Search

Here we report the use of capillary isoelectric focusing under native conditions for the separation of protein complex isoforms and subcomplexes. Using biologically relevant HIS-tsg and FLAG-tag purified protein complexes, we demonstrate the separations of protein complex isoforms of the mammalian target of rapamycin complex (mTORC1 and 2) and the subcomplexes and different phosphorylation states of the Dam1 complex. The high efficiency capillary isoelectric focusing separation allowed for resolution of protein complexes and subcomplexes similar in size and biochemical composition. By performing separations with native buffers and reduced temperature (15[degrees]C) we were able to maintain the complex integrity of the more thermolabile mTORC2 during isoelectric focusing and detection ( 10.1021/ac101235k

Electron transfer dissociation (ETD) can dissociate highly charged ions. Efficient analysis of ions dissociated with ETD requires accurate determination of charge states for calculation of molecular weight. We created an algorithm to assign the charge state of ions often used for ETD. The program, Charge Prediction Machine (CPM), uses Bayesian decision theory to account for different charge reduction processes encountered in ETD and can also handle multiplex spectra. CPM correctly assigned charge states to 98% of the 13 097 MS2 spectra from a combined data set of four experiments. In a comparison between CPM and a competing program, Charger (ThermoFisher), CPM produced half the mistakes.

Methods for the global analysis of protein expression offer an approach to study the molecular basis of disease. Studies of protein expression in tissue, such as brain, are complicated by the need for efficient and unbiased digestion of proteins that permit identification of peptides by shotgun proteomic methods. In particular, identification and characterization of less abundant membrane proteins has been of great interest for studies of brain physiology, but often proteins of interest are of low abundance or exist in multiple isoforms. Parsing protein isoforms as a function of disease will be essential. In this study, we develop a digestion scheme using detergents compatible with mass spectrometry that improves membrane protein identification from brain tissue. We show the modified procedure yields close to 5,000 protein identifications from 1.8 mg of rat brain homogenate with an average of 25% protein sequence coverage. This procedure achieves a remarkable reduction in the amount of starting material required to observe a broad spectrum of membrane proteins. Among the proteins identified from a mammalian brain homogenate, 1897 (35%) proteins are annotated by Gene Ontology as membrane proteins, and 1225 (22.6%) proteins are predicted to contain at least one transmembrane domain. Membrane proteins identified included neurotransmitter receptors and ion channels implicated in important physiological functions and disease.

We investigated and compared three approaches for shotgun protein identification by combining MS and MS/MS information using LTQ-Orbitrap high mass accuracy data. In the first approach, we employed a unique mass identifier method where MS peaks matched to peptides predicted from proteins identified from an MS/MS database search are first subtracted before using the MS peaks as unique mass identifiers for protein identification. In the second method, we used an accurate mass and time tag method by building a potential mass and retention time database from previous MudP1T analyses. For the third method, we used a peptide mass fingerprinting-like approach in combination with a randomized database for protein identification. We show that we can improve protein identification sensitivity for low-abundance proteins by combining MS and MS/MS information. Furthermore, 'one-hit wonders' from MS/MS database searching can be further substantiated by MS information and the approach improves the identification of low-abundance proteins. The advantages and disadvantages for the three approaches are then discussed.

Multidimensional liquid chromatography in combination with tandem mass spectrometry has been used to analyze a variety of biological structures including protein complexes. Incorporating this approach with autosampling devices presents a number of problems including decreased sensitivity due to exposure to extra surfaces, carryover from run to run, and increased dead volume. We developed a device, termed Radial Column Array for Distribution and Automation (RCADiA), to automate multiple MuDPIT experiments while eliminating many of these problems and maintaining a high resolution and sensitive analysis. The design, which places each sample downstream of any common fluid path, presents a low risk of carryover between successive analyses. Beyond the convenience of automation, the RCADiA platform also produces data of similar quality to the standard method of performing individual MuDPIT experiments. We demonstrate this device by performing a comparative analysis of mitochondria enriched from rat liver and spinal cord.

