This work describes the development of a program that predicts whether or not a polypeptide sequence from a Gram-negative bacterium is an integral beta-barrel outer membrane protein. The program, called the beta-barrel Outer Membrane protein Predictor (BOMP), is based on two separate components to recognize integral beta-barrel proteins. The first component is a C-terminal pattern typical of many integral beta-barrel proteins. The second component calculates an integral beta-barrel score of the sequence based on the extent to which the sequence contains stretches of amino acids typical of transmembrane beta-strands. The precision of the predictions was found to be 80% with a recall of 88% when tested on the proteins with SwissProt annotated subcellular localization in Escherichia coli K 12 (788 sequences) and Salmonella typhimurium (366 sequences). When tested on the predicted proteome of E.coli, BOMP found 103 of a total of 4346 polypeptide sequences to be possible integral beta-barrel proteins. Of these, 36 were found by BLAST to lack similarity (E-value score < 1e-10) to proteins with annotated subcellular localization in SwissProt. BOMP predicted the content of integral beta-barrels per predicted proteome of 10 different bacteria to range from 1.8 to 3%. BOMP is available at http://www.bioinfo.no/tools/bomp.

High-resolution two-dimensional gel electrophoresis and mass spectrometry has been used to identify the outer membrane (OM) subproteome of the Gram-negative bacterium Methylococcus capsulatus (Bath). Twenty-eight unique polypeptide sequences were identified from protein samples enriched in OMs. Only six of these polypeptides had previously been identified. The predictions from novel bioinformatic methods predicting beta-barrel outer membrane proteins (OMPs) and OM lipoproteins were compared to proteins identified experimentally. BOMP ( http://www.bioinfo.no/tools/bomp ) predicted 43 beta-barrel OMPs (1.45%) from the 2,959 annotated open reading frames. This was a lower percentage than predicted from other Gram-negative proteomes (1.8-3%). More than half of the predicted BOMPs in M. capsulatus were annotated as (conserved) hypothetical proteins with significant similarity to very few sequences in Swiss-Prot or TrEMBL. The experimental data and the computer predictions indicated that the protein composition of the M. capsulatus OM subproteome was different from that of other Gram-negative bacteria studied in a similar manner. A new program, Lipo, was developed that can analyse entire predicted proteomes and give a list of recognised lipoproteins categorised according to their lipo-box similarity to known Gram-negative lipoproteins ( http://www.bioinfo.no/tools/lipo ). This report is the first using a proteomics and bioinformatics approach to identify the OM subproteome of an obligate methanotroph.

MS-based proteomics produces large amounts of mass spectra that require processing, identification and possibly quantification before interpretation can be undertaken. Highthroughput 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. Keywords:Bioinformatics / Data management / Laboratory information management system / Mascot / MS Proteomics labs nowadays often acquire hundreds of thousands to millions of MS/MS spectra per proteome analysis to make large-scale (comprehensive) proteome maps [1]. They rely on contemporary mass spectrometers with rapid duty cycles that increase the amount of produced data by a full order of magnitude compared to older instruments [2]. Automating the processing of these data, and managing their provenance has correspondingly become an important postanalysis task. The automation of these tasks requires the implementation of a start-to-end workflow around a central database management system that is designed for proteomics experiments, with some of the most prominent commercial and academic systems recently reviewed in [3]. Typical actions include collecting and warehousing MS/MS peak lists (often acquired on multiple, different instruments), assigning the accumulated MS/MS data to peptide identifications, quantifying peptides and proteins from MS or MS/MS data, and organizing both data and analysis results in a navigable project structure. In most high-throughput environments, these diverse actions are typically undertaken by different individuals, which further necessitates a role-based implementation of the software interfaces [4].To tackle and manage these problems associated with MSdriven proteomics, we developed ms_lims, an open source and instrument vendor-independent system for proteomics data management. In contrast to existing web-based tools such as MASPECTRAS [4] or CPAS [5], ms_lims embraces a client-server architecture, which allows for more dynamic interaction. Additionally, ms_lims also differs from libraries Abbreviations: LIMS, laboratory information management system; MGF, Mascot generic file; SQL, Structured Query Language à These authors contributed equally to this work.

In contemporary peptide-centric or non-gel proteome studies, vast amounts of peptide fragmentation data are generated of which only a small part leads to peptide or protein identification. This motivates the development and use of a filtering algorithm that removes spectra that contribute little to protein identification. Removal of unidentifiable spectra reduced both the amount of computational and human time spent on analyzing spectra as well as the chances of obtaining false identifications. Thorough testing on various proteome datasets from different instruments showed that the best suggested machine-learning classifier is, on average, able to recognize half of the unidentified spectra as bad spectra. Further analyses showed that several unidentified spectra classified as good were derived from peptides carrying unanticipated amino acid modifications or contained sequence tags that allowed peptide identification using homology searches. The implementation of the classifiers is available under the GNU General Public License at http://www.bioinfo.no/software/spectrumquality.

Cerebrospinal fluid (CSF) is a perfect source to search for new biomarkers to improve early diagnosis of neurological diseases. Standardization of pre-analytical handling of the sample is, however, important to obtain acceptable analytical quality. In the present study, MALDI-TOF MS was used to examine the influence of pre-analytical sample procedures on the low molecular weight (MW) CSF proteome. Different storage conditions like temperature and duration or the addition of as little as 0.2 µL blood/mL neat CSF caused significant changes in the mass spectra. The performance of different types of MW cut-off spin cartridges from different suppliers used to enrich the low MW CSF proteome showed great variance in cut-off accuracy, stability and reproducibility. The described analytical method achieved a polypeptide discriminating limit of approximately 800 pM, two to three orders of magnitude lower than reported for plasma. Based on this study, we recommend that CSF is centrifuged immediately after sampling, prior to storage at -80ºC without addition of protease inhibitors. Guanidinium hydrochloride is preferred to break protein-protein interactions. A spin cartridge with cut-off limit above the intended analytical mass range is recommended. Our study contributes to the important task of developing standardized pre-analytical protocols for the proteomic study of CSF.