Sean L. Seymour

The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra

The Paragon™ Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database to be determined on a continuum. Counter to conventional approaches, features such as modifications, substitutions, and cleavage events are modeled with probabilities rather than by discrete user-controlled settings to consider or not consider a feature. The use of feature probabilities in conjunction with Sequence Temperature Values allows for a very large increase in the effective search space with only a very small increase in the actual number of hypotheses that must be scored. The algorithm has a new kind of user interface that removes the user expertise requirement, presenting control settings in the language of the laboratory that are translated to optimal algorithmic settings. To validate this new algorithm, a comparison with Mascot is presented for a series of analogous searches to explore the relative impact of increasing search space probed with Mascot by relaxing the tryptic digestion conformance requirements from trypsin to semitrypsin to no enzyme and with the Paragon Algorithm using its Rapid mode and Thorough mode with and without tryptic specificity. Although they performed similarly for small search space, dramatic differences were observed in large search space. With the Paragon Algorithm, hundreds of biological and artifact modifications, all possible substitutions, and all levels of conformance to the expected digestion pattern can be searched in a single search step, yet the typical cost in search time is only 2–5 times that of conventional small search space. Despite this large increase in effective search space, there is no drastic loss of discrimination that typically accompanies the exploration of large search space.

A Cross-platform Toolkit for Mass Spectrometry and Proteomics

Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

Nonlinear Fitting Method for Determining Local False Discovery Rates from Decoy Database Searches

False discovery rate (FDR) analyses of protein and peptide identification results using decoy database searching conventionally report aggregate or global FDRs for a whole set of identifications, which are often not very informative about the error rates of individual members in the set. We describe a nonlinear curve fitting method for calculating the local FDR, which estimates the chance that an individual protein (or peptide) is incorrect, and present a simple tool that implements this analysis. The goal of this method is to offer a simple extension to the now commonplace decoy database searching, providing additional valuable information.

The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results

We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.

A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies

Large databases (>106 sequences) used in metaproteomic and proteogenomic studies present challenges in matching peptide sequences to MS/MS data using database‐search programs. Most notably, strict filtering to avoid false‐positive matches leads to more false negatives, thus constraining the number of peptide matches. To address this challenge, we developed a two‐step method wherein matches derived from a primary search against a large database were used to create a smaller subset database. The second search was performed against a target‐decoy version of this subset database merged with a host database. High confidence peptide sequence matches were then used to infer protein identities. Applying our two‐step method for both metaproteomic and proteogenomic analysis resulted in twice the number of high confidence peptide sequence matches in each case, as compared to the conventional one‐step method. The two‐step method captured almost all of the same peptides matched by the one‐step method, with a majority of the additional matches being false negatives from the one‐step method. Furthermore, the two‐step method improved results regardless of the database search program used. Our results show that our two‐step method maximizes the peptide matching sensitivity for applications requiring large databases, especially valuable for proteogenomics and metaproteomics studies.

Guidelines for reporting the use of mass spectrometry in proteomics

Joeri Borstlap1, Glyn Stacey2, Andreas Kurtz3, Anja Elstner3, Alexander Damaschun1, Begoña Arán4 & Anna Veiga4,5 1CellNet Initiative, Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité– Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 2The UK Stem Cell Bank, National Institute for Biological Standards and Control, Blanch Lane, South Mimms, Potters Bar, Hertfordshire, EN6 3QG, UK. 3Cell Therapy Group, BerlinBrandenburg Center for Regenerative Therapies (BCRT), Charité–Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 4Banc de Linies Cellulars, Centre de Medicina Regenerativa de Barcelona (CMRB), C/Dr. Aiguader 88, 08003-Barcelona, Spain. 5Institut Universitari Dexeus, Passeig de la Bonanova 67, 08017-Barcelona, Spain. e-mail: joeri.borstlap@b-crt.de

Guidelines for reporting the use of mass spectrometry informatics in proteomics.

volume 26 number 8 august 2008 nature biotechnology are addressed in the MIAPE-MS (mass spectrometry) module, the latest version of which can be obtained from the MIAPE home page. Note also that these guidelines do not cover all the available features of a protein and peptide identification and characterization tool (e.g., some of the less frequently used parameters, types of spectra or other experimental data); subsequent versions may have expanded coverage, as will almost certainly be the case for all MIAPE modules. These guidelines will evolve in step with progress in research. The most recent version of MIAPE-MSI is available at http://www.psidev.info/miape/msi/ and the content is replicated here as supplementary information (Supplementary Guidelines and Supplementary Table 1). To contribute or to track the process to remain ‘MIAPE compliant’, browse the website at http:// www.psidev.info/miape/.

