Marc Kirchner

Proteome Scale Characterization of Human S-Acylated Proteins in Lipid Raft-enriched and Non-raft Membranes

Protein S-acylation (palmitoylation), a reversible post-translational modification, is critically involved in regulating protein subcellular localization, activity, stability, and multimeric complex assembly. However, proteome scale characterization of S-acylation has lagged far behind that of phosphorylation, and global analysis of the localization of S-acylated proteins within different membrane domains has not been reported. Here we describe a novel proteomics approach, designated palmitoyl protein identification and site characterization (PalmPISC), for proteome scale enrichment and characterization of S-acylated proteins extracted from lipid raft-enriched and non-raft membranes. In combination with label-free spectral counting quantitation, PalmPISC led to the identification of 67 known and 331 novel candidate S-acylated proteins as well as the localization of 25 known and 143 novel candidate S-acylation sites. Palmitoyl acyltransferases DHHC5, DHHC6, and DHHC8 appear to be S-acylated on three cysteine residues within a novel CCX7–13C(S/T) motif downstream of a conserved Asp-His-His-Cys cysteine-rich domain, which may be a potential mechanism for regulating acyltransferase specificity and/or activity. S-Acylation may tether cytoplasmic acyl-protein thioesterase-1 to membranes, thus facilitating its interaction with and deacylation of membrane-associated S-acylated proteins. Our findings also suggest that certain ribosomal proteins may be targeted to lipid rafts via S-acylation, possibly to facilitate regulation of ribosomal protein activity and/or dynamic synthesis of lipid raft proteins in situ. In addition, bioinformatics analysis suggested that S-acylated proteins are highly enriched within core complexes of caveolae and tetraspanin-enriched microdomains, both cholesterol-rich membrane structures. The PalmPISC approach and the large scale human S-acylated protein data set are expected to provide powerful tools to facilitate our understanding of the functions and mechanisms of protein S-acylation.

Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics

Protein identification by tandem mass spectrometry is based on the reliable processing of the acquired data. Unfortunately, the generation of a large number of poor quality spectra is commonly observed in LC-MS/MS, and the processing of these mostly noninformative spectra with its associated costs should be avoided. We present a continuous quality score that can be computed very quickly and that can be considered an approximation of the MASCOT score in case of a correct identification. This score can be used to reject low quality spectra prior to database identification, or to draw attention to those spectra that exhibit a (supposedly) high information content, but could not be identified. The proposed quality score can be calibrated automatically on site without the need for a manually generated training set. When this score is turned into a classifier and when features are used that are independent of the instrument, the proposed approach performs equally to previously published classifiers and feature sets and also gives insights into the behavior of the MASCOT score.

Concise Representation of Mass Spectrometry Images by Probabilistic Latent Semantic Analysis

Imaging mass spectrometry (IMS) is a promising technology which allows for detailed analysis of spatial distributions of (bio)molecules in organic samples. In many current applications, IMS relies heavily on (semi)automated exploratory data analysis procedures to decompose the data into characteristic component spectra and corresponding abundance maps, visualizing spectral and spatial structure. The most commonly used techniques are principal component analysis (PCA) and independent component analysis (ICA). Both methods operate in an unsupervised manner. However, their decomposition estimates usually feature negative counts and are not amenable to direct physical interpretation. We propose probabilistic latent semantic analysis (pLSA) for non-negative decomposition and the elucidation of interpretable component spectra and abundance maps. We compare this algorithm to PCA, ICA, and non-negative PARAFAC (parallel factors analysis) and show on simulated and real-world data that pLSA and non-negative PARAFAC are superior to PCA or ICA in terms of complementarity of the resulting components and reconstruction accuracy. We further combine pLSA decomposition with a statistical complexity estimation scheme based on the Akaike information criterion (AIC) to automatically estimate the number of components present in a tissue sample data set and show that this results in sensible complexity estimates.

Different phosphorylation states of the anaphase promoting complex in response to antimitotic drugs: A quantitative proteomic analysis

The anaphase promoting complex (APC) controls the degradation of proteins during exit from mitosis and entry into S-phase. The activity of the APC is regulated by phosphorylation during mitosis. Because the phosphorylation pattern provides insights into the complexity of regulation of the APC, we studied in detail the phosphorylation patterns at a single mitotic state of arrest generated by various antimitotic drugs. We examined the phosphorylation patterns of the APC in HeLa S3 cells after they were arrested in prometaphase with taxol, nocodazole, vincristine, or monastrol. There were 71 phosphorylation sites on nine of the APC subunits. Despite the common state of arrest, the various antimitotic drug treatments resulted in differences in the phosphorylation patterns and phosphorylation stoichiometries. The relative phosphorylation stoichiometries were determined by using a method adapted from the isotope-free quantitation of the extent of modification (iQEM). We could show that during drug arrest the phosphorylation state of the APC changes, indicating that the mitotic arrest is not a static condition. We discuss these findings in terms of the variable efficacy of antimitotic drugs in cancer chemotherapy.

NITPICK: peak identification for mass spectrometry data

BackgroundThe reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.ResultsThis contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averagine, a novel extension to Senkos well-known averagine model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra.ConclusionExtensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from http://hci.iwr.uni-heidelberg.de/mip/proteomics/.

When less can yield more - Computational preprocessing of MS/MS spectra for peptide identification.

