Pierre-Alain Binz

ProteomeXchange provides globally coordinated proteomics data submission and dissemination

5. Tools available and ways to submit data to PX ............................................................. 11 5.1. MS/MS data submissions to PRIDE .................................................................................... 11 5.1.1. Creation of supported files for “Complete” submissions .................................................. 11 5.1.1.1. PRIDE XML .................................................................................................................................. 11 5.1.1.2. mzIdentML ................................................................................................................................. 13 5.1.2. Checking the files before submission (initial quality assessment) ..................................... 14 5.1.3. File submission to PRIDE: the PX submission tool ............................................................. 15 5.1.3.1. General Information ................................................................................................................... 15 5.1.3.2. Functionality, Design and Implementation Details .................................................................... 15 5.1.3.3. New open source libraries made available with PX submission tool ......................................... 18 5.1.3.4. PX Submission Tool Java Web Start ............................................................................................ 18 5.1.4. File submission to PRIDE: Command line support using Aspera ........................................ 19 5.1.5. Examples of Partial submissions to PRIDE ......................................................................... 19 5.2. SRM data submissions via PASSEL ..................................................................................... 20

mzML—a Community Standard for Mass Spectrometry Data

Mass spectrometry is a fundamental tool for discovery and analysis in the life sciences. With the rapid advances in mass spectrometry technology and methods, it has become imperative to provide a standard output format for mass spectrometry data that will facilitate data sharing and analysis. Initially, the efforts to develop a standard format for mass spectrometry data resulted in multiple formats, each designed with a different underlying philosophy. To resolve the issues associated with having multiple formats, vendors, researchers, and software developers convened under the banner of the HUPO PSI to develop a single standard. The new data format incorporated many of the desirable technical attributes from the previous data formats, while adding a number of improvements, including features such as a controlled vocabulary with validation tools to ensure consistent usage of the format, improved support for selected reaction monitoring data, and immediately available implementations to facilitate rapid adoption by the community. The resulting standard data format, mzML, is a well tested open-source format for mass spectrometer output files that can be readily utilized by the community and easily adapted for incremental advances in mass spectrometry technology.

The mouse SWISS‐2D PAGE database: a tool for proteomics study of diabetes and obesity

A number of two‐dimensional electrophoresis (2‐DE) reference maps from mouse samples have been established and could be accessed through the internet. An up‐to‐ date list can be found in WORLD‐2D PAGE (http://www.expasy.ch/ch2d/2d‐index.html), an index of 2‐DE databases and services. None of them were established from mouse white and brown adipose tissues, pancreatic islets, liver nuclei and skeletal muscle. This publication describes the mouse SWISS‐2D PAGE database. Proteins present in samples of mouse (C57Bl/6J) liver, liver nuclei, muscle, white and brown adipose tissue and pancreatic islets are assembled and described in an accessible uniform format. SWISS‐2D PAGE can be accessed through the World Wide Web (WWW) network on the ExPASy molecular biology server (http://www.expasy.ch/ch2d/).

Improving protein identification from peptide mass fingerprinting through a parameterized multi-level scoring algorithm and an optimized peak detection.

We have developed a new algorithm to identify proteins by means of peptide mass fingerprinting. Starting from the matrix‐assisted laser desorption/ionization‐time‐of‐flight (MALDI‐TOF) spectra and environmental data such as species, isoelectric point and molecular weight, as well as chemical modifications or number of missed cleavages of a protein, the program performs a fully automated identification of the protein. The first step is a peak detection algorithm, which allows precise and fast determination of peptide masses, even if the peaks are of low intensity or they overlap. In the second step the masses and environmental data are used by the identification algorithm to search in protein sequence databases (SWISS‐PROT and/or TrEMBL) for protein entries that match the input data. Consequently, a list of candidate proteins is selected from the database, and a score calculation provides a ranking according to the quality of the match. To define the most discriminating scoring calculation we analyzed the respective role of each parameter in two directions. The first one is based on filtering and exploratory effects, while the second direction focuses on the levels where the parameters intervene in the identification process. Thus, according to our analysis, all input parameters contribute to the score, however with different weights. Since it is difficult to estimate the weights in advance, they have been computed with a generic algorithm, using a training set of 91 protein spectra with their environmental data. We tested the resulting scoring calculation on a test set of ten proteins and compared the identification results with those of other peptide mass fingerprinting programs.

