Interpretation of Shotgun Proteomic Data

Abstract

The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the state of protein sequence databases and their role in shotgun proteomic analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, the integration of proteomic and transcriptional data, and the development of a computational infrastructure for the integration of multiple diverse datasets. Molecular & Cellular Proteomics 4:1419 -1440, 2005.An explicit goal of proteomics is the identification and quantification of all the proteins expressed in a cell or tissue (1).Although not yet at the levels of data throughput and automation achieved in other genomic analyses such as DNA sequencing or microarray gene expression analysis, global protein profiling methods are rapidly evolving. This has been possible because of recent improvements in MS instrumentation, protein and peptide separation techniques, computational data analysis tools, and the availability of complete sequence databases for many species. As a result, analysis of complex protein mixtures using shotgun proteomics, a strategy based on the combination of protein digestion and MS/ MS-based peptide sequencing (2-4), has become widely adopted. The method allows protein identifications and, when combined with stable isotope labeling, quantification of the changes in the protein expression levels for hundreds of proteins in a single experiment (1).Compared with other MS-based proteomic technologies such as intact proteins sequencing (5, 6) or 2D 1 gel-based protein analysis (7), shotgun proteomic analysis has achieved a relatively high throughput. This is the result of a combination of several factors. Proteolytic digestion of proteins into shorter peptides simplifies MS/MS sequencing (peptides are easier to fragment in the mass spectrometer than intact proteins), whereas elimination of the 2D gel-based separation at the protein level simplifies sample handling and increases the overall data throughput. At the same time, computational analysis and interpretation of the data become more challenging (8 -13). The first and foremost computational challenge is the need to process large volumes of acquired MS/MS data with the purpose of identifying peptides that gave rise to observed spectra. This ch...

A workflow to increase the detection rate of proteins from unsequenced organisms in high‐throughput proteomics experiments

“…The differences between the precision/recall values at the spectrum and at the protein level illustrate the protein inference problem, which results from the loss of connectivity between peptide and protein [29]. Additional filter steps may be required to increase the reliability of protein assignments on the basis of MS-BLAST alignments (see also below for an example).…”

The mzqLibrary – An open source Java library supporting the HUPO‐PSI quantitative proteomics standard

“…First, a mapping from peptides to proteins is obtained from the input file, and all peptides are classified as either “unique” (mapping to only a single protein accession), or “common” (mapping to same‐set or subset groups of proteins) 32. Peptides that are “conflicted” (mapping to multiple protein accessions that have unique peptides) are removed.…”

Capture and analysis of quantitative proteomic data

“…There are a large variety of excellent tools available, which are targeted in the main towards particular approaches, instruments, vendors, third-party software and/or formats [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Table 1 summarises existing freely available quantitative proteomic software.…”

“…Similarly, other quality control tests may also be applied to improve the results. Firstly, certain peptides will not be unambiguously assigned to a single protein and may occur in several unrelated proteins [36]. It is therefore prudent to omit these degenerate peptides from the final protein ratio calculation as they may be subject to error receiving signal from multiple protein species.…”