The effectiveness of database search algorithms, such as Mascot, Sequest and ProteinPilot is limited by the quality of the input spectra: spurious peaks in MS/MS spectra can jeopardize the correct identification of peptides or reduce their score significantly. Consequently, an efficient preprocessing of MS/MS spectra can increase the sensitivity of peptide identification at reduced file sizes and run time without compromising its specificity. We investigate the performance of 25 MS/MS preprocessing methods on various data sets and make software for improved preprocessing of mgf/dta‐files freely available from http://hci.iwr.uni‐heidelberg.de/mip/proteomics or http://www.childrenshospital.org/research/steenlab.

Toward digital staining using imaging mass spectrometry and random forests.

We show on imaging mass spectrometry (IMS) data that the Random Forest classifier can be used for automated tissue classification and that it results in predictions with high sensitivities and positive predictive values, even when intersample variability is present in the data. We further demonstrate how Markov Random Fields and vector-valued median filtering can be applied to reduce noise effects to further improve the classification results in a posthoc smoothing step. Our study gives clear evidence that digital staining by means of IMS constitutes a promising complement to chemical staining techniques.

Life Cycle Stage-resolved Proteomic Analysis of the Excretome/Secretome from Strongyloides ratti—Identification of Stage-specific Proteases

A wide range of biomolecules, including proteins, are excreted and secreted from helminths and contribute to the parasites successful establishment, survival, and reproduction in an adverse habitat. Excretory and secretory proteins (ESP) are active at the interface between parasite and host and comprise potential targets for intervention. The intestinal nematode Strongyloides spp. exhibits an exceptional developmental plasticity in its life cycle characterized by parasitic and free-living generations. We investigated ESP from infective larvae, parasitic females, and free-living stages of the rat parasite Strongyloides ratti, which is genetically very similar to the human pathogen, Strongyloides stercoralis. Proteomic analysis of ESP revealed 586 proteins, with the largest number of stage-specific ESP found in infective larvae (196), followed by parasitic females (79) and free-living stages (35). One hundred and forty proteins were identified in all studied stages, including anti-oxidative enzymes, heat shock proteins, and carbohydrate-binding proteins. The stage-selective ESP of (1) infective larvae included an astacin metalloproteinase, the L3 Nie antigen, and a fatty acid retinoid-binding protein; (2) parasitic females included a prolyl oligopeptidase (prolyl serine carboxypeptidase), small heat shock proteins, and a secreted acidic protein; (3) free-living stages included a lysozyme family member, a carbohydrate-hydrolyzing enzyme, and saponin-like protein. We verified the differential expression of selected genes encoding ESP by qRT-PCR. ELISA analysis revealed the recognition of ESP by antibodies of S. ratti-infected rats. A prolyl oligopeptidase was identified as abundant parasitic female-specific ESP, and the effect of pyrrolidine-based prolyl oligopeptidase inhibitors showed concentration- and time-dependent inhibitory effects on female motility. The characterization of stage-related ESP from Strongyloides will help to further understand the interaction of this unique intestinal nematode with its host.

Deuteration distribution estimation with improved sequence coverage for HX/MS experiments

MOTIVATION Time-resolved hydrogen exchange (HX) followed by mass spectrometry (MS) is a key technology for studying protein structure, dynamics and interactions. HX experiments deliver a time-dependent distribution of deuteration levels of peptide sequences of the protein of interest. The robust and complete estimation of this distribution for as many peptide fragments as possible is instrumental to understanding dynamic protein-level HX behavior. Currently, this data interpretation step still is a bottleneck in the overall HX/MS workflow. RESULTS We propose HeXicon, a novel algorithmic workflow for automatic deuteration distribution estimation at increased sequence coverage. Based on an L(1)-regularized feature extraction routine, HeXicon extracts the full deuteration distribution, which allows insight into possible bimodal exchange behavior of proteins, rather than just an average deuteration for each time point. Further, it is capable of addressing ill-posed estimation problems, yielding sparse and physically reasonable results. HeXicon makes use of existing peptide sequence information, which is augmented by an inferred list of peptide candidates derived from a known protein sequence. In conjunction with a supervised classification procedure that balances sensitivity and specificity, HeXicon can deliver results with increased sequence coverage. AVAILABILITY The entire HeXicon workflow has been implemented in C++ and includes a graphical user interface. It is available at http://hci.iwr.uni-heidelberg.de/software.php. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

mz5: Space- and Time-efficient Storage of Mass Spectrometry Data Sets

Across a host of MS-driven-omics fields, researchers witness the acquisition of ever increasing amounts of high throughput MS data and face the need for their compact yet efficiently accessible storage. Addressing the need for an open data exchange format, the Proteomics Standards Initiative and the Seattle Proteome Center at the Institute for Systems Biology independently developed the mzData and mzXML formats, respectively. In a subsequent joint effort, they defined an ontology and associated controlled vocabulary that specifies the contents of MS data files, implemented as the newer mzML format. All three formats are based on XML and are thus not particularly efficient in either storage space requirements or read/write speed. This contribution introduces mz5, a complete reimplementation of the mzML ontology that is based on the efficient, industrial strength storage backend HDF5. Compared with the current mzML standard, this strategy yields an average file size reduction to ∼54% and increases linear read and write speeds ∼3–4-fold. The format is implemented as part of the ProteoWizard project and is available under a permissive Apache license. Additional information and download links are available from http://software.steenlab.org/mz5.