Proteomics: Praise, Promises and Problems Kelvin H. Lee Cornell ...

Proteomics: Praise, Promises and Problems Kelvin H. Lee Cornell University Chemical Engineering [email protected] www.leelab.org The drive to better understand the relationship between genome sequence information, gene regulation and expression and biological phenomena simultaneously challenges several disciplines and creates many new opportunities. As high-throughput DNA sequencing technology is applied to an ever increasing number of organisms, ones attention turns to a more detailed understanding of gene regulation and expression. Indeed, the past several years have seen an unprecedented amount of effort and interest devoted to new technologies for quantitative and semi-quantitative mRNA analysis such as serial analysis of gene expression and microarrays. At the same time, there has been relatively little development of new technologies for "high-throughput" simultaneous protein expression - particularly in technologies which are discovery-based. This area is clearly an important one and it poses many challenges. Unlike nucleic acids which vary largely in length and in relatively few other characteristics (sequence, secondary structures and other features are certainly important), proteins, and the amino acids which result in protein sequences, are relatively diverse. Indeed, no current technology can reliably resolve all of the proteins of various hydrophobicities that are expressed in a particular cell or organism. In as much as the Human Genome Initiative has truly benefited from an initial effort to define and to develop improved technologies for generating DNA sequence information before a concerted effort to sequence the Genome was mounted, we should take time to both develop new technologies for assessing the state of the proteome - which includes an analysis of protein expression, post-translational modifications and function - while also applying these tools to systems which lend themselves well to the development of predictive mathematical models. We will consider generally accessible state-of-the art methods for proteome measurements and consider their strengths and limitations.