Improvements in protein identification by MALDI-TOF-MS peptide mapping

A new strategy for identifying proteins in sequence data-bases by MALDI-MS peptide mapping is reported. The strategy corrects for systematic deviations of determined peptide molecular masses using information contained in the opened database and thereby renders unnecessary internal spectrum calibration. As a result, data acquisition is simplified and less error prone. Performance of the new strategy is demonstrated by identification of a set of recombinant, human cDNA expression products as well as native proteins isolated from crude mouse brain extracts by 2-D electrophoresis. Using one set of calibration constants for the mass spectrometric analyses, 20 proteins were identified without applying any molecular weight restrictions, which was not possible without data correction. A sequence database search program has been written that performs all necessary calculations automatically, access to which will be provided to the scientific community in the Internet.     

Identification of bacteria using tandem mass spectrometry combined with a proteome database and statistical scoring

Detection and identification of pathogenic bacteria and their protein toxins play a crucial role in a proper response to natural or terrorist-caused outbreaks of infectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens new diagnostic possibilities for identification of bacteria by retrieving their genomic or proteomic information. We describe a method for identification of bacteria based on tandem mass spectrometric (MS/MS) analysis of peptides derived from bacterial proteins. This method involves bacterial cell protein extraction, trypsin digestion, liquid chromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MS spectral matching results for bacterial identification. To facilitate spectral data searching, a proteome database was constructed by translating genomes of bacteria of interest with fully or partially determined sequences. In this work, a prototype database was constructed by the automated analysis of 87 publicly available, fully sequenced bacterial genomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done by SEQUEST. To gauge the relative significance of the SEQUEST-generated matching parameters for correct peptide assignment, discriminant function (DF) analysis of these parameters was applied and DF scores were used to calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. The peptides with DF scores exceeding a threshold value determined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filtering or removal of degenerate peptides matched with multiple bacteria was then performed to further improve identification. It is demonstrated that using a preset criterion with known distributions of discriminant function scores and probabilities of correct peptide sequence assignments, a test bacterium within the 87 database microorganisms can be unambiguously identified.     

Stable-isotope dimethyl labeling for quantitative proteomics

In this paper, we report a novel, stable-isotope labeling strategy for quantitative proteomics that uses a simple reagent, formaldehyde, to globally label the N-terminus and epsilon-amino group of Lys through reductive amination. This labeling strategy produces peaks differing by 28 mass units for each derivatized site relative to its nonderivatized counterpart and 4 mass units for each derivatized isotopic pair. This labeling reaction is fast (less than 5 min) and complete without any detectable byproducts based on the analysis of MALDI and LC/ESI-MS/MS spectra of both derivatized and nonderivatized peptide standards and tryptic peptides of hemoglobin molecules. The intensity of the a(1) and y(n-1) ions produced, which were not detectable from most of the nonderivatized fragments, was substantially enhanced upon labeling. We further tested the method based on the analysis of an isotopic pair of peptide standards and a pair of defined protein mixtures with known H/D ratios. Using LC/MS for quantification and LC/MS/MS for peptide sequencing, the results show a negligible isotopic effect, a good mass resolution between the isotopic pair, and a good correlation between the experimental and theoretical data (errors 0-4%). The relative standard deviation of H/D values calculated from peptides deduced from the same protein are less than 13%. The applicability of the method for quantitative protein profiling was also explored by analyzing changes in nuclear protein abundance in an immortalized E7 cell with and without arsenic treatment.     

Statistical model for large-scale peptide identification in databases from tandem mass spectra using SEQUEST

Recent technological advances have made multidimensional peptide separation techniques coupled with tandem mass spectrometry the method of choice for high-throughput identification of proteins. Due to these advances, the development of software tools for large-scale, fully automated, unambiguous peptide identification is highly necessary. In this work, we have used as a model the nuclear proteome from Jurkat cells and present a processing algorithm that allows accurate predictions of random matching distributions, based on the two SEQUEST scores Xcorr and DeltaCn. Our method permits a very simple and precise calculation of the probabilities associated with individual peptide assignments, as well as of the false discovery rate among the peptides identified in any experiment. A further mathematical analysis demonstrates that the score distributions are highly dependent on database size and precursor mass window and suggests that the probability associated with SEQUEST scores depends on the number of candidate peptide sequences available for the search. Our results highlight the importance of adjusting the filtering criteria to discriminate between correct and incorrect peptide sequences according to the circumstances of each particular experiment.     

Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper, we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors and decreases the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high-accuracy mass measurements.     

Protein identification with a single accurate mass of a cysteine-containing peptide and constrained database searching

A method for rapid and unambiguous identification of proteins by sequence database searching using the accurate mass of a single peptide and specific sequence constraints is described. Peptide masses were measured using electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry to an accuracy of 1 ppm. The presence of a cysteine residue within a peptide sequence was used as a database searching constraint to reduce the number of potential database hits. Cysteine-containing peptides were detected within a mixture of peptides by incorporating chlorine into a general alkylating reagent specific for cysteine residues. Secondary search constraints included the specificity of the protease used for protein digestion and the molecular mass of the protein estimated by gel electrophoresis. The natural isotopic distribution of chlorine encoded the cysteine-containing peptide with a distinctive isotopic pattern that allowed automatic screening of mass spectra. The method is demonstrated for a peptide standard and unknown proteins from a yeast lysate using all 6118 possible yeast open reading frames as a database. As judged by calculation of codon bias, low-abundance proteins were identified from the yeast lysate using this new method but not by traditional methods such as tandem mass spectrometry via data-dependent acquisition or mass mapping.     

DBDigger: reorganized proteomic database identification that improves flexibility and speed

Database search identification algorithms, such as Sequest and Mascot, constitute powerful enablers for proteomic tandem mass spectrometry. We introduce DBDigger, an algorithm that reorganizes the database identification process to remove a problematic bottleneck. Typically such algorithms determine which candidate sequences can be compared to each spectrum. Instead, DBDigger determines which spectra can be compared to each candidate sequence, enabling the software to generate candidate sequences only once for each HPLC separation rather than for each spectrum. This reorganization also reduces the number of times a spectrum must be predicted for a particular candidate sequence and charge state. As a result, DBDigger can accelerate some database searches by more than an order of magnitude. In addition, the software offers features to reduce the performance degradation introduced by posttranslational modification (PTM) searching. DBDigger allows researchers to specify the sequence context in which each PTM is possible. In the case of CNBr digests, for example, modified methionine residues can be limited to occur only at the C-termini of peptides. Use of "context-dependent" PTM searching reduces the performance penalty relative to traditional PTM searching. We characterize the performance possible with DBDigger, showcasing MASPIC, a new statistical scorer. We describe the implementation of these innovations in the hope that other researchers will employ them for rapid and highly flexible proteomic database search.     

A calibration method that simplifies and improves accurate determination of peptide molecular masses by MALDI-TOF MS

The use of delayed ion extraction in MALDI time-of-flight mass spectrometry distorts the linear relationship between m/z and the square of the ion flight time (t2) with the consequence that, if a mass accuracy of 10 ppm or better is to be obtained, the calibrant signals have to fall close to the analyte signals. If this is not possible, systematic errors arise. To eliminate these, a higher-order calibration function and thus several calibrant signals are required. For internal calibration, however, this approach is limited by signal suppression effects and the increasing chance of the calibrant signals overlapping with analyte signals. If instead the calibrants are prepared separately, this problem is replaced by an other; i.e., the ion flight times are dependent on the sample plate position. For this reason, even if the calibrants are placed close to the sample, the mass accuracy is not improved when a higher-order calibration function is applied. We have studied this phenomenon and found that the relative errors, which result when moving from one sample to the next, are directly proportional to m/z. Based on this observation, we developed a two-step calibration method, that overcomes said limitations. The first step is an external calibration with a high-order polynomial function used for the determination of the relation between m/z and t2, and the second step is a first-order internal correction for sample position-dependent errors. Applying this method, for instance, to a mass spectrum of a mixture of 18 peptides from a tryptic digest of a recombinant protein resulted in an average mass error of 1.0 ppm with a standard deviation of 3.5 ppm. When instead using a conventional two-point internal calibration, the average relative error was 2.2 ppm with a standard deviation of 15 ppm. The new method is described and its performance is demonstrated with examples relevant to proteome research.     

