Abstract
Proteomics, the study of cellular protein expression, is an evolving technology platform that has the potential to identify novel proteins involved in key biological processes in the cell that may serve as potential drug targets. While proteomics has considerable theoretical promise, individual cells/tissues have the potential to generate many millions of proteins while the current analytical technologies that involve the use of time-consuming two dimensional gel electrophoresis (2DIGE) and various mass spectrometry (MS) techniques are unable to handle complex biological samples without multiple high-resolution purification steps to reduce their complexity. This can significantly limit the speed of data generation and replication and requires the use of bioinformatic algorithms to reconstitute the parent proteome, a process that does not always result in a reproducible outcome. In addition, membrane bound proteins, e.g., receptors and ion channels, that are the targets of many existing drugs, are not amenable to study due, in part, to limitations in current proteomic techniques and also to these being present in low abundance and thus disproportionally represented in proteome profiles. Subproteomes with reduced complexity have been used to generate data related to specific, hypothesis-driven questions regarding target identification, protein-interaction networks and signaling pathways. However progress to date, with the exception of diagnostic proteomics in the field of cancer, has been exceedingly slow with an inability to put such studies in the context of a larger proteome, limiting the value of the information. Additionally the pathway for target validation (which can be more accurately described at the preclinical level as
target confidence building) remains unclear. It is important that the ability to measure and interrogate proteomes matches expectations, avoiding a repetition of the disappointment and subsequent skepticism that accompanied what proved to be unrealistic expectations for the rapid contribution of data based on the genome maps, to biomedical research.