Tandem Mass Spectral Data Research Articles

PEPPeR, a Platform for Experimental Proteomic Pattern Recognition

Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed "Landmark Matching" and "Peak Matching" that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery.

An evaluation for cross‐species proteomics research by publicly available expressed sequence tag database search using tandem mass spectral data

With 1383 tandem mass spectra derived from 120 individual protein spots separated by the two-dimensional (2-D) gel electrophoresis of protein samples from three different species, comparative analyses were performed by searching the Expressed Sequence Tag (EST) database (DB) and the NCBI non-redundant (nr) DB of green plants, respectively, which uses the Mascot search engine to establish a statistical basis. It was confirmed that the former could identify more peptides manually validated by de novo sequencing (DNS) from fewer species in more closely phylogenetic relationships than the latter in a statistically significant manner. Our data demonstrated that correct peptide identifications were given low Mascot scores (e.g. 6-14) and incorrect peptide identifications were given high Mascot scores (e.g. 68-83). Our data also showed that the current evaluation approaches to protein assignments are unsatisfactory because a few 'false-positive' proteins are recognized and several 'false-negative' proteins are rescued by manual validation.

Identification of the Major Site of O-Linked β-N-Acetylglucosamine Modification in the C Terminus of Insulin Receptor Substrate-1

Signal transduction from the insulin receptor to downstream effectors is attenuated by phosphorylation at a number of Ser/Thr residues of insulin receptor substrate-1 (IRS-1) resulting in resistance to insulin action, the hallmark of type II diabetes. Ser/Thr residues can also be reversibly glycosylated by O-linked beta-N-acetylglucosamine (O-GlcNAc) monosaccharide, a dynamic posttranslational modification that offers an alternative means of protein regulation to phosphorylation. To identify sites of O-GlcNAc modification in IRS-1, recombinant rat IRS-1 isolated from HEK293 cells was analyzed by two complementary mass spectrometric methods. Using data-dependent neutral loss MS3 mass spectrometry, MS/MS data were scanned for peptides that exhibited a neutral loss corresponding to the mass of N-acetylglucosamine upon dissociation in an ion trap. This methodology provided sequence coverage of 84% of the protein, permitted identification of a novel site of phosphorylation at Thr-1045, and facilitated the detection of an O-GlcNAc-modified peptide of IRS-1 at residues 1027-1073. The level of O-GlcNAc modification of this peptide increased when cells were grown under conditions of high glucose with or without chronic insulin stimulation or in the presence of an inhibitor of the O-GlcNAcase enzyme. To map the exact site of O-GlcNAc modification, IRS-1 peptides were chemically derivatized with dithiothreitol following beta-elimination and Michael addition prior to LC-MS/MS. This approach revealed Ser-1036 as the site of O-GlcNAc modification. Site-directed mutagenesis and Western blotting with an anti-O-GlcNAc antibody suggested that Ser-1036 is the major site of O-GlcNAc modification of IRS-1. Identification of this site will facilitate exploring the biological significance of the O-GlcNAc modification.

Interpretation of Shotgun Proteomic Data

The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the state of protein sequence databases and their role in shotgun proteomic analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, the integration of proteomic and transcriptional data, and the development of a computational infrastructure for the integration of multiple diverse datasets.

Enhanced Shutoff of Phototransduction in Transgenic Mice Expressing Palmitoylation-deficient Rhodopsin*♦

Palmitoylation is a reversible, post-translational modification observed in a number of G-protein-coupled receptors. To gain a better understanding of its role in visual transduction, we produced transgenic knock-in mice that expressed a palmitoylation-deficient rhodopsin (Palm(-/-)). The mutant rhodopsin was expressed at wild-type levels and showed normal cellular localization to rod outer segments, indicating that neither rhodopsin stability nor its intracellular trafficking were compromised. But Palm(-/-) rods had briefer flash responses and reduced sensitivity to flashes and to steps of light. Upon exposure to light, rhodopsin became phosphorylated at a faster rate in mutant than in wild-type retinas. Since quench of rhodopsin begins with its phosphorylation, these results suggest that palmitoylation may modulate rod photoreceptor sensitivity by permitting rhodopsin to remain active for a longer period.

