Lennart Martens

Exploring proteomes and analyzing protein processing by mass spectrometric identification of sorted N-terminal peptides.

Current non-gel techniques for analyzing proteomes rely heavily on mass spectrometric analysis of enzymatically digested protein mixtures. Prior to analysis, a highly complex peptide mixture is either separated on a multidimensional chromatographic system or it is first reduced in complexity by isolating sets of representative peptides. Recently, we developed a peptide isolation procedure based on diagonal electrophoresis and diagonal chromatography. We call it combined fractional diagonal chromatography (COFRADIC). In previous experiments, we used COFRADIC to identify more than 800 Escherichia coli proteins by tandem mass spectrometric (MS/MS) analysis of isolated methionine-containing peptides. Here, we describe a diagonal method to isolate N-terminal peptides. This reduces the complexity of the peptide sample, because each protein has one N terminus and is thus represented by only one peptide. In this new procedure, free amino groups in proteins are first blocked by acetylation and then digested with trypsin. After reverse-phase (RP) chromatographic fractionation of the generated peptide mixture, internal peptides are blocked using 2,4,6-trinitrobenzenesulfonic acid (TNBS); they display a strong hydrophobic shift and therefore segregate from the unaltered N-terminal peptides during a second identical separation step. N-terminal peptides can thereby be specifically collected for further liquid chromatography (LC)-MS/MS analysis. Omitting the acetylation step results in the isolation of non-lysine-containing N-terminal peptides from in vivo blocked proteins.

A guide to the Proteomics Identifications Database proteomics data repository

The Proteomics Identifications Database (PRIDE, www.ebi.ac.uk/pride) is one of the main repositories of MS derived proteomics data. Here, we point out the main functionalities of PRIDE both as a submission repository and as a source for proteomics data. We describe the main features for data retrieval and visualization available through the PRIDE web and BioMart interfaces. We also highlight the mechanism by which tailored queries in the BioMart can join PRIDE to other resources such as Reactome, Ensembl or UniProt to execute extremely powerful across‐domain queries. We then present the latest improvements in the PRIDE submission process, using the new easy‐to‐use, platform‐independent graphical user interface submission tool PRIDE Converter. Finally, we speak about future plans and the role of PRIDE in the ProteomExchange consortium.

Caspase-specific and nonspecific in vivo protein processing during Fas-induced apoptosis

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.

Chromatographic Isolation of Methionine-containing Peptides for Gel-free Proteome Analysis Identification Of More Than 800 Escherichia Coli Proteins

A novel gel-free proteomic technology was used to identify more than 800 proteins from 50 million Escherichia coli K12 cells in a single analysis. A peptide mixture is first obtained from a total unfractionated cell lysate, and only the methionine-containing peptides are isolated and identified by mass spectrometry and database searching. The sorting procedure is based on the concept of diagonal chromatography but adapted for highly complex mixtures. Statistical analysis predicts that we have identified more than 40% of the expressed proteome, including soluble and membrane-bound proteins. Next to highly abundant proteins, we also detected low copy number components such as the E. coli lactose operon repressor, illustrating the high dynamic range. The method is about 100 times more sensitive than two-dimensional gel-based methods and is fully automated. The strongest point, however, is the flexibility in the peptide sorting chemistry, which may target the technique toward quantitative proteomics of virtually every class of peptides containing modifiable amino acids, such as phosphopeptides, amino-terminal peptides, etc., adding a new dimension to future proteome research.

Peptide and protein quantification: A map of the minefield

The increasing popularity of gel‐free proteomics technologies has created a strong demand for compatible quantitative analysis methods. As a result, a plethora of different techniques has been proposed to perform gel‐free quantitative analysis of proteomics samples. Each of these methods comes with certain strengths and shortcomings, and they often are dedicated to a specific purpose. This review will present a brief overview of the main methods, organized by their underlying concepts, and will discuss the issues they raise with a focus on data processing. Finally, we will list the available software that can help with the data processing from quantitative experiments. We hope that this review will thus enable researchers to find the most appropriate method available for their research objectives, and can also serve as a basis for creating a reliable data processing strategy.

ms_lims, a simple yet powerful open source laboratory information management system for MS-driven proteomics.

MS‐based proteomics produces large amounts of mass spectra that require processing, identification and possibly quantification before interpretation can be undertaken. High‐throughput studies require automation of these various steps, and management of the data in association with the results obtained. We here present ms_lims (http://genesis.UGent.be/ms_lims), a freely available, open‐source system based on a central database to automate data management and processing in MS‐driven proteomics analyses.

Protein identification based on matrix assisted laser desorption/ionization-post source decay-mass spectrometry.

Due to its very short analysis time, its high sensitivity and ease of automation, matrix‐assisted laser desorption/ionization (MALDI)‐peptide mass fingerprinting has become the preferred method for identifying proteins of which the sequences are available in databases. However, many protein samples cannot be unambiguously identified by exclusively using their peptide mass fingerprints (e.g., protein mixtures, heavily post‐translationally modified proteins and small proteins). In these cases, additional sequence information is needed and one of the obvious choices when working with MALDI‐mass spectrometry (MS) is to choose for post source decay (PSD) analysis on selected peptides. This can be performed on the same sample which is used for peptide mass fingerprinting. Although in this type of peptide analysis, fragmentation yields are very low and PSD spectra are often very difficult to interpret manually, we here report upon our five years of experience with the use of PSD spectra for protein identification in sequence (protein or expressed sequence tag (EST)) databases. The combination of peptide mass fingerprinting and PSD and analysis described here generally leads to unambiguous protein identification in the amount of material range generally encountered in most proteome studies.

Current methods for global proteome identification.

In a time frame of a few decades, protein identification went from laborious single protein identification to automated identification of entire proteomes. This shift was enabled by the emergence of peptide-centric, gel-free analyses, in particular the so-called shotgun approaches, which not only rely on extensive experiments, but also on cutting-edge data processing methods. The present review therefore provides an overview of a shotgun proteomics identification workflow, listing the state-of-the-art methods involved and software that implement these. The authors focus on freely available tools where possible. Finally, data analysis in the context of emerging across-omics studies will also be discussed briefly, where proteomics goes beyond merely delivering a list of protein accession numbers.

Analysis of the resolution limitations of peptide identification algorithms.

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.

thermo-msf-parser: An Open Source Java Library to Parse and Visualize Thermo Proteome Discoverer msf Files

The Thermo Proteome Discoverer program integrates both peptide identification and quantification into a single workflow for peptide-centric proteomics. Furthermore, its close integration with Thermo mass spectrometers has made it increasingly popular in the field. Here, we present a Java library to parse the msf files that constitute the output of Proteome Discoverer. The parser is also implemented as a graphical user interface allowing convenient access to the information found in the msf files, and in Rover, a program to analyze and validate quantitative proteomics information. All code, binaries, and documentation is freely available at http://thermo-msf-parser.googlecode.com.