http://ntour.ntou.edu.tw:8080/ir/handle/987654321/48980
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