Analysis of Benzylisoquinoline-Type Alkaloids by Electrospray Tandem Mass Spectrometry and Atmospheric Pressure Photoionization

Benzylisoquinoline alkaloids found in the Papaveraceae family play a major role in pharmaceutical biology. This is the first systematic study dealing with electrospray tandem mass spectrometry (ESI-MS/MS) of all benzylisoquinolines found as biogenetic precursors of morphinan alkaloids. Tandem mass spectral data are presented for norlaudanosoline, laudanosoline, 4'-O-methyl-norlaudanosoline, 6-O-methyl-norlaudanosoline, norcoclaurine, coclaurine, N-methylcoclaurine, N-methyl-3'-hydroxycoclaurine, N-methyl-3'-O-methylcoclaurine, norreticuline and reticuline. This study compares results obtained using an ion trap mass spectrometer with those obtained using a triple quadrupole one. The results highlight the differences of the tandem-in-time versus the tandem-in-space principle, often hampering the development of ESI-MS/MS libraries. Additionally, the use of the atmospheric pressure photoionisation technique for the analysis of such substances is discussed.

Organic Environments on Saturn’s Moon, Titan: Simulating Chemical Reactions and Analyzing Products by FT-ICR and Ion-Trap Mass Spectrometry

Laboratory simulations have been carried out to model chemical reactions that possibly take place in the stratosphere of Saturn's moon, Titan. The aerosol products of these reactions (tholin samples) have been systematically analyzed by mass spectrometry using electrospray ionization (ESI) and laser desorption (LD). A wide variety of ions with a general formula C(x)H(y)N(z) detected by ultrahigh resolution and accurate mass measurements in a Fourier transform/ion cyclotron resonance (FT-ICR) cell reflect the complexity of these polymeric products, both in chemical compositions and isomeric distributions. As a common feature, however, tandem mass spectral (MS/MS) data and H/D exchange products in the solution phase support the presence of amino and nitrile functionalities in these (highly unsaturated) "tholin" compounds. The present work demonstrates that ESI-MS coupled with FT-ICR is a suitable and "intact" method to analyze tholin components formed under anaerobic conditions; only species with C(x)H(y)N(z) are detected for freshly prepared and harvested samples. However, when intentionally exposed to water, oxygen-containing compounds are unambiguously detected.

A proteomic tool for protein identification from tandem mass spectral data

Instead of using the probability mean, a simple and yet effective heuristic approach was employed to treat experimentally obtained tandem mass spectrometry (MS/MS) data for protein identification. The proposed approach is based on the total number (T) of identified experimental MS/MS data. To warrant the subsequent ranking, the total number of identified b- and y-type ions (Tb+y) must be greater than 50% of T. Peptides having the same T and Tb+y are either ranked by the contiguity of identified ions or discarded during identification. When compared to other protein identification tools, good agreement with the searched results was seen.

Proteomic Profiling of Pancreatic Cancer for Biomarker Discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.

ANALYSIS OF PROSTATE CANCER BY PROTEOMICS USING TISSUE SPECIMENS

Prostate cancer cells differ from their normal counterpart in gene expression. Therefore, cancer and normal tissue/cells would show differences in their proteomes. From a diagnostic standpoint differences in the composition of secreted protein species are especially relevant. We used ProteinChip Array (Ciphergen Biosystems, Fremont, California) surface enhanced laser desorption/ionization (SELDI) time of flight mass spectrometry to profile prostate tissue samples to generate phenomic fingerprints. We used quantitative proteomics based on glycopeptide capture followed by tandem mass spectrometry to identify differentially expressed proteins. Patient matched cancer and noncancer specimens were digested by collagenase to single cells. After digestion the cells were pelleted and the cell-free supernatant was used for analysis. A reversed phase hydrophobic ProteinChip Array was used to generate SELDI patterns from 43 primary prostate tumors, including 26 with matched noncancer specimens. Quantitative proteomics was applied to 1 specimen and the expression pattern was verified by Western blotting and immunohistochemistry. SELDI profiles showed that cancers of similar TNM stages were more likely to have similar profiles. On quantitative proteomics tissue metalloproteinase inhibitor-1 was identified to be down-regulated in cancer. Tissue metalloproteinase inhibitor-1 expression was localized to secretory cells. Protein profiling by SELDI is relatively easy to perform and it has great potential in prostate cancer diagnosis through pattern recognition. Quantitative proteomics can potentially determine the identity of many biomarkers specific for prostate cancer.

