Protein identification and protein expression profiling of Saccharomyces cerevisiae grown under low and very high gravity conditions

Proteomics is the analysis of the total complement of proteins expressed by a cell or organism grown under a specified condition. The obtained protein profile would provide a better understanding of phenotypic characteristics of a cell grown under pre-determined conditions. Mass spectrometric-based protein analysis is currently the standard method in proteomic studies; however, there are many limitations associated with its application. The major objectives of this study included the development of a strategy to analyze the confidence of identified proteins and the development of an algorithm to interpret the experimentally obtained mass spectral data. A two-step strategy was developed to analyze the confidence of identified proteins. In the first step, the proteins identified by a single protein identification tool were classified into two groups: high confidence proteins that were identified by unique peptides, and low confidence proteins that were identified by non-unique peptides. In the second step, the proteins identified by different tools (e.g., SEQUEST and Mascot in our work) were cross-compared. After integrating the two-step analysis, the identified proteins were classified into four levels of confidence. The proteins that were identified by the presence of unique peptides and that were commonly identified by different tools were grouped into the highest confidence level - Level 4. Even though the number of proteins in Level 4 was reduced significantly, the conclusions drawn from the proteins were more reliable. According to the operation of tandem mass spectrometry and the characteristics of the peptides generated by site-specific protease digestion, a two-pass approach for identifying the species-specific proteins was developed. The approach can find all possible peptides corresponding to a precursor ion and gives detailed matching information of each peptide candidate to the experimental product ion series. According to the total number of matched product ions, the total number of matched b- and y- ions, and the contiguity characteristic of identified product ions, the peptide candidates were ranked decreasingly from the most probable to the least. Combined with the concept of unique peptide, the obtained most probable peptide can then be used to predict proteins existing in the original sample. The developed two-pass approach and two-step strategy were then used to study the protein profiling of Saccharomyces cerevisiae cultivated in various gravity conditions (10 and 300 g glucose/l) in order to investigate the changes in central metabolic pathways of S. cerevisiae. Our fermentation data indicated that the higher glucose contents would result in lower cell growth and higher ethanol production (e.g., high ethanol concentration in fermentation broth). However, the relative ethanol yield as related to the glucose consumption was lower under higher glucose concentrations. The protein profile showed that a higher flux of nutrient was channelled into the pentose phosphate pathway when S. cerevisiae was grown under a high glucose concentration. The reason for this phenomenon might be that the cell needs more reducing power (e.g., NADPH) for the synthesis of macromolecules such as proteins, nucleic acids, and lipids. These materials are essential to the cell in order to modify its structure (e.g., cell wall), to survive osmotic stress and to replicate