Deep metaproteomic analysis of human salivary supernatant

The human salivary proteome is extremely complex, including proteins from salivary glands, serum, and oral microbes. Much has been learned about the host component, but little is known about the microbial component. Here we report a metaproteomic analysis of salivary supernatant pooled from six healthy subjects. For deep interrogation of the salivary proteome, we combined protein dynamic range compression (DRC), multidimensional peptide fractionation, and high‐mass accuracy MS/MS with a novel two‐step peptide identification method using a database of human proteins plus those translated from oral microbe genomes. Peptides were identified from 124 microbial species as well as uncultured phylotypes such as TM7. Streptococcus, Rothia, Actinomyces, Prevotella, Neisseria, Veilonella, Lactobacillus, Selenomonas, Pseudomonas, Staphylococcus, and Campylobacter were abundant among the 65 genera from 12 phyla represented. Taxonomic diversity in our study was broadly consistent with metagenomic studies of saliva. Proteins mapped to 20 KEGG pathways, with carbohydrate metabolism, amino acid metabolism, energy metabolism, translation, membrane transport, and signal transduction most represented. The communities sampled appear to be actively engaged in glycolysis and protein synthesis. This first deep metaproteomic catalog from human salivary supernatant provides a baseline for future studies of shifts in microbial diversity and protein activities potentially associated with oral disease.

CysTRAQ — A combination of iTRAQ and enrichment of cysteinyl peptides for uncovering and quantifying hidden proteomes

Shotgun proteomics is capable of characterizing differences in both protein quality and quantity, and has been applied in various biomedical applications. Unfortunately, the high complexity and dynamic range of proteins in studied samples, clinical in particular, often hinders the identification of relevant proteins. Indeed, information-rich, low abundance proteins often remain undetected, whereas repeatedly reported altered concentrations in high abundance proteins are often ambiguous and insignificant. Several techniques have therefore been developed to overcome this obstacle and provide a deeper insight into the proteome. Here we report a novel approach, which enables iTRAQ reagent quantitation of peptides fractionated based on presence of a cysteine residue (thus CysTRAQ). For the first time, we prove that iTRAQ quantitation is fully compatible with cysteinyl peptide enrichment and is not influenced by the fractionation process. Moreover, the employment of the method combined with high-resolution TripleTOF 5600 mass spectrometer for very fast MS/MS acquisition in human amniotic fluid analysis significantly increased the number of identified proteins, which were simultaneously quantified owing to the introduction of iTRAQ labeling. We herein show that CysTRAQ is a robust and straightforward method with potential application in quantitative proteomics experiments, i.e. as an alternative to the ICAT reagent approach.

Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm

LTQ Orbitrap data analyzed with ProteinPilot can be further improved by MaxQuant raw data processing, which utilizes precursor‐level high mass accuracy data for peak processing and MGF creation. In particular, ProteinPilot results from MaxQuant‐processed peaklists for Orbitrap data sets resulted in improved spectral utilization due to an improved peaklist quality with higher precision and high precursor mass accuracy (HPMA). The output and postsearch analysis tools of both workflows were utilized for previously unexplored features of a three‐dimensional fractionated and hexapeptide library (ProteoMiner) treated whole saliva data set comprising 200 fractions. ProteinPilots ability to simultaneously predict multiple modifications showed an advantage from ProteoMiner treatment for modified peptide identification. We demonstrate that complementary approaches in the analysis pipeline provide comprehensive results for the whole saliva data set acquired on an LTQ Orbitrap. Overall our results establish a workflow for improved protein identification from high mass accuracy data.