MS/MS Identification Strategies for the Arginine and Lysine-Deficient Proline-Rich Glutamine-Rich Wheat Storage Proteins - Dupont, F. (Association of Biomolecular Resource Facilities, 2010)
RP-103 Tandem mass spectrometry (MS/MS) is routinely used to identify proteins from a variety of sources. Generally, proteins are identified by comparing spectra generated by MS/MS to in silico generated peptide fragmentation patterns from protein sequence databases. Wheat gluten proteins, consisting of gliadins and glutenins, are particularly difficult to distinguish by MS/MS as the major groups each contain many individual proteins with similar sequences including repetitive motifs rich in proline and glutamine. Most have few cleavable tryptic sites. Thus tryptic peptides may not provide sufficient information for MS/MS identification. Additionally, wheat has a very large partially-sequenced genome with only 14,000 complete protein sequences in the current NCBInr release. We optimized MS/MS methods for the identification of wheat gluten proteins by using chymotrypsin and thermolysin as well as trypsin to fragment the proteins. In addition, databases were constructed that included protein sequences derived from contigs from several wheat EST assemblies as well as gluten protein sequences translated from consensus sequences of contigs assembled from ESTs of the cultivar under study (Butte 86). Mascot and X Tandem were used to interrogate the different databases that had decoy databases appended. The results were analyzed and displayed using Scaffold. Combining the results from three enzymes and two search engines increased the number of proteins identified and their % coverage. As database size increased above 30,000 sequences there was a decrease in proteins identified. Smaller database size ∼ 3,000 resulted in increased identification of Butte 86 proteins. The type of decoy database also influenced the number and identity of the proteins identified. When the decoy was randomized target-sequences or sequences from an unrelated organism more Butte 86 proteins were identified. Using 3 enzymes, two search engines and careful database selection allowed increased individual protein identification.