Systems Analysis of Arrestin Pathway Functions - Progress in Molecular Biology and Translational Science - page 429

contrast between global proteomic datasets and those from array experi-
ments is the routine and repeatable expectation of the inclusion of certain
datapoints, that is, protein identities. Gene arrays provide a reproducible
experimental platform, while the recovery and mass spectrometric identifi-
cation of the same protein between different experiments is often unlikely.
The use therefore of signaling pathway bioinformatics, which can infer
function from a variety of related proteins rather than individual protein
identities, in such experiments may be paramount for the eventual applica-
tion of proteomic workflows for GPCR signaling analysis.
In contrast to gene arrays, the primary concern for MS-based workflows
is the speed and consistency of protein detection and identification. Using
the two most commonly used MS platforms, time-of-flight (TOF) or linear
ion-trap tandem MS (LC-MS 2 ), the identification of proteins in the sample
is based upon the fragmentation ion spectrum (MS 2 -spectrum) of a specific
peptide ion that is broken down into its constituent components in a gas
filled collision cell. Due to the enormous complexity of peptides composed
of 20 amino acids, a large number of MS 2 spectra do not contain sufficient
identity information to allow definitive peptide identification. To minimize
false peptide identification, strict filtering criteria are required, which can be
enforced, for example, by searching retrieved MS 2 spectra against a compos-
ite of both “target” and “decoy” (often reverse peptide alignments) sequence
databases. 106 The correct correlation and attribution of an MS 2 spectrum to
its originating peptide sequence, followed by eventual protein matching and
identification, is the first and central step in proteomic data processing.
Numerous computational approaches and software tools have been devel-
oped to automatically assign candidate peptide sequences to fragment ion
spectra, for example, SEQUEST, MASCOT, ProteinProspector, or
ProbID. 107-110 These computational approaches involve database interroga-
tion, where peptide sequences are identified by correlating acquired frag-
ment ion spectra with theoretical spectra predicted for each peptide
contained in a protein sequence database or by correlating acquired fragment
ion spectra with libraries of experimental MS 2 spectra identified in previous
curated experimental datasets.
Initially, MS-based proteomic analyses of biological samples were
restricted to qualitative analyses. However, with the advent of multiple
labeling or label-free quantitation platforms, cellular signaling proteomics
has been converted to a primarily quantitative process. Perhaps, the most
commonly used quantitative MS workflows are facilitated by peptide or
protein mass labeling. Mass tag labeling techniques, for example, iTRAQ