Christine Hoogland

ExPASy: the proteomics server for in-depth protein knowledge and analysis

The ExPASy (the Expert Protein Analysis System) World Wide Web server (http://www.expasy.org), is provided as a service to the life science community by a multidisciplinary team at the Swiss Institute of Bioinformatics (SIB). It provides access to a variety of databases and analytical tools dedicated to proteins and proteomics. ExPASy databases include SWISS-PROT and TrEMBL, SWISS-2DPAGE, PROSITE, ENZYME and the SWISS-MODEL repository. Analysis tools are available for specific tasks relevant to proteomics, similarity searches, pattern and profile searches, post-translational modification prediction, topology prediction, primary, secondary and tertiary structure analysis and sequence alignment. These databases and tools are tightly interlinked: a special emphasis is placed on integration of database entries with related resources developed at the SIB and elsewhere, and the proteomics tools have been designed to read the annotations in SWISS-PROT in order to enhance their predictions. ExPASy started to operate in 1993, as the first WWW server in the field of life sciences. In addition to the main site in Switzerland, seven mirror sites in different continents currently serve the user community.

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.

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.

New perspectives in the Escherichia coli proteome investigation.

Escherichia coli is a model organism for biochemical and biological studies as it is one of the best characterised prokaryote. Two‐dimensional polyacrylamide gel electrophoresis, computer image analysis and different protein identification techniques gave rise, in 1995, to the Escherichia coli SWISS‐2D PAGE database (http://www.expasy.ch/ch2d/). In the E. coli 3.5–10 SWISS‐2D PAGE map, 40% of the E. coli proteome was displayed. The present study demonstrated that the use of narrow range pH gradients is able to potentially display up to a few copies of protein per E. coli cell. Moreover, the six new E. coli SWISS‐2D PAGE maps (pH 4–5, 4.5–5.5, 5–6, 5.5–6.7, 6–9 and 6–11) presented here displayed altogether more than 70% of the entire E. coli proteome.

EasyProt - An easy-to-use graphical platform for proteomics data analysis

High throughput protein identification and quantification analysis based on mass spectrometry are fundamental steps in most proteomics projects. Here, we present EasyProt (available at http://easyprot.unige.ch), a new platform for mass spectrometry data processing, protein identification, quantification and unexpected post-translational modification characterization. EasyProt provides a fully integrated graphical experience to perform a large part of the proteomic data analysis workflow. Our goal was to develop a software platform that would fulfill the needs of scientists in the field, while emphasizing ease-of-use for non-bioinformatician users. Protein identification is based on OLAV scoring schemes and protein quantification is implemented for both, isobaric labeling and label-free methods. Additional features are available, such as peak list processing, isotopic correction, spectra filtering, charge-state deconvolution and spectra merging. To illustrate the EasyProt platform, we present two identification and quantification workflows based on isobaric tagging and label-free methods.

Guidelines for reporting the use of gel electrophoresis in proteomics

Gibson, Frank Anderson, Leigh Babnigg, Gyorgy Baker, Mark Berth, Matthias Binz, Pierre-Alain Borthwick, Andy Cash, Phil Day, Billy W. Friedman, David B. Garland, Donita Gutstein, Howard B. Hoogland, Christine Jones, Neil A. Khan, Alamgir Klose, Joachim Lamond, Angus I. Lemkin, Peter F. Lilley, Kathryn S. Minden, Jonathan Morris, Nicholas J. Paton, Norman W. Pisano, Michael R. Prime, John E. Rabilloud, Thierry Stead, David A. Taylor, Chris F. Voshol, Hans Wipat, Anil Jones, Andrew R. 2 NATURE PUBLISHING GROUP NEW YORK 335WX

Two‐dimensional electrophoresis resources available from ExPASy

This paper describes the set of two‐dimensional electrophoresis (2‐DE) resources currently available from the ExPASy proteomics Web server. These resources include the SWISS‐2DPAGE database, 2‐DE software packages, 2‐DE technical and educational services, as well as indexes and search engines for 2‐DE related sites over the Internet.

The World-2DPAGE Constellation to promote and publish gel-based proteomics data through the ExPASy server

Since it was launched in 1993, the ExPASy server has been and is still a reference in the proteomics world. ExPASy users access various databases, many dedicated tools, and lists of resources, among other services. A significant part of resources available is devoted to two-dimensional electrophoresis data. Our latest contribution to the expansion of the pool of on-line proteomics data is the World-2DPAGE Constellation, accessible at http://world-2dpage.expasy.org/. It is composed of the established WORLD-2DPAGE List of 2-D PAGE database servers, the World-2DPAGE Portal that queries simultaneously world-wide proteomics databases, and the recently created World-2DPAGE Repository. The latter component is a public standards-compliant repository for gel-based proteomics data linked to protein identifications published in the literature. It has been set up using the Make2D-DB package, a software tool that helps building SWISS-2DPAGE-like databases on ones own Web site. The lack of necessary informatics infrastructure to build and run a dedicated website is no longer an obstacle to make proteomics data publicly accessible on the Internet.

Quantitative analysis of human cerebrospinal fluid proteins using a combination of cysteine tagging and amine-reactive isobaric labeling

Highly complex and dynamic protein mixtures are hardly comprehensively resolved by direct shotgun proteomic analysis. As many proteins of biological interest are of low abundance, numerous analytical methodologies have been developed to reduce sample complexity and go deeper into proteomes. The present work describes an analytical strategy to perform cysteinyl-peptide subset enrichment and relative quantification through successive cysteine and amine-isobaric tagging. A cysteine-reactive covalent capture tag (C³T) allowed derivatization of cysteines and specific isolation on a covalent capture (CC) resin. The 6-plex amine-reactive tandem mass tags (TMT) served for relative quantification of the targeted peptides. The strategy was first evaluated on a model protein mixture with increasing concentrations to assess the specificity of the enrichment and the quantitative performances of the workflow. It was then applied to human cerebrospinal fluid (CSF) from post-mortem and ante-mortem samples. These studies confirmed the specificity of the C³T and the CC technique to cysteine-containing peptides. The model protein mixture analysis showed high precision and accuracy of the quantification with coefficients of variation and mean absolute errors of less than 10% on average. The CSF experiments demonstrated the potential of the strategy to study complex biological samples and identify differential brain-related proteins. In addition, the quantification data were highly correlated with a classical TMT experiment (i.e., without C³T cysteine-tagging and enrichment steps). Altogether, these results legitimate the use of this quantitative C³T strategy to enrich and relatively quantify cysteine-containing peptides in complex mixtures.