Proteome automation

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. 

Coldham, N. G. Woodward, M. J. Characterization of the Salmonella typhimurium proteome by semi-automated two dimensional HPLC-mass spectrometry Detection of proteins implicated in multiple antibiotic resistance. J. Proteome Res. 2004, 3,595-603. 

Dunlop, K. Y. Li, L. Automated Mass Analysis of low-molecular-mass bacterial proteome by liquid chromatography-electrospray ionization mass spectrometry. J. Chromatogr. A 2001, 925,123-132. 

The present chapter does not consider analysis of extracted protein biomarkers but rather focuses on strategies for rapid chemotaxonomic analysis of intact microorganisms with automated sample manipulation. Rapid means less than 5 minutes. Advantages of the application of bioinformatics and proteomics strategies for rapid identification of microorganisms include the following ... 

Davis, M.T., Beierle, J., Bures, E.T., McGinley, M.D., Mort, J., Robinson, J.H., Spahr, C.S., Yu, W., Luethy, R., Patterson, S.D. (2001). Automated LC-LC-MS-MS platform using binary ion-exchange and gradient reversed-phase chromatography for improved proteomic analyses. J. Chromatogr. B Biomed Sci. Appl. 752, 281-291. 

Field, H.I., Fenyo, D., Beavis, R.C. (2002). RADARS, a bioinformatics solution that automates proteome mass spectral analysis, optimises protein identification, and archives data in a relational database. Proteomics 2, 36 17. 

Although classic two-dimensional gel electrophoresis provides exquisite peak capacity, it suffers from several limitations. First, the technology is labor-intensive and difficult to automate, which hampers applications to large-scale proteomics analyses (Hanash, 2000). 

Michels, D.A., Hu, S., Schoenherr, R.M., Eggertson, M.J., Dovichi, NJ. (2002). Fully automated two-dimensional capillary electrophoresis for high sensitivity protein analysis. Mol. Cell. Proteomics 1, 69-74. 

Vinarov, D.A. and Markley, J.L. (2005) High-throughput automated platform for nuclear magnetic resonance-based structural proteomics. Expert Review of Proteomics, 2 (1), 49-55. 

Lopez MF et al. High-throughput profiling of the mitochondrial proteome using affinity fractionation and automation. Electrophoresis 2000 21 3427-3440. Reinheckel T et al. Adaptation of protein carbonyl detection to the requirements of proteome analysis demonstrated for hypoxia/reoxygenation in isolated rat liver mitochondria. Arch Biochem Biophys 2000 376 59-65. 

Computational methods have been applied to determine the connections in systems that are not well-defined by canonical pathways. This is either done by semi-automated and/or curated literature causal modeling [1] or by statistical methods based on large-scale data from expression or proteomic studies (a mostly theoretical approach is given by reference [2] and a more applied approach is in reference [3]). Many methods, including clustering, Bayesian analysis and principal component analysis have been used to find relationships and "fingerprints" in gene expression data [4]. 

H. I. Field, D. Fenyo, and R. C. Beavis. RADARS, a Bioinformatics Solution that Automates Proteome Mass Spectral Analysis, Optimises Protein Identification, and Archives Data in a Relational Database. Proteomics, 2, no. 1 (2002) 36-47. 

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