Yeast Proteome Database

Gene names, predicted pi, and moleculai mass values are from the Yeast Proteome Database (YPD) (13). Peptide sequences were identified automatically and verified manually by using SEQUEST (12). ... 

Figure 1.1. Complex two-hybrid interaction networks. Two-hybrid interaction networks for proteins related to spindle pole body (A) and vesicular transport (B) are shown. Allows indicate two-hybrid interactions, beginning from the bait and ending at the prey. Double-headed allows mean that the interactions were detected bidirectionally. Note that arrows indicate the direction of two-hybrid interactions but not any biological orientation. Solid lines indicate known interactions recorded in the Yeast Proteome Database (14) but not yet detected by our two-hybrid screening [Refs in 45].

The first step in constructing a cell signaling network model is to generate an in silica interaction network. Signaling components of interest are identified, and data on binary interactions are extracted from the experimental literature. Often times, the creation of an entire interaction map for a large network is beyond the scope of one laboratory therefore, public databases have been created in which newly discovered proteins and/or protein interactions are deposited (57-63). However, often data are included from studies that cover a broad range of protein interactions, such as proteomic studies or yeast two-hybrid screens, and frequently contain many potential false positives or negatives. Thus, it becomes necessary to critically examine each reference to verify the interaction data reported, as this in silica network is the basis of further study. 

The discussion in this chapter focussed at developments in the field of protein characterization, and provided an overview of the technology developed to enable proteomics studies (Ch. 18). The strategies outline above have been applied to increasingly complex samples. Some examples are the detection and identification of human leucocyte antigen peptides related to the major histocompatibility complex [166], and the unattended identification of 90 proteins from the yeast Saccharomyces cerevisiae by means of an integrated workstation for LC-MS-MS under DDA and with database searching [34]. 

We see two major appearing frontiers for new kinds of molecular data. The first is proteomics (See Chapter 4 of volume 2) and metabolomics. With a combination of 2D gel, mass spectrometry, protein microarray and yeast-two-hybrid methods, a large amount of protein sequence, expression, and interaction data will be produced on a cell-wide level. On the one hand, bioinformatics has to address the challenge of interpreting these data. On the other hand, especially the protein interaction data will provide an interesting basis for probing deeper into the details of regulatory networks. Such data are collected in special protein interaction databases such as DIP [9,10] and BIND [11],... 

A sequence tag from an unknown protein allows a number of further options for characterisation. Even a short stretch of amino acid sequence provides a powerful means of interrogating a protein database, and may provide a useful alternative to PMF in poor quality samples that may have peptides derived from more than one protein. More advanced database searching systems will find proteins with homologous sequences to that of the peptide tag. A good example of this type of database searching system is BLAST (Basic Local Alignment Sequence Tool), (Altschul et al., 1997). A modified form of this (MS BLAST) was used in conjunction with PMF to characterise the proteome of the yeast Pichia pastoris, whose genome has not been fully sequenced... 

---