Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Pathogen Recognition and Innate Immunity

Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.

Probability-based protein identification by searching sequence databases using mass spectrometry data.

▪ Abstract The innate immune system is a universal and ancient form of host defense against infection. Innate immune recognition relies on a limited number of germline-encoded receptors. These receptors evolved to recognize conserved products of microbial metabolism produced by microbial pathogens, but not by the host. Recognition of these molecular structures allows the immune system to distinguish infectious nonself from noninfectious self. Toll-like receptors play a major role in pathogen recognition and initiation of inflammatory and immune responses. Stimulation of Toll-like receptors by microbial products leads to the activation of signaling pathways that result in the induction of antimicrobial genes and inflammatory cytokines. In addition, stimulation of Toll-like receptors triggers dendritic cell maturation and results in the induction of costimulatory molecules and increased antigen-presenting capacity. Thus, microbial recognition by Toll-like receptors helps to direct adaptive immune responses ...

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Innate Immune Recognition

One of the mechanisms by which the innate immune system senses the invasion of pathogenic microorganisms is through the Toll-like receptors (TLRs), which recognize specific molecular patterns that are present in microbial components. Stimulation of different TLRs induces distinct patterns of gene expression, which not only leads to the activation of innate immunity but also instructs the development of antigen-specific acquired immunity. Here, we review the rapid progress that has recently improved our understanding of the molecular mechanisms that mediate TLR signalling.

Toll-like receptor signalling

HCN Channel Phosphorylation Sites Mapped by Mass Spectrometry in Human Epilepsy Patients and in an Animal Model of Temporal Lobe Epilepsy.

SpectraST is able to obtain better score separation between the good and bad hits, hence better separation between the sensitivity and error curves, than SEQUEST, in all cases. SpectraST is able to obtain considerably more highly-confident IDs than SEQUEST for the LCQ-generated datasets (Dataset 1 & 2). The extra hits that are missed by SEQUEST are manually verified to be correct (labeled F). False positives (labeled G) are extremely rare.

SpectraST: An open-source MS/MS spectra-matching search tool for targeted proteomics

Mass spectrometry provides a high-throughput approach to identify proteins in biological samples. A key step in the analysis of mass spectrometry data is to identify the peptide sequence that, most probably, gave rise to each observed spectrum. This is often tackled using a database search: each observed spectrum is compared against a large number of theoretical “expected” spectra predicted from candidate peptide sequences in a database, and the best match is identified using some heuristic scoring criterion. Here we provide a more principled, likelihood-based, scoring criterion for this problem. Specifically, we introduce a probabilistic model that allows one to assess, for each theoretical spectrum, the probability that it would produce the observed spectrum. This probabilistic model takes account of peak locations and intensities, in both observed and theoretical spectra, which enables incorporation of detailed knowledge of chemical plausibility in peptide identification. Besides placing peptide scoring on a sounder theoretical footing, the likelihood-based score also has important practical benefits: it provides natural measures for assessing the uncertainty of each identification, and in comparisons on benchmark data it produced more accurate peptide identifications than other methods, including SEQUEST. Although we focus here on peptide identification, our scoring rule could easily be integrated into any downstream analyses that require peptide-spectrum match scores.

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A likelihood-based scoring method for peptide identification using mass spectrometry

The community of proteomic researchers has been generating openly available software infrastructure for systematic proteomic data analyses and data management for the past several years. The Computational Proteomics Analysis System (CPAS) integrates many of these efforts into a single Web-based platform for mining liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic experiments. CPAS incorporates several tools currently used in proteomic analysis, including the X! Tandem search engine and the PeptideProphet and ProteinProphet data mining tools. These tools and others are integrated into a single application built on the open-source LabKey platform, an extensible architecture for developing high-throughput biological applications. The CPAS analysis pipeline acts on data in standardized file formats, so that researchers may use CPAS with other search engines, including Mascot or SEQUEST, that follow a standardized procedure for reporting search engine results. All components of the CPAS toolkit are freely available at http://cpas.fhcrc.org.

Jimmy K. Eng

Papers

HCN Channel Phosphorylation Sites Mapped by Mass Spectrometry in Human Epilepsy Patients and in an Animal Model of Temporal Lobe Epilepsy.

SpectraST: An open-source MS/MS spectra-matching search tool for targeted proteomics

A likelihood-based scoring method for peptide identification using mass spectrometry

Installation and Use of the Computational Proteomics Analysis System (CPAS)

Bacterial Typing and Identification by Mass Spectrometry