An automated online multidimensional liquid chromatography system coupled to ESI-based tandem mass spectrometry was used to assess the effectiveness of Ti[O.sub.2] in the enrichment of phosphopeptides from tryptic digests of protein mixtures. By monitoring the enrichment of phosphopeptides, an optimized set of loading, wash, and elution conditions were realized for Ti[O.sub.2]. A comparison of Ti02 with other resins used for phosphopeptide enrichment, Fe(III)-IMAC and Zr[O.sub.2], was also carded out using tryptic digests of both simple and moderately complex protein mixtures; where Ti[O.sub.2] was shown to be superior in performance.

Shotgun proteomics typically uses multidimensional LC/ MS/MS analysis of enzymatically digested proteins, where strong cation-exchange (SCX) and reversed-phase (RP) separations are coupled to increase the separation power and dynamic range of analysis. Here we report an on-line multidimensional LC method using an anion- and cation-exchange mixed bed for the first separation dimension. The mixed-bed ion-exchange resin improved peptide recovery over SCX resins alone and showed better orthogonality to RP separations in two-dimensional separations. The Donnan effect, which was enhanced by the introduction of fixed opposite charges in one column, is proposed as the mechanism responsible for improved peptide recovery by producing higher fluxes of salt cations and lower populations of salt anions proximal to the SCX phase. An increase in orthogonality was achieved by a combination of increased retention for acidic peptides and moderately reduced retention of neutral to basic peptides by the added anion-exchange resin. The combination of these effects led to ~100% increase in the number of identified peptides from an analysis of a tryptic digest of a yeast whole cell lysate. The application of the method to phosphopeptide-enriched samples increased by 94% phosphopeptide identifications over SCX alone. The lower pKa of phosphopeptides led to specific enrichment in a single salt step resolving acidic phosphopeptides from other phospho- and non-phosphopeptides. Unlike previous methods that use anion exchange to alter selectivity or enrich phosphopeptides, the proposed format is unique in that it works with typical acidic buffer systems used in electrospray ionization, making it feasible for online multidimensional LC/MS/MS applications.

The quantitative analysis of complex biological samples has emerged as a key research area in the field of proteomics. Although quantitative proteomic experiments remain challenging, these strategies have been greatly facilitated by the development of newer high-performance mass spectrometers. In this work, we have evaluated the use of the LTQ-Orbitrap, a hybrid mass spectrometer in which a linear ion trap is coupled to an Orbitrap mass analyzer, for quantitative analyses. By analyzing a range of yeast protein standards, we found that the high mass accuracy, high resolution, large ion capacity, and large dynamic range of the LTQ-Orbitrap led to as much as a 4-5-fold improvement in the number and quality of the peptide ratio measurements compared to similar analyses done on the LTQ. We also successfully quantified protein expression differences that occur in metabolically labeled rat synapses during brain development to further demonstrate the suitability of the LTQ-Orbitrap for the comparative analysis of complex tissue samples.

Multidimensional separation is one of the most successful approaches for proteomics studies that deal with complex samples. We have developed an automated ultra-high-pressure multidimensional liquid chromatography system that operates up to ~20 kpsi to improve separations and increase protein coverage from limited amount of samples. The reversed-phase gradient is operated in the constant-flow mode opposed to the constant-pressure mode, which is typical of previous ultra-high-pressure systems. In contrast to constant-pressure systems, the gradient shape is fully controllable and can be optimized for the type of samples to be run. The system also features fast sample loading/desalting using a vented column approach to improve sample throughput. This approach was validated on a soluble fraction from yeast lysate where we achieved ~30% more protein identifications using a 60-cm-long triphasic capillary column than with our traditional approach. Advantages of the use of a relatively long reversed-phase column (~50 cm) for MudPIT-type experiments are also discussed.

