<p>The greenshell mussel Perna canaliculus is considered to be a suitable biomonitor for heavy metal pollution. This is due to their ability to accumulate and tolerate heavy metals in their tissues. These characteristics make them useful for identifying protein biomarkers of heavy metal pollution, as well as proteins associated with heavy metal detoxification and homeostasis. However, the identification of such proteins is restricted by the greenshell mussel being poorly represented in sequence databases. Several strategies have previously been used to identify proteins in unsequenced species, but only one of these strategies has been applied to the greenshell mussel. The objective of this thesis was to examine different protein identification strategies using a combined two-dimensional gel electrophoresis and MALDI-TOF/TOF mass spectrometry approach. The protein identification strategies used include a Mascot database search, as well as de novo sequencing approaches using PEAKS DB and SPIDER homology searches. In total, 155 protein spots were excised and a total of 68 identified. Fifty-six proteins were identified using a Mascot search against the Mollusca, NCBInr and Invertebrate EST database, with seven single-peptide identifications. De novo sequencing strategies identified additional proteins, with two from a PEAKS DB search and 10 from an error-tolerant SPIDER homology search. The most noticeable protein groups identified were cytoskeletal proteins, stress response proteins and those involved in protein biosynthesis. Actin and tubulin made up the bulk of the identifications, accounting for 39% of all proteins identified. This multifaceted approach was shown to be useful for identifying proteins in the greenshell mussel Perna canaliculus. Mascot and PEAKS DB performed equally well, while the error-tolerant functionality of SPIDER was useful for identifying additional proteins. A subsequent search against the Invertebrate EST database was also found to be useful for identifying additional proteins. Despite this, more than half of all proteins remained unidentified. Most of these proteins either failed to produce good quality MS spectra or did not find a match to a sequence in the database. Future research should first focus on obtaining quality MS spectra for all proteins concerned and then examine other strategies that may be more suitable for identifying proteins for species with poor representation in sequence databases.</p>
Clustering and Visualization of Large Protein Sequence Databases by Means of an Extension of the Self-Organizing Map