Standard guidelines for the chromosome-centric human proteome project.

The objective of the international Chromosome-Centric Human Proteome Project (C-HPP) is to map and annotate all proteins encoded by the genes on each human chromosome. The C-HPP consortium was established to organize a collaborative network among the research teams responsible for protein mapping of individual chromosomes and to identify compelling biological and genetic mechanisms influencing colocated genes and their protein products. The C-HPP aims to foster the development of proteome analysis and integration of the findings from related molecular -omics technology platforms through collaborations among universities, industries, and private research groups. The C-HPP consortium leadership has elicited broad input for standard guidelines to manage these international efforts more efficiently by mobilizing existing resources and collaborative networks. The C-HPP guidelines set out the collaborative consensus of the C-HPP teams, introduce topics associated with experimental approaches, data production, quality control, treatment, and transparency of data, governance of the consortium, and collaborative benefits. A companion approach for the Biology and Disease-Driven HPP (B/D-HPP) component of the Human Proteome Project is currently being organized, building upon the Human Proteome Organizations organ-based and biofluid-based initiatives (www.hupo.org/research). The common application of these guidelines in the participating laboratories is expected to facilitate the goal of a comprehensive analysis of the human proteome.

Heat shock proteins in human cancer

The heat shock proteins (hsp) are ubiquitous molecules induced in cells exposed to sublethal heat shock, present in all living cells, and highly conserved during evolution. Their function is to protect cells from environmental stress damage by binding to partially denatured proteins, dissociating protein aggregates, to regulate the correct folding, and to cooperate in transporting newly synthesized polypeptides to the target organelles. The molecular chaperones are involved in numerous diseases, including cancer, revealing changes of expression. In this review, we mainly describe the relationship of hsp expression with human cancer, and discuss what is known about their post‐translational modifications according to malignancies.

Exploring glycopeptide-resistance in Staphylococcus aureus: a combined proteomics and transcriptomics approach for the identification of resistance-related markers

BackgroundTo unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome.ResultsIn the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides.ConclusionOur proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets.

The 1999 SWISS-2DPAGE database update

SWISS-2DPAGE (http://www.expasy.ch/ch2d/ ) is an annotated two-dimensional polyacrylamide gel electro-phoresis (2-DE) database established in 1993. The current release contains 24 reference maps from human and mouse biological samples, as well as from Saccharomyces cerevisiae, Escherichia coli and Dictyostelium discoideum origin. These reference maps have now 2824 identified spots, corresponding to 614 separate protein entries in the database, in addition to virtual entries for each SWISS-PROT sequence or any user-entered amino acids sequence. Last year improvements in the SWISS-2DPAGE database are as follows: three new maps have been created and several others have been updated; cross-references to newly built federated 2-DE databases have been added; new functions to access the data have been provided through the ExPASy proteomics server.

TraML—A Standard Format for Exchange of Selected Reaction Monitoring Transition Lists

Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.

Modeling peptide mass fingerprinting data using the atomic composition of peptides.

The peptide mass fingerprinting technique is commonly used for identifying proteins analyzed by mass spectrometry (MS) after enzymatic digestion. Our goal is to build a theoretical model that predicts the mass spectra of such digestion products in order to improve the identification and characterization of proteins using this technique. We present here the first step towards a full MS model. We have modeled MS spectra using the atomic composition of peptides and evaluated the influence that this composition may have on the MS signals. Peptides deduced from the SWISS‐PROT protein sequence database were used for the calculation. To validate the model, the variability of the peptide mass distribution in SWISS‐PROT was compared to two theoretical, randomly generated databases. Functions have been built that describe the behavior of the isotopic distribution according to the mass of peptides. The variability of these functions was analyzed. In particular, the influence of sulfur was studied. This work, while representing only a first step in the construction of an MS model, yields immediate practical results, as the new isotopic distribution model significantly improves peak detection in MS spectra used by protein identification algorithms.