Biological Mass Spectrometry. Development of Proteome Analysis Systems for Efficient Expression Profiling: Towards High Throughput Identification and Quantitation of Disease Target Proteins.
Biological Mass Spectrometry. Development of Proteome Analysis Systems for Efficient Expression Profiling: Towards High Throughput Identification and Quantitation of Disease Target Proteins.
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生物质谱。
DOI：
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
T. Nishimura
中科院分区：
文献类型：
--
作者：
T. Kawakami;H. Anyoji;T. Nishimura
In the post-genome era, there is an immediate requirement for the analysis of proteome, which gives a great impact on the methodology of disease diagnosis and drug discovery in medicine and pharmaceutical sciences. Mass spectrometry (MS) is essential for high throughput and accurate detection/identification of potential drug target proteins in low abundance localized to intrinsic functions at the extremely early stage of disease progression. Conventional proteome analysis strategy with two-dimensional electrophoresis (2-DE) techniques is not compatible for the drug discovery process and MS analysis, mainly due to poor solubility of many hydrophobic proteins in the current 2-DE sample buffers and its drawbacks including being low reproducibility, time consuming and very labor intensive. Higher throughput identification of proteins, including membrane-binding ones, was accomplished by introducing one-dimensional (1D) sodium dodecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE) for protein fractionation coupled with nano-liquid chromatography (nanoLC)/nano-electrospray ionization (nanoESI)/ion trap (IT)-MS/MS. Toward fully automated proteome analysis, a five-dimensional (5D)-LC-based system was constructed, consisting of two-dimensional LC for protein fractionation, followed by proteolytic fragmentation of each protein fraction, and two-dimensional nanoLC for peptide fractionation directly linked with nanoESI/IT-MS/MS. A combination of the isotope-coded affinity tags (ICAT) assay with 1D-SDS-PAGE fractionation or the 5D-LC proteome workstation followed by nanoESI/IT-MS/MS data-dependent measurements under the dynamic exclusion mode will bring a promising approach for identification and quantification of proteins expressed in a disease progression. This strategy leads to the high throughput discovery of key target components related to disease progression or origin.
影响因子：
7.4
作者：
Russell, WK;Park, ZY;Russell, DH
通讯作者：
Russell, DH