We have developed an approach to identify the molecular weight of a peptide ion directly from its corresponding tandem mass spectrum using a cross-correlation function. We have shown that the monoisotopic molecular weight can be calculated for ~90% of tandem mass spectra identified from tryptic digests of complex protein mixtures. The accuracy of the calculated monoisotopic masses was dependent on the resolution and mass accuracy of the spectra analyzed, but was typically

Proteomic analysis of digested complex protein mixtures has become a useful strategy to identify proteins involved in biological processes. We have evaluated the use of a new mass spectrometer that combines a linear ion trap and an Orbitrap to create a hybrid tandem mass spectrometer. A digested submandibular/sublingual saliva sample was used for the analysis. We find the instrument is capable of mass resolution in excess of 40 000 and mass measurement accuracies of less than 2 ppm for the analysis of complex peptide mixtures. Such high mass accuracy allowed the elimination of virtually any false positive peptide identifications, suggesting that peptides that do not match the specificity of the protease used in the digestion of the sample should not automatically be considered as false positives. Tandem mass spectra from the linear ion trap and from the Orbitrap have very similar ion abundance ratios. We conclude this instrument will be well suited for shotgun proteomic types of analyses.

In this paper, we present an intensity-based probability function to identify peptides from tandem mass spectra and amino acid sequence databases. The function is an approximation to the central limiting theorem, and it explicitly depends on the cumulative product ion intensifies, number of product ions of a peptide, and expectation value of the cumulative intensity. We compare the results of database searches using the new scoring function and scoring functions from earlier algorithms, which implement hypergeometric probability, Poisson's model, and cross-correlation scores. For a standard protein mixture (tandem mass spectra generated from the mixture of five known proteins), we generate receiver operating curves with all scoring schemes. The receiver operating curves show that the shared peaks count-based probability methods (like Poisson and hypergeometric models) are the most specific for matching high-quality tandem mass spectra. The intensity-based (central limit model) and intensity-modeled (cross-correlation) methods are more sensitive when matching low-quality tandem mass spectra, where the number of shared peaks is insufficient to correctly identify a peptide. Cross-correlation methods show a small advantage over the intensity-based probability method.

Stable isotope-enriched molecules are used as internal standards and as tracers of in vivo substrate metabolism. The accurate conversion of measured ratios in the mass spectrometer to mole ratios is complicated because a polyatomic molecule containing enriched atoms will result in a combinatorial distribution of isotopomers depending on the enrichment and number of 'labeled' atoms. This effect could potentialy cause a large error in the mole ratio measurement depending on which isotope peak or peaks were used to determine the ratio. We report a computational method that predicts isotope distributions over a range of enrichments and compares the predicted distributions to experimental peptide isotope distributions obtained by Fourier transform ion cyclotron resonance mass spectrometry. Our approach is accurate with measured enrichments within 1.5% of expected isotope distributions. The method is also precise with 4.9, 2.0, and 0.8% relative standard deviations for peptides containing 59, 79, and 99 atom % excess [sup.15]N, respectively. The approach is automated making isotope enrichment calculations possible for thousands of peptides in a single [micro]LC--FTICR-MS experiment.

To quantify proteins on a global level from mammalian tissue, a method was developed to metabolically introduce [sup.15]N stable isotopes into the proteins of Rattus norvegicus for use as internal standards. The long-term metabolic labeling of rats with a diet enriched in [sup.15]N did not result in adverse health consequences. The average [sup.15]N amino acid enrichments reflected the relative turnover rates in the different tissues and ranged from 74.3 mpe in brain to 92.2 mpe in plasma. Using the [sup.15]N-enriched liver as a quantitative internal standard, changes in individual protein levels in response to cycloheximide treatment were measured for 310 proteins. These measurements revealed 127 proteins with altered protein level (p < 0.05). Most proteins with altered level have previously reported functions involving xenobiotic metabolism and protein-folding machinery of the endoplasmic reticulum. This approach is a powerful tool for the global quantitation of proteins, is capable of measuring proteome-wide changes in response to a drug, and will be useful for studying animal models of disease.

Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.

Peptide identification based on tandem mass spectrometry and database searching algorithms has become one of the central technologies in proteomics. At the heart of this technology is the ability to reproducibly acquire high-quality tandem mass spectra for database interrogation. The variability in tandem mass spectra generation is often assumed to be minimal, and peptide identifications are typically based on a single tandem mass spectrum. In this paper, we characterize the variance of scores derived from replicate tandem mass spectra using several database search algorithms and demonstrate the effects of spectral variability on the correct identification of peptides. We show that the variance associated with the collection of tandem mass spectra can be substantial leading to sizable errors in search algorithm scores (~5-25% RSD) and ultimately incorrect assignments. Processing strategies are discussed to minimize the impact of tandem mass spectra variability on peptide identification.

