Identification technologies

Wolters, D. A. Washburn, M. P. Yates, J. R., 3rd. An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73, 5683-5690. 

Table 5.1 lists several heart-cut and comprehensive techniques. Heart-cut 2DLC is very common and has great application for the increased resolution of one or several components from the first dimension (Augenstein and Stickler, 1990 Majors, 1980 Pasch et al., 1992 and Dixon et al., 2006). Heart-cut 2DLC for the analysis of polymers is often referred to as cross-fractionation (Balke and Patel, 1980). Protein digest analysis with MS/MS identification has been called multidimensional protein identification technology or MUDPIT. This is described in detail in Chapter 11. 

There exist essentially three categories of SCX/RP/MS/MS approaches. In one approach, SCX is run off-line followed by on-line RP/MS/MS (Fig. 11.1). In the offline SCX approach, fractions do not directly elute onto RP material but rather are collected. In one of the two in-line approaches, SCX is run in line with RP/MS/MS using different columns for SCX and RP (Fig. 11.2). In the multidimensional protein identification technology approach (MudPIT), SCX and RP are run in line in the same column, and this column serves as the ion source for a tandem mass spectrometer (Fig. 11.3). Both the in-line approaches are true SCX/RP/MS/MS approaches the first approach could be abbreviated as SCX—RP/MS/MS where... 

Washburn, M.P., Wolters, D., Yates, J.R., 3rd (2001). Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242-247. 

This multidimensional protein identification technology (MudPIT) specifically incorporates a strong cationic exchange (SCX) column in tandem with an RP column to achieve maximal resolution and exquisite sensitivity. MudPIT is effective for studying complex proteomes such as mammalian cellular samples. It has been applied to large-scale protein characterization with identification of up to 1484 proteins from yeast in a single experiment.12... 

E. Rhodes, C. E. Dickerman, C. W. Peters, Associated-Particle Sealed-Tube Neutron Probe for Characterization of Materials, ANL/B-E/CP-78949, Active Probe Technologies Conference on International Symposium on Substance Identification Technologies, 4—8 October 1993, Innsbruck, Austria. 

Multi-dimensional protein identification technology (MuDPIT). 342... 

More importantly insoluble proteins, such as membrane-bound and nuclear proteins, are under-represented within 2DE analysis. However, membrane proteins can be identified by MS following a IDE separation. Unfortunately, hydrophobicity is the most common reason for poor representation of abundant proteins and seems set to be with us for quite some time (Santoni et al., 2000 Rabilloud, 2002). Emerging proteomic methods without the use of 2DE are being developed, such as isotope-coded affinity tagging (ICAT) and multi-dimensional protein identification technology (MuDPIT). 

Bioinformatics tools involving computer-based statistical analyses are essential for data management and analysis. When a complex biological sample containing thousands of different proteins is analyzed by multifaceted approaches, such as multidimensional protein identification technology, the identification of the proteins in the mixture is extremely complicated. Even multiple peptide identification methods, such as using both MS and... 

MS, either after electrophoretic separation and proteolysis or independently of 2DE [e.g., as in Multidimensional Protein Identification Technology (MudPIT), Ref. 21, also see Sec. 3.1.3), as a stand-alone system for protein identification. 

Proteomic platforms combine separation and identification technologies that can be completed as an experiment in a timely fashion. Proteomic platforms strive to accomplish five critical objectives (1) a high level of information about one or more protein attributes (2) a large number of samples per analysis session (sample throughput) (3) quantitative and comparative protein expression among samples (4) timely analysis of raw and processed data and (5) use of a discovery-oriented or open platform system. 

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