Large scale localization of protein phosphorylation by use of electron capture dissociation mass spectrometry.

We used on-line electron capture dissociation (ECD) for the large scale identification and localization of sites of phosphorylation. Each FT-ICR ECD event was paired with a linear ion trap collision-induced dissociation (CID) event, allowing a direct comparison of the relative merits of ECD and CID for phosphopeptide identification and site localization. Linear ion trap CID was shown to be most efficient for phosphopeptide identification, whereas FT-ICR ECD was superior for localization of sites of phosphorylation. The combination of confident CID and ECD identification and confident CID and ECD localization is particularly valuable in cases where a phosphopeptide is identified just once within a phosphoproteomics experiment

Power and limitations of electrophoretic separations in proteomics strategies

Proteomics can be defined as the large-scale analysis of proteins. Due to the complexity of biological systems, it is required to concatenate various separation techniques prior to mass spectrometry. These techniques, dealing with proteins or peptides, can rely on chromatography or electrophoresis. In this review, the electrophoretic techniques are under scrutiny. Their principles are recalled, and their applications for peptide and protein separations are presented and critically discussed. In addition, the features that are specific to gel electrophoresis and that interplay with mass spectrometry (i.e., protein detection after electrophoresis, and the process leading from a gel piece to a solution of peptides) are also discussed

Anatomy and evolution of database search engines — a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.acceptedVersio

De novo sequencing of MS/MS spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field. © 2011 Expert Reviews Ltd.The Turkish Academy of Science (TÜBA

Determination of Peptide and Protein Ion Charge States by Fourier Transformation of Isotope-Resolved Mass Spectra

We report an automated method for determining charge states from high-resolution mass spectra. Fourier transforms of isotope packets from high-resolution mass spectra are compared to Fourier transforms of modeled isotopic peak packets for a range of charge states. The charge state for the experimental ion packet is determined by the model isotope packet that yields the best match in the comparison of the Fourier transforms. This strategy is demonstrated for determining peptide ion charge states from “zoom scan” data from a linear quadrupole ion trap mass spectrometer, enabling the subsequent automated identification of singly- through quadruply-charged peptide ions, while reducing the numbers of conflicting identifications from ambiguous charge state assignments. We also apply this technique to determine the charges of intact protein ions from LC-FTICR data, demonstrating that it is more sensitive under these experimental conditions than two existing algorithms. The strategy outlined in this paper should be generally applicable to mass spectra obtained from any instrument capable of isotopic resolution

Computational Analysis of Mass Spectrometric Data for Whole Organism Proteomic Studies

In the last decades great breakthroughs have been achieved in the study of the genomes, supplying us with the vast knowledge of the genes and a large number of sequenced organisms. With the availability of genome information, the new systematic studies have arisen. One of the most prominent areas is proteomics. Proteomics is a discipline devoted to the study of the organism’s expressed protein content. Proteomics studies are concerned with a wide range of problems. Some of the major proteomics focuses upon the studies of protein expression patterns, the detection of protein-protein interactions, protein quantitation, protein localization analysis, and characterization of post-translational modifications. The emergence of proteomics shows great promise to furthering our understanding of the cellular processes and mechanisms of life. One of the main techniques used for high-throughput proteomic studies is mass spectrometry. Capable of detecting masses of biological compounds in complex mixtures, it is currently one of the most powerful methods for protein characterization. New horizons are opening with the new developments of mass spectrometry instrumentation, which can now be applied to a variety of proteomic problems. One of the most popular applications of proteomics involves whole organism high-throughput experiments. However, as new instrumentation is being developed, followed by the design of new experiments, we find ourselves needing new computational algorithms to interpret the results of the experiments. As the thresholds of the current technology are being probed, the new algorithmic designs are beginning to emerge to meet the challenges of the mass spectrometry data evaluation and interpretation. This dissertation is devoted to computational analysis of mass spectrometric data, involving a combination of different topics and techniques to improve our understanding of biological processes using high-throughput whole organism proteomic studies. It consists of the development of new algorithms to improve the data interpretation of the current tools, introducing a new algorithmic approach for post-translational modification detection, and the characterization of a set of computational simulations for biological agent detection in a complex organism background. These studies are designed to further the capabilities of understanding the results of high-throughput mass spectrometric experiments and their impact in the field of proteomics

A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet

Abstract PeptideProphet is a post-processing algorithm designed to evaluate the confidence in identifications of MS/MS spectra returned by a database search. In this manuscript we describe the "what and how" of PeptideProphet in a manner aimed at statisticians and life scientists who would like to gain a more in-depth understanding of the underlying statistical modeling. The theory and rationale behind the mixture-modeling approach taken by PeptideProphet is discussed from a statistical model-building perspective followed by a description of how a model can be used to express confidence in the identification of individual peptides or sets of peptides. We also demonstrate how to evaluate the quality of model fit and select an appropriate model from several available alternatives. We illustrate the use of PeptideProphet in association with the Trans-Proteomic Pipeline, a free suite of software used for protein identification.http://deepblue.lib.umich.edu/bitstream/2027.42/112836/1/12859_2012_Article_5421.pd