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.

The primary utility of trypsin digestion in proteomics is that it cleaves proteins at predictable locations, but it is also notable for yielding peptides that terminate in basic arginine and lysine residues. Tryptic peptides fragment in ion trap tandem mass spectrometry to produce prominent C-terminal y series ions. Alternative proteolytic digests may produce peptides that do not follow these rules. In this study, we examine 2568 peptides generated through proteinase K digestion, a technique that produces a greater diversity of basic residue content in peptides. We show that the position of basic residues within peptides influences the peak intensities of b and y series ions; a basic residue near the N-terminus of a peptide can lead to prominent b series peaks rather than the intense y series peaks associated with tryptic peptides. The effects of presence and position for arginine, lysine, and histidine are explored separately and in combination. Arg shows the most dominant effects followed by His and then by Lys. Fragment ions containing basic residues produce more intense peaks than those without basic residues. Doubly charged precursor ions have generally been modeled as producing only singly charged fragment ions, but fragment ions that contain two basic residues may accept both protons during fragmentation. By characterizing the influence of basic residues on gas-phase fragmentation of peptides, this research makes possible more accurate fragmentation models for peptide identification algorithms.

Quantitative shotgun proteomic analyses are facilitated using chemical tags such as ICAT and metabolic labeling strategies with stable isotopes. The rapid high-throughput production of quantitative 'shotgun' proteomic data necessitates the development of software to automatically convert mass spectrometry-derived data of peptides into relative protein abundances. We describe a computer program called RelEx, which uses a least-squares regression for the calculation of the peptide ion current ratios from the mass spectrometry-derived ion chromatograms. RelEx is tolerant of poor signal-to-noise data and can automatically discard nonusable chromatograms and outlier ratios. We apply a simple correction for systematic errors that improves the accuracy of the quantitative measurement by 32 [+ or -] 4%. Our automated approach was validated using labeled mixtures composed of known molar ratios and demonstrated in a real sample by measuring the effect of osmotic stress on protein expression in Saccharomyces cerevisiae.

Shotgun proteomics is a powerful tool for identifying the protein content of complex mixtures via liquid chromatography and tandem mass spectrometry. The most widely used class of algorithms for analyzing mass spectra of peptides has been database search software such as SEQUEST. A new sequence tag database search algorithm, called GutenTag, makes it possible to identify peptides with unknown posttranslational modifications or sequence variations. This software automates the process of inferring partial sequence 'tags' directly from the spectrum and efficiently examines a sequence database for peptides that match these tags. When multiple candidate sequences result from the database search, the software evaluates which is the best match by a rapid examination of spectral fragment ions. We compare GutenTag's accuracy to that of SEQUEST on a defined protein mixture, showing that both modified and unmodified peptides can be successfully identified by this approach. GutenTag analyzed 33 000 spectra from a human lens sample, identifying peptides that were missed in prior SEQUEST analysis due to sequence polymorphisms and posttranslational modifications. The software is available under license; visit http://fields.scripps.edu for information.

If quantitative proteomic technologies are to be of widespread use to the biological community, the reproducibility of each method must be investigated and determined. We have analyzed the reproducibility of complex quantitative proteomic analyses of metabolically labeled S. cerevisiae analyzed via multidimensional protein identification technology (MudPIT). Three independent cell growths of S. cerevisiae grown in rich and minimal media and independent MudPIT analyses of each were compared and contrasted. Quantitative MudPIT was found to be intra- and interexperimentally reproducible at both the peptide and protein levels. Proteins of potential low abundance were detected, identified, and quantified by identical peptides from three independent samples. In addition, when multiple peptides were matched to a protein, the relative abundance of each peptide was in agreement across the three samples. Despite the reproducibility, errors in the experimental determination of protein expression levels occurred, but the impact of the variation was minimized by replicate experiments. Last, quantitative MudPIT analyses will likely be improved by increasing the number of peptide hits per protein in a given analysis, which will provide for greater intraexperimental reproducibility.

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).sup.+] 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 [chi square] 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.