Mass spectrometry as an emerging tool for systems biology.

Completion of the human genome project has created a blueprint of the genes and proteins necessary to construct and maintain a complex organism. Understanding how the molecular entities of human cells and tissues function will require sophisticated experiments to decipher how these molecules interact as a system. Beyond genes and proteins, metabolites encompass another important level of the system. To understand cellular systems, high-throughput strategies to investigate the functions of proteins and metabolites within the context of the system are necessary. Thus, understanding gene function (and ultimately biological systems) will require methods to broadly and quickly determine how the amounts and forms of various molecules are changing (Figure 1). Three considerations are relevant: (i) broad and unbiased measurement tools; (ii) comprehensive separation techniques; and (iii) informatics to analyze the data. This article will discuss mass spectrometry for the measurement of proteins and metabolites.

Statistical models for protein validation using tandem mass spectral data and protein amino acid sequence databases.

The purpose of this work is to develop and verify statistical models for protein identification using peptide identifications derived from the results of tandem mass spectral database searches. Recently we have presented a probabilistic model for peptide identification that uses hypergeometric distribution to approximate fragment ion matches of database peptide sequences to experimental tandem mass spectra. Here we apply statistical models to the database search results to validate protein identifications. For this we formulate the protein identification problem in terms of two independent models, two-hypothesis binomial and multinomial models, which use the hypergeometric probabilities and cross-correlation scores, respectively. Each database search result is assumed to be a probabilistic event. The Bernoulli event has two outcomes: a protein is either identified or not. The probability of identifying a protein at each Bernoulli event is determined from relative length of the protein in the database (the null hypothesis) or the hypergeometric probability scores of the protein's peptides (the alternative hypothesis). We then calculate the binomial probability that the protein will be observed a certain number of times (number of database matches to its peptides) given the size of the data set (number of spectra) and the probability of protein identification at each Bernoulli event. The ratio of the probabilities from these two hypotheses (maximum likelihood ratio) is used as a test statistic to discriminate between true and false identifications. The significance and confidence levels of protein identifications are calculated from the model distributions. The multinomial model combines the database search results and generates an observed frequency distribution of cross-correlation scores (grouped into bins) between experimental spectra and identified amino acid sequences. The frequency distribution is used to generate p-value probabilities of each score bin. The probabilities are then normalized with respect to score bins to generate normalized probabilities of all score bins. A protein identification probability is the multinomial probability of observing the given set of peptide scores. To reduce the effect of random matches, we employ a marginalized multinomial model for small values of cross-correlation scores. We demonstrate that the combination of the two independent methods provides a useful tool for protein identification from results of database search using tandem mass spectra. A receiver operating characteristic curve demonstrates the sensitivity and accuracy level of the approach. The shortcomings of the models are related to the cases when protein assignment is based on unusual peptide fragmentation patterns that dominate over the model encoded in the peptide identification process. We have implemented the approach in a program called PROT_PROBE.

Direct electrospray tandem mass spectrometry of the unstable hydroperoxy bishemiacetal product derived from cholesterol ozonolysis

