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We present an MS(2) peak intensity prediction server that computes MS(2) charge 2+ and 3+ spectra from peptide sequences for the most common fragment ions. The server integrates the Unimod public domain post-translational modification database for modified peptides. The prediction model is an improvement of the previously published MS(2)PIP model for Orbitrap-LTQ CID spectra. Predicted MS(2) spectra can be downloaded as a spectrum file and can be visualized in the browser for comparisons with observations. In addition, we added prediction models for HCD fragmentation (Q-Exactive Orbitrap) and show that these models compute accurate intensity predictions on par with CID performance. We also show that training prediction models for CID and HCD separately improves the accuracy for each fragmentation method. The MS(2)PIP prediction server is accessible from http://iomics.ugent.be/ms2pip., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

A growing number of proteogenomics and metaproteomics studies indicate potential limitations of the application of the "decoy" database paradigm used to separate correct peptide identifications from incorrect ones in traditional shotgun proteomics. We therefore propose a binary classifier called Nokoi that allows fast yet reliable decoy-free separation of correct from incorrect peptide-to-spectrum matches (PSMs). Nokoi was trained on a very large collection of heterogeneous data using ranks supplied by the Mascot search engine to label correct and incorrect PSMs. We show that Nokoi outperforms Mascot and achieves a performance very close to that of Percolator at substantially higher processing speeds.

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor., (© 2014 The Authors.)

Positional proteomics refers to mass spectrometry (MS)-driven proteomics technologies by which peptides carrying the amino or carboxyl terminus of proteins are enriched from whole proteomes, and identified by means of MS. Proteases irreversibly modify their substrates by hydrolyzing peptide bonds and have thus profound effects on the biological processes steered by their substrates. Since proteases create novel termini in their substrates, positional proteomics is ideally suited for protease degradomics studies. We here review recent developments in the field of positional proteomics applied to protease research., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

We have created a new software platform called sigpep that analyzes transition redundancy in selected reaction monitoring assays. Building on this platform, we also created a web application to generate transition sets with unique signatures for targeted peptides. The platform has been made available under the permissive Apache 2.0 open-source license, and the web application can be accessed from http://iomics.ugent.be/sigpep., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Proteome identification using peptide-centric proteomics techniques is a routinely used analysis technique. One of the most powerful and popular methods for the identification of peptides from MS/MS spectra is protein database matching using search engines. Significance thresholding through false discovery rate (FDR) estimation by target/decoy searches is used to ensure the retention of predominantly confident assignments of MS/MS spectra to peptides. However, shortcomings have become apparent when such decoy searches are used to estimate the FDR. To study these shortcomings, we here introduce a novel kind of decoy database that contains isobaric mutated versions of the peptides that were identified in the original search. Because of the supervised way in which the entrapment sequences are generated, we call this a directed decoy database. Since the peptides found in our directed decoy database are thus specifically designed to look quite similar to the forward identifications, the limitations of the existing search algorithms in making correct calls in such strongly confusing situations can be analyzed. Interestingly, for the vast majority of confidently identified peptide identifications, a directed decoy peptide-to-spectrum match can be found that has a better or equal match score than the forward match score, highlighting an important issue in the interpretation of peptide identifications in present-day high-throughput proteomics.

Tyrosine nitration is the consequence of a complex machinery of formation and merging of oxygen and nitrogen radicals, and has been associated with both physiological pathways as well as with several human diseases. The latter turned this posttranslational protein modification into an interesting biomarker, being either a consequence of the disease or a factor contributing to the disease onset. However, the interpretation of MS and MS/MS data of peptides containing nitrotyrosine has proven to be very challenging and consequently, the risk of linking MS/MS spectra to incorrect peptide sequences exists and has been reported. Here, we discuss the causes of data misinterpretation and describe a general method to avoid mistakes of MS/MS spectrum misinterpretation. Central in our approach is the reduction of nitrotyrosine into aminotyrosine and the use of the Peptizer algorithm to inspect MS/MS quality-related assumptions., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

MS-driven proteomics has evolved over the past two decades to a high tech and high impact research field. Two distinct factors clearly influenced its expansion: the rapid growth of an arsenal of instrument and proteomic techniques that led to an explosion of high quality data and the development of software tools to analyze and interpret these data which boosted the number of scientific discoveries. In analogy with the benchmarking of new instruments and proteomic techniques, such software tools must be thoroughly tested and analyzed. Recently, new tools were developed for automatic peptide quantification in quantitative proteomic experiments. Here we present a case study where the most recent and frequently used tools are analyzed and compared.

Proteins are reckoned to be the key actors in a living organism. By studying proteins, one engages into deciphering a complex series of events occurring during a protein's life span. This starts at the creation of a protein, which is tightly controlled on both a transcriptional (Williams and Tyler, 2007, Curr Opin Genet Dev 17, 88-93) and a translational level (Van Der Kelen et al., 2009, Crit Rev Biochem Mol Biol 44, 143-168). During translation, a primary strand of amino acids undergoes a complex folding process in order to obtain a native three-dimensional protein structure (Gross et al., 2003, Cell 115, 739-750). Proteins take on a plethora of functions, such as complex formation, receptor activity, and signal transduction, which ultimately adds up to a cellular phenotype. Consequently, protein analysis is of major interest in molecular biology and involves annotating their presence and localization, as well as their modification state and biochemical context. To accomplish this, many methods have been developed over the last decades, and their general principles and important recent advances in large-scale protein analysis or proteomics are discussed in this review.