Liquid chromatography paired with tandem mass spectrometry is a standard technique for identifying peptides from complex protein mixtures. Most fragment ion spectra acquired by this technique are unique, but some are repeated. Similarities among the spectra from 1D and 2D liquid chromatography experiments were calculated by the dot product algorithm. Similar spectra were grouped, and the degree of duplication was calculated for each sample. In 1D liquid chromatography data from 1D gel bands, 18% of the fragment ion spectra were duplicates. A six-cycle 2D liquid chromatographic separation of more than 200 proteins produced 28% duplicate spectra. A rat hippocampal homogenate analyzed by a 12-cycle 2D liquid chromatographic separation contained 25% duplicate spectra. Removal of these duplicate spectra, however, resulted in fewer peptides being successfully identified by SEQUEST. We propose a modification for peptide identification algorithms that would improve their performance and accuracy by explicitly recognizing and making use of spectral similarity.

Fragmentation at the Xxx-Pro bond was analyzed for a group of peptide mass spectra that were acquired in a Finnigan ion trap mass spectrometer and were generated from proteins digested by enzymes and identified by the Sequest algorithm. Cleavage with formation of a + b + y ions occurred more readily at the Xxx-Pro bond than at other locations in these peptides, and the importance of this cleavage varied by the identity of Xxx. The most abundant Xxx-Pro relative bond cleavage ratios were observed when Xxx was Val, His, Asp, Ile, and Leu, whereas the least abundant cleavage ratios occurred when Xxx was Gly or Pro. Rationalization for these cleavage ratios at Xxx-Pro may include contribution of the Asp or His side chain to enhanced cleavage or the conformation of Pro, Gly, and the aliphatic residues Val, Ile, and Leu at the Xxx location in the Xxx-Pro bond. Although unusual fragmentation behavior has been noted for Pro-containing peptides, this analysis suggests that fragmentation at the Xxx-Pro bond is predictable and that this information may be used to improve the identification of proteins if it is incorporated into peptide sequencing algorithms.

Collision-induced dissociation (CID) is a common ion activation technique used to energize mass-selected peptide ions during tandem mass spectrometry. Characteristic fragment ions form from the cleavage of amide bonds within a peptide undergoing CID, allowing the inference of its amino acid sequence. The statistical characterization of these fragment ions is essential for improving peptide identification algorithms and for understanding the complex reactions taking place during CID. An examination of 1465 ion trap spectra from doubly charged tryptic peptides reveals several trends important to understanding this fragmentation process. While less abundant than y ions, b ions are present in sufficient numbers to aid sequencing algorithms. Fragment ions exhibit a characteristic series-specific relationship between their masses and intensities. Each residue influences fragmentation at adjacent amide bonds, with Pro quantifiably enhancing cleavage at its N-terminal amide bond and His increasing the formation of b ions at its C-terminal amide bond. Fragment ions corresponding to a formal loss of ammonia appear preferentially in peptides containing Gin and Asn. These trends are partially responsible for the complexity of peptide tandem mass spectra.

Database-searching algorithms compatible with shotgun proteomics match a peptide tandem mass spectrum to a predicted mass spectrum for an amino acid sequence within a database. SEQUEST is one of the most common software algorithms used for the analysis of peptide tandem mass spectra by using a cross-correlation (XCorr) scoring routine to match tandem mass spectra to model spectra derived from peptide sequences. To assess a match, SEQUEST uses the difference between the first- and second-ranked sequences ([DELTA]Cn). This value is dependent on the database size, search parameters, and sequence homologies. In this report, we demonstrate the use of a scoring routine (SEQUEST-NORM) that normalizes XCorr values to be independent of peptide size and the database used to perform the search. This new scoring routine is used to objectively calculate the percent confidence of protein identifications and posttranslational modifications based solely on the XCorr value.