Cholesterol is the most abundant neutral lipid in the epithelial lining fluid of the lower airways of the lung also known as pulmonary surfactant and a potential target for reaction with ambient ozone when inspired into the human lung. The isolated double bond of cholesterol has been shown to be susceptible to attack by ozone, but the major reaction product from cholesterol ozonolysis had been remarkably difficult to structurally characterize. Recently, NMR and X-ray crystallography have been used to suggest the formation of a hydroperoxy, hydroxy hemiacetal product, using various derivatives and models of cholesterol to stabilize this chemically reactive product. Electrospray ionization mass spectrometry was used to study the somewhat unstable ozonolysis product of cholesterol which was found to display unique ionization and fragmentation properties when collisionally activated. The electron-deficient carbon atoms of this highly oxygenated product permitted covalent attachment of an acetate anion during negative ion electrospray ionization, leading to the formation of abundant adduct ions at m/ z 511. Surprisingly, positive ions were not readily formed. Collision induced dissociation of the adduct anion yielded a major ion at m/ z 477, corresponding to the loss of hydrogen peroxide. The most abundant fragment ion following collisional activation was observed at m/ z 93, resulting from a complex rearrangement subsequent to the attack of the hydroperoxide anion on the carbon center of the acetate adduct. Based on the interpretation of the tandem mass spectral data, the major cholesterol ozonization product was characterized as a hydroperoxy, hydroxy hemiacetal derivative, which was consistent with the NMR and X-ray crystallographic studies which were carried out on the more stable methyl ether derivative.

Efficiency improvement of peptide identification for an organism without complete genome sequence, using expressed sequence tag database and tandem mass spectral data.

We compared peptide identification by database (DB) search methods with de novo sequencing results for proteomics study in an organism without genome sequence information. When the former was done by searching the Expressed Sequence Tag (EST) DB of the sample organism or the NCBI nonredundant (nr) protein DB of green plants using either the MASCOT or SEQUEST software program, it was confirmed that the former is as accurate as the latter. Peptides identified from EST DB were twice as many as those from the nr protein DB, in spite of the fact that the EST DB has less data (26 222 EST) than the NCBI nr protein DB (224 238). This study demonstrates that EST DB with tandem mass spectra can be used reliably for high-throughput proteomics studies in an organism without genome information.

Polydispersity of a mammalian chaperone: mass spectrometry reveals the population of oligomers in alphaB-crystallin.

The quaternary structure of the polydisperse mammalian chaperone alphaB-crystallin, a member of the small heat-shock protein family, has been investigated by using electrospray mass spectrometry. The intact assemblies give rise to mass spectra that are complicated by the overlapping of charge states from the different constituent oligomers. Therefore, to determine which oligomers are formed by this protein, tandem mass spectrometry experiments were performed. The spectra reveal a distribution, primarily of oligomers containing 24-33 subunits, the relative populations of which were quantified, to reveal a dominant species being composed of 28 subunits. Additionally, low levels of oligomers as small as 10-mers and as large as 40-mers were observed. Interpretation of the tandem mass spectral data was confirmed by simulating and summing spectra arising from the major individual oligomers. The ability of mass spectrometry to quantify the relative populations of particular oligomeric states also revealed that, contrary to the dimeric associations observed in other small heat-shock proteins, there is no evidence for any stable substructures of bovine alphaB-crystallin isolated from the lens.

A hypergeometric probability model for protein identification and validation using tandem mass spectral data and protein sequence databases.

We present a new probability-based method for protein identification using tandem mass spectra and protein databases. The method employs a hypergeometric distribution to model frequencies of matches between fragment ions predicted for peptide sequences with a specific (M + H)+ value (at some mass tolerance) in a protein sequence database and an experimental tandem mass spectrum. The hypergeometric distribution constitutes null hypothesis-all peptide matches to a tandem mass spectrum are random. It is used to generate a score characterizing the randomness of a database sequence match to an experimental tandem mass spectrum and to determine the level of significance of the null hypothesis. For each tandem mass spectrum and database search, a peptide is identified that has the least probability of being a random match to the spectrum and the corresponding level of significance of the null hypothesis is determined. To check the validity of the hypergeometric model in describing fragment ion matches, we used chi2 test. The distribution of frequencies and corresponding hypergeometric probabilities are generated for each tandem mass spectrum. No proteolytic cleavage specificity is used to create the peptide sequences from the database. We do not use any empirical probabilities in this method. The scores generated by the hypergeometric model do not have a significant molecular weight bias and are reasonably independent of database size. The approach has been implemented in a database search algorithm, PEP_PROBE. By using a large set of tandem mass spectra derived from a set of peptides created by digestion of a collection of known proteins using four different proteases, a false positive rate of 5% is demonstrated.

