期刊文献+

燕麦种子蛋白质组的GeLC-MS/MS分析 被引量:5

GeLC-MS/MS Analysis of Oat Seed Proteome
下载PDF
导出
摘要 采用十二烷基磺酸钠-聚丙烯酰胺凝胶(SDS-PAGE)电泳结合液相色谱串联质谱方法(GeLC-MS/MS)对燕麦(Avena sativa L.)品种白燕2号种子不同溶解性的蛋白质进行分离鉴定,以期建立其种子蛋白质表达谱,为探讨干燥燕麦种子储藏及萌发的生理生化机理提供基础。结果表明:GeLC-MS/MS方法鉴定出溶解于超纯水、2.5%NaCl和70%乙醇的燕麦种子蛋白质分别为40,33和15个,共54个非冗余蛋白,其中29个蛋白至少可溶于2种溶剂。通过生物信息学软件对鉴定蛋白的分子功能、细胞组成和生物学功能进行预测,其中40个蛋白质功能得到明确的预测,涉及细胞过程、刺激应答及酶调节活性等12类。这些蛋白质与燕麦种子成熟干燥时期生命活动有关,为进一步从蛋白质水平探索燕麦种子生理代谢机理提供了理论依据。 For exploring the mechanism of biochemistry,the proteins of dry oat seeds(Avena sativa L.'baiyan No.2')were extracted with MillQ water,2.5% NaCl solution and 70% ethanol then identified using SDS-PAGE and mass spectrometery approach(LC-MS/MS).Results showed that fifty-four proteins were identified by LC-MS/MS,hereinto;twenty-nine proteins could be dissolved in two or three kinds of solutions.Forty,thirty-three and fifteen proteins could be dissolved in MillQ water,2.5% NaCl solution and 70% ethanol,respectively.The molecular function,cell components and biological pathways of fifty-four proteins were predicted by GO database.The protein functions of forty proteins were identified involving in twelve major categories including stimulate response,enzyme regulator activity,cellular processes and so on.These proteins affected the activity of dried ripe oat seed.These results provided information to explore the biochemistry mechanism of oat seeds at the protein level in the future.
出处 《草地学报》 CAS CSCD 北大核心 2012年第1期108-115,共8页 Acta Agrestia Sinica
基金 安徽省博士后基金资助
关键词 燕麦 蛋白质组 质谱 Oat Proteome Mass spectrometry
  • 相关文献

参考文献20

  • 1Wilkins M R, Pasquali C, Appel R D. From proteins to proteome: large scale protein identification by two-dimensional electrophoresis and amino acid analysis[J]. Nature Biotechnology, 1996,14 :61-65.
  • 2顾洁.电力系统中长期负荷的可变权综合预测模型[J].电力系统及其自动化学报,2003,15(6):56-60. 被引量:28
  • 3Skylas D J, Mackintosh J A, Cordwell S J, et al. Proteomeapproach to the characterisation of protein composition in the developing and mature wheat-grain endosperm [J]. Journal of Cereal Science, 2000, 32:169-188.
  • 4Chow T W S, Leung C T. Neural network based short-term load forecasting using weather compensation[J]. IEEE Trans on Power Systems, 1996, 11(4): 1736-1742.
  • 5Alfuhaid A S, El SayedM A, Mahmoud M S. Cascaded artificial neural networks for short-term load forecasting[J]. IEEE Trans on Power Systems, 1997, 12(4): 1524-1529.
  • 6Mohan Saiti L , Kumar Soni M . Artificial neural network based peak load forecasting using conjugate gradient methods[J]. IEEE Trans on Power Systems, 2002, 17(3): 907-912.
  • 7Xie Z S, Wang J Q, Cao M L, et al. Pedigree analysis of an elite rice hybrid using proteomic approach[J]. Proteomics, 2006,6 (2): 474-486.
  • 8张大海,江世芳,毕研秋,邹贵彬.基于小波神经网络的电力负荷预测方法[J].电力自动化设备,2003,23(8):29-32. 被引量:21
  • 9李春梅,杨守萍,盖钧镒,喻德跃.野生大豆与栽培大豆种子差异蛋白质组学研究[J].生物化学与生物物理进展,2007,34(12):1296-1302. 被引量:12
  • 10Neuhoff V, Arold N, Taube N, et al. Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using Coomassie Brilliant Blue G-250 and R-250 [J]. Electrophoresis, 1988,9(6) :255-262.

二级参考文献55

共引文献154

同被引文献91

引证文献5

二级引证文献23

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部