We describe the analysis of quantitative proteomic samples via multidimensional protein identification technology (MudPIT). Ratio amounts of the soluble portion of the S. cerevisiae proteome from cultures of S. cerevisiae strain S288C grown in either [sup.14]N minimal media or [sup.15]N-enriched minimal media were mixed and digested into a complex peptide mixture. A 1x [sup.14]N/1x [sup.15]N complex peptide mixture was analyzed by single-dimensional reversed-phase chromatography and electrospray ionization quadrapole time-of-flight mass spectrometry in order to demonstrate the replacement of [sup.14]N by [sup.15]N under the growth conditions used. After conformation of the incorporation of [sup.15]N into the labeled sample, three separate samples consisting of a 1 x [sup.14]N/1 x [sup.15]N complex peptide mixture, a 5x [sup.14]N/1 x [sup.15]N complex peptide mixture, and a 10x [sup.14]N/1 x [sup.15]N complex peptide mixture were analyzed via MudPIT. We demonstrate the dynamic range of the system by analyzing a 1:1, 5:1, and 10:1 data set using the soluble portion from S. cerevisiae grown in either [sup.14]N or [sup.15]N-enriched minimal media. The method described provides an accurate way to undertake a large-scale quantitative proteomic study.

We describe an automated method for shotgun proteomics named multidimensional protein identification technology (MudPIT), which combines multidimensional liquid chromatography with electrospray ionization tandem mass spectrometry. The multidimensional liquid chromatography method integrates a strong cation-exchange (SCX) resin and reversed-phase resin in a biphasic column. We detail the improvements over a system described by Link et al. (Link, A. J.; Eng, J.; Schieltz, D. M.; Carmack, E.; Mize, G. J.; Morris, D. R.; Garvik, B. M.; Yates, J. R., III. Nat. Biotechnol. 1999, 17, 676-682) that separates and acquires tandem mass spectra for thousands of peptides. Peptides elute off the SCX phase by increasing pi, and elution off the SCX material is evenly distributed across an analysis. In addition, we describe the chromatographic benchmarks of MudPIT. MudPIT was reproducible within 0.5% between two analyses. Furthermore, a dynamic range of 10 000 to 1 between the most abundant and least abundant proteins/ peptides in a complex peptide mixture has been demonstrated. By improving sample preparation along with separations, the method improves the overall analysis of proteomes by identifying proteins of all functional and physical classes.

ESI (electrospray ionization) MS and tandem mass spectrometry (MS/MS) were used for the analysis of single nucleotide polymorphisms (SNPs) and more complex genetic variations. Double-stranded (ds) PCR products were studied. PCR products of the proline [5'-(mathematical expression not reproducible in ASCII) (G(sub 17))- (mathematical expression not reproducible in ASCII) (C(sub 38)) (mathematical expression not reproducible in ASCII)-3'] and arginine variants [(5'-(mathematical expression not reproducible in ASCII)(G(sub 17))- (mathematical expression not reproducible in ASCII) (G(sub 38)) (mathematical expression not reproducible in ASCII)-3'] of the p53 gene are distinguished by an SNP (cytosine or guanine) and were discriminated using both quadrupole and quadrupole ion trap MS analysis. A 69 bp arginine mutant PCR product [5'-(mathematical expression not reproducible in ASCII)(C(sub 17))- (mathematical expression not reproducible in ASCII)(G(sub 38)) (mathematical expression not reproducible in ASCII)-3'] with a negating switch has the same mass as the proline variant but was readily distinguishable on ion trap MS/MS analysis; fragments containing the mutation site, but not the polymorphism, were identified. The 69 bp PCR products were restriction-enzyme-digested, to create 43 bp fragments. ESI quadrupole ion trap MS/MS analysis of the 43 bp product-ion spectra readily demonstrated both polymorphism and negating switch sites. MS and MS/MS are powerful and complemenmor techniques for analysis of DNA. MS can readily distinguish SNPs but MS/MS is required to differentiate isomeric PCR products (same nucleotide composition but different sequence).

Amino acid sequence variations resulting from single-nucleotide polymorphisms (SNPs) were identified using a novel mass spectrometric method. This method obtains 99+% protein sequence coverage for human hemoglobin in a single LC-microspray tandem mass spectrometry ((mu)LC-MS/MS) experiment. Tandem mass spectrometry data was analyzed using a modified version of the computer program SEQUEST to identify the sequence variations. Conditions of sample preparation, chromatographic separation, and data collection were optimized to correctly identify amino acid changes in six variants of human hemoglobin (Hb C, Hb E, Hb D-Los Angeles, Hb G-Philadelphia, Hb Hope, and Hb S). Hemoglobin proteins were isolated and purified, dehemed, (S)-carboxyamidomethylated, and then subjected to a combination proteolytic digestion to obtain a complex peptide mixture with multiple overlaps in sequence. Reversed-phase chromatographic separation of peptides was achieved on-line with MS utilizing a robust fritless microelectrospray interface. Tandem mass spectrometry was performed on an ion trap mass spectrometer using automated data-dependent MS/ MS procedures. Tandem mass spectra were collected from the five most abundant ions in each scan using dynamic and isotopic exclusion to minimize redundancy. The spectra were analyzed by a version of the SEQUEST algorithm modified to identify amino acid substations resulting from SNPs.