A Suboptimal Algorithm for De Novo Peptide Sequencing via Tandem Mass Spectrometry

Tandem mass spectrometry has emerged to be one of the most powerful high-throughput techniques for protein identification. Tandem mass spectrometry selects and fragments peptides of interest into N-terminal ions and C-terminal ions, and it measures the mass/charge ratios of these ions. The de novo peptide sequencing problem is to derive the peptide sequences from given tandem mass spectral data of k ion peaks without searching against protein databases. By transforming the spectral data into a matrix spectrum graph G = (V, E), where |V| = O(k(2)) and |E| = O(k(3)), we give the first polynomial time suboptimal algorithm that finds all the suboptimal solutions (peptides) in O(p|E|) time, where p is the number of solutions. The algorithm has been implemented and tested on experimental data. The program is available at http://hto-c.usc.edu:8000/msms/menu/denovo.htm.

ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data.

With the recent quick expansion of DNA and protein sequence databases, intensive efforts are underway to interpret the linear genetic information of DNA in terms of function, structure, and control of biological processes. The systematic identification and quantification of expressed proteins has proven particularly powerful in this regard. Large-scale protein identification is usually achieved by automated liquid chromatography-tandem mass spectrometry of complex peptide mixtures and sequence database searching of the resulting spectra [Aebersold and Goodlett, Chem. Rev. 2001, 101, 269-295]. As generating large numbers of sequence-specific mass spectra (collision-induced dissociation/CID) spectra has become a routine operation, research has shifted from the generation of sequence database search results to their validation. Here we describe in detail a novel probabilistic model and score function that ranks the quality of the match between tandem mass spectral data and a peptide sequence in a database. We document the performance of the algorithm on a reference data set and in comparison with another sequence database search tool. The software is publicly available for use and evaluation at http://www.systemsbiology.org/research/software/proteomics/ProbID.

Complementary Profiling of Gene Expression at the Transcriptome and Proteome Levels in Saccharomyces cerevisiae

Using an integrated genomic and proteomic approach, we have investigated the effects of carbon source perturbation on steady-state gene expression in the yeast Saccharomyces cerevisiae growing on either galactose or ethanol. For many genes, significant differences between the abundance ratio of the messenger RNA transcript and the corresponding protein product were observed. Insights into the perturbative effects on genes involved in respiration, energy generation, and protein synthesis were obtained that would not have been apparent from measurements made at either the messenger RNA or protein level alone, illustrating the power of integrating different types of data obtained from the same sample for the comprehensive characterization of biological systems and processes.

Application of the high mass accuracy capabilities of FT-ICR-MS and Q-ToF-MS to the characterisation of arsenic compounds in complex biological matrices

This communication details our recent developments in the application of electrospray mass spectrometry to the field of arsenic speciation. The experiments detailed demonstrate that recognition of individual arsenic-containing compounds can be achieved in electrospray mass spectra of biological extracts containing at least fifty different compounds. The excellent mass accuracy and resolution obtainable with modern mass spectrometers makes this process possible. This work demonstrates the first application, as far as the authors are aware, of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR) to the analysis of organoarsenic compounds. The excellent mass accuracy of FT-ICR is exploited to assign elemental compositions and thus identify ions generated from arsenic-containing compounds. This extends to the tandem mass spectral data, where product ions could be assigned as arsenic-containing or non-arsenic-containing. As FT-ICR instrumentation is expensive and not readily available to most arsenic-chemists the approach developed for use with FT-ICR was also evaluated using a hybrid quadrupole–time-of-flight (Q-ToF) instrument. It is shown that the low parts per million (ppm) mass accuracies obtained using these instruments, although not as great as those obtainable with FT-ICR technology, still enable the characterisation of arsenic-containing compounds in a crudely fractionated kelp extract. These results offer new perspectives for the rapid recognition, and subsequent identification, of unknown arsenic species in crude extracts, without the need for extensive purification or previously characterised standards.