False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics. They occur in regions where the score distributions of true positives and true negatives overlap. Removal of these false positive identifications necessarily involves a trade-off between sensitivity and specificity. Existing postprocessing tools typically rely on a fixed or semifixed set of assumptions in their attempts to optimize both the sensitivity and the specificity of peptide and protein identification using MS/MS spectra. Because of the expanding diversity in available proteomics technologies, however, these postprocessing tools often struggle to adapt to emerging technology-specific peculiarity. Here we present a novel tool named Peptizer that solves this adaptability issue by making use of pluggable assumptions. This research-oriented postprocessing tool also includes a graphical user interface to perform efficient manual validation of suspect identifications for optimal sensitivity recovery. Peptizer is open source software under the Apache2 license and is written in Java.

Nitrated tyrosines are easily converted into their aminotyrosine equivalents by a reduction step. We here show that this conversion can be exploited to readily discern 3-aminotyrosine peptides in a background of non-nitrated peptides. Furthermore, aminotyrosine peptides are more stable in single mass spectrometry (MS) mode rendering peptide mass maps easier to interpret. One significant caveat of both 3-nitrotyrosine and 3-aminotyrosine peptides is their lack of efficient fragmentation upon collision-induced dissociation (CID) which, in the case of the latter peptides, also produces unexpected, deviating isotopic patterns of fragment ions containing the aminotyrosine residue. The net result is that sequence database searching becomes daunting as the correct peptide is frequently missed since insufficient and/or inaccurate peptide fragments are used. We show that a simple acetylation step, blocking all amines (including aminotyrosine), produces peptides that undergo extensive backbone fragmentation by CID and are thus easily identifiable in databases. Our procedure is additionally illustrated by doubling the number of nitration events mapped in tetranitromethane-nitrated bovine serum albumin (BSA) as compared to a direct analysis of the nitrated peptides using the same amount of material. In conclusion, we here illustrate that this two-step process, heme-mediated reduction and acetylation, can be used for more efficient characterization of protein-bound nitrated tyrosines., (Copyright 2006 John Wiley & Sons, Ltd.)

We generated a comprehensive picture of protease substrates in anti-Fas-treated apoptotic human Jurkat T lymphocytes. We used combined fractional diagonal chromatography (COFRADIC) sorting of protein amino-terminal peptides coupled to oxygen-16 or oxygen-18 differential labeling. We identified protease substrates and located the exact cleavage sites within processed proteins. Our analysis yielded 1,834 protein identifications and located 93 cleavage sites in 71 proteins. Indirect evidence of apoptosis-specific cleavage within 21 additional proteins increased the total number of processed proteins to 92. Most cleavages were at caspase consensus sites; however, other cleavage specificities suggest activation of other proteases. We validated several new processing events by immunodetection and by an in vitro assay using recombinant caspases and synthetic peptides containing presumed cleavage sites. The spliceosome complex appeared a preferred target, as 14 of its members were processed. Differential isotopic labeling further revealed specific release of nucleosomal components from apoptotic nuclei.

Diagonal electrophoresis/chromatography was described 40 years ago and was used to isolate specific sets of peptides from simple peptide mixtures such as protease digests of purified proteins. Recently, we have adapted the core technology of diagonal chromatography so that the technique can be used in so-called gel-free, peptide-centric proteome studies. Here we review the different procedures we have developed over the past few years, sorting of methionyl, cysteinyl, amino terminal, and phosphorylated peptides. We illustrate the power of the technique, termed COFRADIC (combined fractional diagonal chromatography), in the case of a peptide-centric analysis of a sputum sol phase sample of a patient suffering from chronic obstructive pulmonary disease (COPD). We were able to identify an unexpectedly high number of intracellular proteins next to known biomarkers.

Background: Profilin is a small cytoskeletal protein which interacts with actin, proline-rich proteins and phosphatidylinositol 4,5-bisphosphate (PI(4,5)-P2). Crystallography, NMR and mutagenesis of vertebrate profilins have revealed the amino acid residues that are responsible for the interactions with actin and poly(L-proline) peptides. Although Arg88 of human profilin I was shown to be involved in PI(4,5)-P2-binding, it was suggested that carboxy terminal basic residues may be involved as well., Results: Using site directed mutagenesis we have refined the PI(4,5)-P2 binding site of human profilin I. For each mutant we assessed the stability and studied the interactions with actin, a proline-rich peptide and PI(4,5)-P2 micelles. We identified at least two PI(4,5)-P2-binding regions in human profilin I. As expected, one region comprises Arg88 and overlaps with the actin binding site. The second region involves Arg136 in the carboxy terminal helix and neighbours the poly(L-proline) binding site. In addition, we show that adding a small protein tag to the carboxy terminus of profilin strongly reduces binding to poly(L-proline), suggesting local conformational changes of the carboxy terminal alpha-helix may have dramatic effects on ligand binding., Conclusions: The involvement of the two terminal alpha-helices of profilin in ligand binding imposes important structural constraints upon the functions of this region. Our data suggest a model in which the competitive interactions between PI(4,5)-P2 and actin and PI(4,5)-P2 and poly(L-proline) regulate profilin functions.