A method to directly identify proteins in complex mixtures by solid-phase microextraction (micro-SPE)/multistep elution/capillary electrophoresis (CE)/tandem mass spectrometry (MS/MS) is described. A sheathless liquid-metal junction interface is used to interface CE and electrospray ionization MS/MS. A subfemtomole detection limit is achieved for protein identification through database searching using MS/MS data. The SPE serves as a semiseparation dimension using an organic-phase step-elution gradient in combination with the second separation dimension for increased resolving power of complex peptide mixtures. This approach improves the concentration detection limit for CE and allows more proteins in complex mixtures to be identified. A 75-protein complex from yeast ribosome is analyzed using this method and 80-90% of the proteins in the complex can be identified by searching the database using the MS/MS data from a complete analysis. This multidimensional CE/MS/MS methodology provides an alternative to multidimensional liquid chromatography/MS/MS for direct identification of small amounts of protein in mixtures.

We report the development of a method to compare collision-induced dissociation (CID) spectra of peptides. This method employs a cross-correlation analysis of a CID spectrum to a reference spectrum and normalizes the cross-correlation score to the autocorrelation of the CID spectra. The query spectrum is compared by using both mass information and fragmentation patterns. Fragmentation patterns are compared to each other using a correlation function. To evaluate the specificity of the approach, a set of 2180 tandem mass spectra obtained from both triple-quadrupole tandem mass spectrometers (TSQ) and quadrupole ion trap mass spectrometers (LCQ) was created. Comparisons are performed between tandem mass spectra obtained on the same instrument type as well as between different instrument types. Accurate and reliable comparisons are demonstrated in both types of analyses. The scores obtained in the cross-comparison of TSQ and LCQ tandem mass spectra of the same peptide are found to be slightly lower than comparisons performed with spectra obtained on the same instrument type. The method appears insensitive to variations in day-to-day performance of the instrument, minor variations in fragment ion abundance, and instrumental differences inherent in the same instrument model. The use of this method of comparison is demonstrated for library searching and subtractive analysis of tandem mass spectra obtained during LC/MS/MS experiments.

Databases of information obtained from mass spectrometry (MS) can be utilized to identify unknown amino acid sequences and proteins. Computer algorithms have been developed to search protein sequence databases with peptide mass maps. Searching databases with a peptide mass map can also be used to correlate mass spectral data to a sequence database. By using microseparation processes in conjunction with tandem MS, immunologically significant peptides have been sequenced.

A method to correlate uninterpreted tandem mass spectra of modified peptides, produced under low-energy (10-50 eV) collision conditions, with amino acid sequences in a protein database has been developed. The fragmentation patterns observed in the tandem mass spectra of peptides containing covalent modifications is used to directly search and fit linear amino acid sequences in the database. Specific information relevant to sites of modification is not contained in the character-based sequence information of the databases. The search method considers each putative modification site as both modified and unmodified in one pass through the database and simultaneously considers up to three different sites of modification. The search method will identify the correct sequence if the tandem mass spectrum did not represent a modified peptide. This approach is demonstrated with peptides containing modifications such as S-carboxymethylated cysteine, oxidized methionine, phosphoserine, phosphothreonine, or phosphotyrosine. In addition, a scanning approach is used in which neutral loss scans are used to initiate the acquisition of product ion MS/MS spectra of doubly charged phosphorylated peptides during a single chromatographic run for data analysis with the database-searching algorithm. The approach described in this paper provides a convenient method to match the nascent tandem mass spectra of modified peptides to sequences in a protein database and thereby identify previously unknown sites of modification.