Optimum speed of visual inspection using a systematic search strategy

In an earlier paper, the authors presented models of human functioning in visual inspection and derived optimal working speeds to balance the cost of time and the cost of errors. Both self-paced and externally paced models assumed a random search strategy. It was noted that choosing a standard speed for visual inspection is difficult with standard work measurement techniques and that the data required for the suggested search model are easily measurable. The purpose of this paper is to complement the earlier work by deriving similar models employing a systematic search strategy. The two models can be considered as bounds on actual performance; thus the current paper complements the previous one by establishing an upper bound rather than a lower bound on performance in search tasks. Comparisons between the two strategies are presented.     

Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search

We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.     

Lookup peaks: a hybrid of de novo sequencing and database search for protein identification by tandem mass spectrometry

A powerful technique for peptide and protein identification is tandem mass spectrometry followed by database search using a program such as SEQUEST or Mascot. These programs, however, become slow and lose sensitivity when allowing nonspecific cleavages or peptide modifications. De novo sequencing and hybrid methods such as sequence tagging offer speed and robustness for wider searches, yet these approaches require better spectra with more complete and consecutive fragmentation and, hence, are less sensitive to low-abundance peptides. Here we describe a new hybrid method that retains the sensitivity of pure database search. The method uses a small amount of de novo analysis to identify likely b- and y-ion peaks--"lookup peaks"--that can then be used to extract candidate peptides from the database, with the number of candidates tunable to fit a computing budget. We describe a program called ByOnic that implements this method, and we benchmark ByOnic on several data sets, including one of mouse blood plasma spiked with low concentrations of recombinant human proteins. We demonstrate that ByOnic is more sensitive than sequence tagging and, indeed, more sensitive than the three most popular pure database search tools--SEQUEST, Mascot, and X!Tandem--on both the peptide and protein levels. On the mouse plasma samples, ByOnic consistently found spiked proteins missed by the other tools.     

N-terminal isotope tagging strategy for quantitative proteomics: results-driven analysis of protein abundance changes

Comparing the relative abundance of each protein present in two or more complex samples can be accomplished using isotope-coded tags incorporated at the peptide level. Here we describe a chemical labeling strategy for the incorporation of a single isotope label per peptide, which is completely sequence-independent so that it potentially labels every peptide from a protein including those containing posttranslational modifications. It is based on a gentle chemical labeling strategy that specifically labels the N-terminus of all peptides in a digested sample with either a d5- or d0-propionyl group. Lysine side chains are blocked by guanidination prior to N-terminal labeling to prevent the incorporation of multiple labels. In this paper, we describe the optimization of this N-terminal isotopic tagging strategy and validate its use for peptide-based protein abundance measurements with a 10-protein standard mixture. Using a results-driven strategy, which targets proteins for identification based on MALDI TOF-MS analysis of isotopically labeled peptide pairs, we also show that this labeling strategy can detect a small number of differentially expressed proteins in a mixture as complex as a yeast cell lysate. Only peptides that show a difference in relative abundance are targeted for identification by tandem MS. Despite the fact that many peptides are quantitated, only those few showing a difference in abundance are targeted for protein identification. Proteins are identified by either targeted LC-ES MS/MS or MALDI TOF/TOF. Identifications can be accomplished equally well by either technique on the basis of multiple peptides. This increases the confidence level for both identification and quantitation. The merits of ES MS/MS or MALDI MS/MS for protein identification in a results-driven strategy are discussed.     

