蛋白质表达规模化鉴定-针对SDS凝胶条带的液质联用质谱鉴定
产品名称： 蛋白质表达规模化鉴定-针对SDS凝胶条带的液质联用质谱鉴定
英文名称： LC-MSMS
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北京蛋白质组研究中心生物质谱实验室
- 联系人 :
- 地址 : 北京市昌平区生命园路38号
- 邮编 : 102206
- 所在区域 : 北京
- 电话 : 134****6462
- 传真 :
- 邮箱 : biomsbprc@163.com
生物样本全蛋白鉴定
——SDS-PAGE凝胶电泳结合QE质谱分析
蛋白质的最终定性对临床蛋白质组学是至关重要的。凭借SDS-PAGE技术将生物样本进行初步分离，应用 QE质谱鉴定对一次凝胶分离的生物样本进行液相分离，同时获得质谱信息，通过数据检索分析得到生物样本全蛋白鉴定结果。检测过程操作更为便捷，得到的信息覆盖范围更为全面。
实验设计：样本制备、蛋白质定量、SDS-PAGE凝胶电泳、胶内酶切、LTQ质谱分析、数据检索
技术简介：
线性离子阱四级杆质谱仪（linear trap quadrupole）
LTQ质谱设备拥有高质谱精确度的MS/MS性能，通过质谱分析和数据库检索实现生物制品的全蛋白鉴定。提供的多参数描述为特定疾病相关的蛋白质网络定性，更真实可靠的解释病因、诊断和治疗疾病的过程。
Ø 应用于蛋白质组学研究，如蛋白鉴定和差别表达的定量分析、生物标记分析研究、翻译后修饰等
Ø 高通量的药物分析，痕量药代、药动分析等
应用实例：
Reference
Wantao Ying , Yangjun Zhang,Songfeng, Jinglan Wang, Yun Cai, Xiaohong Qian,et.al.
A Dataset of Human Fetal Liver Proteome Identified by Subcellular Fractionation and
Multiple Protein Separation and IdentificationTechnology.Molecular & Cellular Proteomics 2006,5,1703-1707
Introduction
By doing the analysiswith integrated protein, expressed sequence tag, and genome datasets, 223 proteins and 15 peptides were complementarily identified with high quality MS/MS data.
Methods
1. TECHNOLOGY FOR PROTEIN EXPRESSION PROFILE SDS-PAGE Separation—Proteins were separated by SDS-PAGEwith different cross-linking percentages, 15, 10, and7.5%, to obtain a full representation of proteins ranging from 5 kDa to more than 300 kDa. After separation, these gels were stained with Colloidal Coomassie Blue R250, and the gel lanes were manually excised from loading position to the bottom of the gel. After in-gel digestion with trypsin, the extracted peptide mixtures were loaded onto nanoscale LCESI-Q-TOF MS or micro-LC-ion trap MS systems for protein identifications.
2. DATABASE QUERIES AND PROTEIN IDENTIFICATIONS
When the data produced by ESI-Q-TOF MS was searched against the IPI_human_2.33 protein database by MASCOT,mass tolerance of peptide precursor and its daughter ions
was set at 0.2 Da in peptide sequence tag, and one possible missed cleavage for trypsin digestion was selected. Protein identifications were performed based on probability-based
Mowse scoring algorithm with a confidence level of 95%.
3. PROTEIN EXPRESSION PROFILE OF HFL
4. COMPLEMENTARY IDENTIFICATION AND NOVEL PROTEIN MINING