Proteomics aims at a large-scale characterization of localization, abundance, post-translational modifications, and biomolecular interactions of the proteins in an organism, with the goal of understanding their function. An extensive insight can be obtained by identifying and quantifying the components of biological mixtures. For example, a) In studies of biomolecular networks, partners interacting with a protein can help determine its function. It is possible to experimentally isolate protein complexes, e.g., using tag affinity purification. Identification of the components of this mixture helps determine potential interactors [1]. b) Post-translational modifications such as phosphorylation play an important role in regulating biological processes, e.g., cellular growth and signaling. Identification and quantification of phosphorylated proteins and their substrates helps elucidate complex signaling pathway phosphorylation events [2]. c) Molecular biomarkers, i.e., proteins for which changes in abundance are indicative of an early onset of a disease or a therapy response, are of interest in clinical research. Identifying and quantifying components of a biofluid such as serum helps detect proteins with such discriminative ability [3]. d) A goal of genome annotation is the discovery and validation of protein-coding regions. Identifying peptides and proteins in a cell helps confirm and improve the annotations at the translational level, e.g., by confirming the presence of intron boundaries or alternative splicings [4].

Mass spectrometry is a method of choice for protein identification and quantification due to its sensitivity and to the versatility of the instrumentation [5],[6]. A typical “bottom-up” workflow experimentally digests the proteins into a mixture of peptides with an enzyme such as trypsin. This is necessary, in part, because the sensitivity of the mass spectrometer is much higher for peptides than for proteins. The peptides are then injected onto a liquid chromatography (LC) column from which they elute sequentially. The eluted peptides are ionized and separated by the mass spectrometer according to their ratio of mass to charge (m/z) in a mass spectrum (MS).

The collection of mass spectra obtained at different elution times forms an LC-MS run shown in Figure 1A. Peaks in the run correspond to peptide ions; however, the sequence of amino acids underlying each peak is unknown. For identification, the mass spectrometer isolates the biological material from a peak (called precursor ion in this context), and subjects it to a high-collision energy. The energy breaks the peptide at different amide bonds, and the resulting fragments are separated according to their m/z in a secondary spectrum (called MS2, MS/MS, or tandem MS), shown in Figure 1B. Distances between peaks in the MS/MS spectrum are used to infer the peptide sequence of the parent LC-MS peak.

Peak intensity is related to the abundances of peptides, and can be used for relative quantification. With the label-free approach, a separate LC-MS run is obtained for each biological sample, and peaks are quantified and compared across runs. In stable isotopic labeling workflow, samples from different groups are labeled metabolically (e.g., in SILAC, where stable isotopes are included in the growth medium of an organism), or chemically (e.g., in ICAT or iTRAQ, where reacting chemical labels are applied after tryptic digestion). Several samples (e.g., one from each group) are then mixed, and their peaks are identified and quantified within the same run. Finally, a targeted workflow based, for example, on selected reaction monitoring (SRM) [7], increases sensitivity and specificity by monitoring signals from a list of predefined peptides.

The design of proteomic experiments, and subsequent analysis of the spectra, involves extensive computation and requires expertise at the intersection of computer science, engineering, and statistics. It presents exciting opportunities for both methodological and applied computational research.

Experimental design specifies how biological samples are selected and allocated in space and time during spectral acquisition. For example, a biomarker discovery project can produce biased conclusions if patients from different groups have different characteristics (such as prior medication), or their spectra are acquired under different conditions. Moreover, sample selection and allocation can be inefficient, and can undermine the ability to uncover the true differences between groups.

Statistical experimental design avoids bias and optimizes efficiency by using replication, randomization, and blocking, and by choosing an appropriate type and number of replicates [8]. The need for a statistical design of proteomic experiments is increasingly emphasized [9]. Specific choices require a statistical model that describes the spectra, and development of such models is an important area of research.

After spectral acquisition, the first computational task is to extract and store peak information. Unfortunately, most mass spectrometer vendors have their own proprietary formats. An advance has been made by implementing open XML-based formats (such as mzXML), and the associated converters and validators, to store this information and to make the subsequent analysis vendor-neutral [10]. These tools are available from http://www.proteomecommons.org. Efforts are invested, for example, by the Proteomics Standards Initiative (http://www.psidev.info), in further developments of XML formats.

An MS/MS spectrum such as in Figure 1A is generated by a series of peptide fragments. Thus, mass differences between neighboring MS/MS peaks are used to determine the underlying amino acid sequence. Typical approaches involve searches of an a priori–defined database, de novo identifications, and combinations of the two [11]. Here we focus on the database-based approach which compares each observed spectrum against entries in a database (Figure 2A). Several aspects of the procedure require consideration.

Quantitative proteomics monitors peptide and protein abundance across samples of multiple types. The goals are similar to other high-throughput experiments such as gene expression microarrays [23],[24]. A typical workflow (Figure 2) involves multiple steps [25].