PICquant: a quantitative platform to measure differential peptide abundance using dual-isotopic labeling with 12C6- and 13C6-phenyl isocyanate

We have developed a complete system for the isotopic labeling, fractionation, and automated quantification of differentially expressed peptides that significantly facilitates candidate biomarker discovery. We describe a new stable mass tagging reagent pair, (12)C(6)- and (13)C(6)-phenyl isocyanate (PIC), that offers significant advantages over currently available tags. Peptides are labeled predominantly at their amino termini and exhibit elution profiles that are independent of label isotope. Importantly, PIC-labeled peptides have unique neutral-mass losses upon CID fragmentation that enable charge state and label isotope identification and, thereby, decouple the sequence identification from the quantification of candidate biomarkers. To exploit these properties, we have coupled peptide fractionation protocols with a Thermo LTQ-XL LC-MS(2) data acquisition strategy and a suite of automated spectrum analysis software that identifies quantitative differences between labeled samples. This approach, dubbed the PICquant platform, is independent of protein sequence identification and excludes unlabeled peptides that otherwise confound biomarker discovery. Application of the PICquant platform to a set of complex clinical samples showed that the system allows rapid identification of peptides that are differentially expressed between control and patient groups.     

Ultrahigh-speed calculation of isotope distributions

This paper introduces an ultrahigh-speed algorithm for calculating isotope distributions from molecular formulas, elemental isotopic masses, and elemental isotopic abundances. For a given set of input data (molecular formula, elemental isotopic masses, and elemental isotopic abundances), and assuming round-off error to be negilgible, the new algorithm rigorously produces isotope distributions whose mean and standard deviation are "correct" in the sense that an error-free algorithm would produce a distribution having the same mean and standard deviation. The peak heights are also "correct" in the sense that the height of each nominal isotope peak from the ultrahigh-speed calculation equals the integrated peak area of the corresponding nominal isotope peak from an exact calculation. As a consequence of these properties, the algorithm generally places isotope peaks within millidaltons of their true centroids. The method uses Fourier transform methods and relates closely to two other recently introduced algorithms. The suite of capabilities provided by these three algorithms is sufficient to solve an extremely wide range of problems requiring isotope distribution simulation.     

Colorimetry for CRT displays

We analyze the sources of error in specifying color in CRT displays. These include errors inherent in the use of the color matching functions of the CIE 1931 standard observer when only colorimetric, not radiometric, calibrations are available. We provide transformation coefficients that prove to correct the deficiencies of this observer very well. We consider four different candidate sets of cone sensitivities. Some of these differ substantially; variation among candidate cone sensitivities exceeds the variation among phosphors. Finally, the effects of the recognized forms of observer variation on the visual responses (cone excitations or cone contrasts) generated by CRT stimuli are investigated and quantitatively specified. Cone pigment polymorphism gives rise to variation of a few per cent in relative excitation by the different phosphors--a variation larger than the errors ensuing from the adoption of the CIE standard observer, though smaller than the differences between some candidate cone sensitivities. Macular pigmentation has a larger influence, affecting mainly responses to the blue phosphor. The estimated combined effect of all sources of observer variation is comparable in magnitude with the largest differences between competing cone sensitivity estimates but is not enough to disrupt very seriously the relation between the L and M cone weights and the isoluminance settings of individual observers. It is also comparable with typical instrumental colorimetric errors, but we discuss these only briefly.     

Method for quantitative proteomics research by using metal element chelated tags coupled with mass spectrometry

The mass spectrometry-based methods with a stable isotope as the internal standard in quantitative proteomics have been developed quickly in recent years. But the use of some stable isotope reagents is limited by the relative high price and synthetic difficulties. We have developed a new method for quantitative proteomics research by using metal element chelated tags (MECT) coupled with mass spectrometry. The bicyclic anhydride diethylenetriamine-N,N,N',N' ',N' '-pentaacetic acid (DTPA) is covalently coupled to primary amines of peptides, and the ligand is then chelated to the rare earth metals Y and Tb. The tagged peptides are mixed and analyzed by LC-ESI-MS/MS. Peptides are quantified by measuring the relative signal intensities for the Y and Tb tag pairs in MS, which permits the quantitation of the original proteins generating the corresponding peptides. The protein is then identified by the corresponding peptide sequence from its MS/MS spectrum. The MECT method was evaluated by using standard proteins as model sample. The experimental results showed that metal chelate-tagged peptides chromatographically coeluted successfully during the reversed-phase LC analysis. The relative quantitation results were accurate for proteins using MECT. DTPA modification of the N-terminal of peptides promoted cleaner fragmentation (only y-series ions) in mass spectrometry and improved the confidence level of protein identification. The MECT strategy provides a simple, rapid, and economical alternative to current mass tagging technologies available.     

