Identification of yeast proteins from two-dimensional gels: working out spot cross-contamination

• Published in: Electrophoresis Vol. 19; no. 11; p. 1920
• Main Authors: Parker, K C, Garrels, J I, Hines, W, Butler, E M, McKee, A H, Patterson, D, Martin, S
• Format: Journal Article
• Language: English
• Published: Germany 01.08.1998
• Subjects: Acrylic Resins; Databases, Factual; Electrophoresis, Gel, Two-Dimensional - methods; Fungal Proteins - analysis; Humans; Keratins - analysis; Methylation; Peptides - metabolism; Pyruvate Decarboxylase - analysis; Saccharomyces cerevisiae - chemistry; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods; Trypsin
• ISSN: 0173-0835
• DOI: 10.1002/elps.1150191110

With the complete sequence of the yeast genome now available, efforts by many laboratories are underway to identify each of the spots on two-dimensional (2-D) gels corresponding to the most abundant yeast proteins. The high mass accuracy now attainable using matrix assisted laser desorption/ionization (MALDI)-mass spectrometry equipped with delayed extraction simplifies the process of identification, such that many spots can be unambiguously identified in a short period of time merely by using peptide mass fingerprinting and generally available database matching programs. Although it is not always possible to match spots between gels run by different laboratories, proteins generally yield the same abundant proteolytic fragments when tryptic digestions are performed. Databases containing these signature peptides not only simplify the task of reidentifying proteins from different gels, but also make it possible to identify small amounts of cross-contaminating proteins from different spots, as well as common extraneous contaminants such as human keratins. In this paper, we present data on the identification of > 20 previously unreported yeast proteins from 2-D gels. Some novel proteins were identified from randomly analyzed spots. Focusing on 14 spots in a narrow-pH-range gel, we demonstrate how organizing peak-table data and peptide match-list data into databases enables the identification of a larger percentage of the peaks.