Computational Mass Spectrometry–Based Proteomics

Proteomics is defined as the system-wide characterization of all the proteins in an organism in terms of their sequence, localization, abundance, post-translational modifications, and biomolecular interactions. Modern proteomic investigations are increasingly quantitative and comprehensive [1]. Examples include the relative quantification of over 4,000 proteins in haploid and diploid yeast, which identified the pheromone signaling pathway as enriched in differential abundance [2]; determination of siteand time-specific dynamics of more than 6,000 phosphorylation sites of HeLa cells stimulated with epidermal growth factor [3]; and characterization of 232 multiprotein complexes in Saccharomyces cerevisiae, which proposed new cellular roles for 344 proteins [4]. Such investigations are now successfully utilized in functional biology [5,6], genomics [7,8], and biomedical research [9]. Challenges of proteomic studies stem from the complexity of the proteome and to its broad dynamic range. For example, the human genome contains around 20,000 protein coding genes. Their translation, combined with splicing or proteolysis, yields an estimated 50,000–500,000 proteins, and over 10 million different protein forms can be derived by somatic DNA rearrangements and post-translational modifications [10]. The abundance of protein species in human plasma spans more than 10 orders of magnitude [11]. Unlike oligonucleotides, proteins cannot be amplified, and therefore the objectives of proteomics are achieved by sensitive and scalable technologies identifying and quantifying proteins [12]. The overall mass spectrometry–based proteomic workflow is summarized in Figure 1.