Global protein identification and quantification technology using two-dimensional liquid chromatography nanospray mass spectrometry

A simple and reliable method is described here for the identification and relative quantification of proteins in complex mixtures using two-dimensional liquid chromatography/tandem mass spectrometry. The method is based on the classical proteomic analysis where proteins are digested with trypsin and the resulting peptides are separated by multidimensional liquid chromatography. The separated peptides are analyzed by tandem mass spectrometry and identified via a database search algorithm such as SEQUEST. The peak areas (integrated ion counts over the peptide elution time) of all identified peptides are calculated, and the relative concentration of each protein is determined by comparing the peak areas of all peptides from that protein in one sample versus those from the other. Using this strategy, we compared the relative level of protein expression of A431 cells (an epidermal cell line) grown in the presence or absence of epidermal growth factor (EGF). Our results are consistent with the published observations of the transient effects of EGF. In addition, the difference in the concentrations of several phosphopeptides determined in our studies suggests the possibility of several new targets involved in the EGF cell-signaling pathway. This global protein identification and quantification technology should prove to be a valuable means for comparing proteomes in biological samples subjected to differential treatments.     

A statistical basis for testing the significance of mass spectrometric protein identification results

A method for testing the significance of mass spectrometric (MS) protein identification results is presented. MS proteolytic peptide mapping and genome database searching provide a rapid, sensitive, and potentially accurate means for identifying proteins. Database search algorithms detect the matching between proteolytic peptide masses from an MS peptide map and theoretical proteolytic peptide masses of the proteins in a genome database. The number of masses that matches is used to compute a score, S, for each protein, and the protein that yields the best score is assumed as the identification result. There is a risk of obtaining a false result, because masses determined by MS are not unique; i.e., each mass in a peptide map can match randomly one or several proteins in a genome database. A false result is obtained when the score, S, due to random matching cannot be discerned from the score due to matching with a real protein in the sample. We therefore introduce the frequency function, f(S), for false (random) identification results as a basis for testing at what significance level, alpha, one can reject a null hypothesis, H0: "the result is false". The significance is tested by comparing an experimental score, S(E), with a critical score, S(C), required for a significant result at the level alpha. If S(E) > or = S(C), H0 is rejected. f(S) and S(C) were obtained by simulations utilizing random tryptic peptide maps generated from a genome database. The critical score, S(C), was studied as a function of the number of masses in the peptide map, the mass accuracy, the degree of incomplete enzymatic cleavage, the protein mass range, and the size of the genome. With S(C) known for a variety of experimental constraints, significance testing can be fully automated and integrated with database searching software used for protein identification.     

GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model

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.     

Linking mass spectrometric imaging and traditional peptidomics: a validation in the obese mouse model

MALDI mass spectrometry imaging (MSI) is a promising technique in the field of molecular (immuno)histology but is confronted with the problematic large-scale identification of peptides from thin tissue sections. In this study we present a workflow that significantly increased the number of identified peptides in a given MALDI-MSI data set and we evaluated its power concerning relative peptide quantifications. Fourier transform mass spectrometry (FTMS) profiling on matrix-coated thin tissue sections allowed us to align spectra of different MS sources, matching identical peaks in the process, thus linking MSI data to tandem mass spectrometry (MS/MS) on one hand and semiquantitative liquid chromatography (LC)/MS data on the other. Bonanza clustering was applied in order to group MS/MS spectra of structurally related peptides, making it possible to infer the identity of MSI-detected compounds based on identified members within the same cluster, effectively increasing the number of identifications in a single MSI data set. Out of 136 detected peptides with MALDI-MSI, we were able to identify 46 peptides. For 31 of these, a LC/quadrupole time-of-flight (QTOF) counterpart was detected, and we observed similar obese (ob/ob) to wild-type (wt) peak intensity ratios for 18 peptides. This workflow significantly increased the number of identifications of peptide masses detected with MALDI-MSI and evaluated the power of this imaging method for relative quantification of peptide levels between experimental conditions.