The Next Dimension in Proteomics

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Cells, tissues, and organisms, including humans, are made of a variety of molecules, including proteins. Proteins are the 'molecular machines' responsible for everything a cell does, from replication to processing nutrients. A typical cell might have 10's of thousands of proteins doing myriad particular chemical 'jobs', including synthesizing or recycling other proteins, so the complement of proteins is constantly changing. The field of proteomics involves detecting as many of these proteins as possible, understanding how they change with time and in reaction to stress or chemical treatment. Knowing this information will allow bioscientists, from basic researchers to clinicians, to better understand what is occurring in the cell under conditions of malnutrition, disease, healthy aging, drought, or even pharmaceutical treatment. Developing better methods to measure the 'proteome' accurately and with sufficient detail is a key underpinning research goal which results in better science, better technology, and ultimately better health and healthier food. We have recently developed a new technology, called 2-dimensional mass spectrometry, which offers the capability to sequence all peptide/protein components in a sample, rather than a select few. We have demonstrated the technology works in several publications and in preliminary data on a whole proteomic analysis of yeast herein, but there are some particular challenges which we must address before the vision can be attained. This proposal aims to solve those challenges.Firstly, we will optimise our detection and fragmentation methods to increase protein sequence coverage. Secondly, we will fully characterise the technique to determine the sensitivity, resolution, accuracy, dynamic range, and capacity for quantitation and detection of protein modifications. Thirdly, we will develop a set of algorithms which will convert raw 2-dimensional MS data into protein sequence information and integrate those algorithms into a web-based data processing pipeline for automated proteomic data analysis. Finally, we will compare our newly developed 2-dimensional MS proteomic data sets with existing proteomic methodologies on the exact same set of samples, to clearly show the improvements (both delivered and potential) provided by doing proteomics with an extra dimension. The overall result of this project will be the development of protocols and methods for optimal proteomic analysis using this exciting new technology and a web-based data analysis pipeline which can process the raw 2-dimensional mass spectrometry data into protein sequence information for whole cells and tissues.