Increasing Throughput and Data Quality for Proteomics
With the availability of microbial and mammalian genomes combined with dramatic improvements in bioanalytical methods, high-throughput analysis of transcriptomes and proteomes has become a reality for academic and industrial laboratories alike. New technologies have resulted in the discovery of a multitude of novel cellular pathways and interconnective regulatory mechanisms. For instance, the number of drug targets has grown from approximately 500 to the thousands in a short amount of time. While the information from post-genomics techniques is useful in its own right, it does not necessarily accelerate discovery, this is partly because of data management constraints. Bottlenecks also include quality of sample preparation, identification of low abundance compounds, uninterrupted unattended operations, and successful matches of the results with information available in databases.
In this study, we addressed these issues in order to develop robust methods for the discovery of compounds relevant to a product oriented biotechnology environment. Specifically, we developed pre-fractionation methods, deglycosylation protocols, non-radioactive isotopic labeling methods, and improvements in matrix assisted laser desorption (MALDI) matrix techniques to visualize and identify low abundance proteins from complex samples and applied them to a semi-automatic two-dimensional electrophoresis (2-DE) line followed by MALDI time of flight mass spectrometry (MALDI-TOF/MS). The complex exoproteome of a filamentous fungus, Trichoderma reesei, an important production organism for a number of biomass related applications, served as a model system to develop and fine tune most of the methods.
KeywordsMALDI Target Subtilisin Protease Peptide Recovery Glycan Group Mass Spectrometry Signal Intensity
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