Increasing information from shotgun proteomic data by accounting for misassigned precursor ion masses

Although mass spectrometers are capable of providing high mass accuracy data, assignment of true monoisotopic precursor ion mass is complicated during data-dependent ion selection for LC-MS/MS analysis of complex mixtures. The complication arises when chromatographic peak widths for a given analyte exceed the time required to acquire a precursor ion mass spectrum. The result is that many measured monoisotopic masses are misassigned due to calculation from a single mass spectrum with poor ion statistics based on only a fraction of the total available ions for a given analyte. Such data in turn produces errors in automated database searches, where precursor m/z value is one search parameter. We propose here a postacquisition approach to correct misassigned monoisotopic m/z values that involves peak detection over the entire elution profile and correction of the precursor ion monoisotopic mass. As a result of using this approach to reprocess shotgun proteomic data we increased peptide sequence assignments by 10% while reducing the estimated false positive ratio from 1 to 0.2%. We also show that 4% of the salvaged identifications may be accounted for by correction of mixed tandem mass spectra resulting from fragmentation of multiple peptides simultaneously, a situation which we refer to as accidental CID.     

Optimal construction of theoretical spectra for MS/MS spectra identification

We derive the optimal number of peaks (defined as the minimum number that provides the required efficiency of spectra identification) in the theoretical spectra as a function of (i) the experimental accuracy, sigma, of the measured ratio m/z; (ii) experimental spectrum density; (iii) size of the database; (iv) number of peaks in the theoretical spectra; and (v) types of ions that the peaks represent. We show that if theoretical spectra are constructed including b and y ions alone, then for sigma = 0.5, which is typical for high-throughput data, peptide chains of eight amino acids or longer can be identified based on the positions of peaks alone, at a rate of false identification below 1%. To discriminate between shorter peptides, additional (e.g., intensity-inferred) information is necessary. We derive the dependence of the probability of false identification on the number of peaks in the theoretical spectra and on the types of ions that the peaks represent. Our results suggest that the class of mass spectrum identification problems, for which more elaborate development of fragmentation rules (such as intensity model) is required, can be reduced to the problems that involve homologous peptides.     

Synthesis and characterization of hexadecadienyl compounds with a conjugated diene system,sex pheromone of the persimmon fruit moth and related compounds

Hexadecadien-1-ol and the derivatives (acetate and aldehyde) with a conjugated diene system have recently been identified from a pheromone gland extract of the persimmon fruit moth (Stathmopoda masinissa), a pest insect of persimmon fruits distributed in East Asia. The alcohol and acetate showed their base peaks at m/z 79 in a GC-MS analysis by electron impact ionization, but the aldehyde produced a unique base peak at m/z 84, suggesting a 4,6-diene structure. To confirm this inference, four geometrical isomers of each 4,6-hexadecadienyl compound were synthesized by two different routes in which one of two double bonds was furnished in a highly stereoselective manner. Separation of the two isomers synthesized together by each route was facilely accomplished by preparative HPLC. Their mass spectra coincided well with those of natural components, indicating that they were available for use as authentic standards for determining the configuration of the natural pheromone. Furthermore, other hexadecadienyl compounds, including the conjugated diene system between the 3- and 10-positions, were synthesized to accumulate the spectral data of pheromone candidates. 5,7-Hexadecadienal interestingly showed the base peak at m/z 80; meanwhile, the base peaks of its alcohol and acetate were detected at m/z 79 like the corresponding 4,6-dienes. The base peaks of all 6,8-, 7,9-, and 8,10-dienes universally appeared at m/z 67 like 9,11-, 10,12-, and 13,15-dienes, the spectra of which have already been published. Although 3,5-hexadecadienal was not prepared, base peaks at m/z 67 and 79 were recorded for the alcohol and acetate, respectively.     

De novo analysis of peptide tandem mass spectra by spectral graph partitioning.

We report on a new de novo peptide sequencing algorithm that uses spectral graph partitioning. In this approach, relationships between m/z peaks are represented by attractive and repulsive springs, and the vibrational modes of the spring system are used to infer information about the peaks (such as "likely b-ion" or "likely y-ion"). We demonstrate the effectiveness of this approach by comparison with other de novo sequencers on test sets of ion-trap and QTOF spectra, including spectra of mixtures of peptides. On all datasets, we outperform the other sequencers. Along with spectral graph theory techniques, the new de novo sequencer EigenMS incorporates another improvement of independent interest: robust statistical methods for recalibration of time-of-flight mass measurements. Robust recalibration greatly outperforms simple least-squares recalibration, achieving about three times the accuracy for one QTOF dataset.     

Annotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkers

Peak detection is a key step in the analysis of SELDI-TOF-MS spectra, but the current default method has low specificity and poor peak annotation. To improve data quality, scientists still have to validate the identified peaks visually, a tedious and time-consuming process, especially for large data sets. Hence, there is a genuine need for methods that minimize manual validation. We have previously reported a multi-spectral signal detection method, called RS for 'region of significance', with improved specificity. Here we extend it to include a peak quantification algorithm based on annotated regions of significance (ARS). For each spectral region flagged as significant by RS, we first identify a dominant spectrum for determining the number of peaks and the m/z region of these peaks. From each m/z region of peaks, a peak template is extracted from all spectra via the principal component analysis. Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model.We have evaluated the ARS algorithm on patient samples from a clinical study. Comparison with the standard method shows that ARS (i) inherits the superior specificity of RS, and (ii) gives more accurate peak annotations than the standard method. In conclusion, we find that ARS alleviates the main problems in the preprocessing of SELDI-TOF spectra. The R-package ProSpect that implements ARS is freely available for academic use at http://www.meb.ki.se/ yudpaw.     

Evaluation of matrix-assisted laser desorption/ionization-time of flight mass spectrometry proteomic profiling: identification of alpha 2-HS glycoprotein B-chain as a biomarker of diet

Biomarkers have the potential to impact a wide range of public health concerns, including early detection of diseases, drug discovery, and improved accuracy of monitoring effects of interventions. Given new technological developments, broad-based screening approaches will likely advance biomarker discovery at an accelerated pace. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) allows for the elucidation of individual protein masses from a complex mixture with high throughput. We have developed a method for identifying serum biomarkers using MALDI-TOF and statistical analysis. However, before applying this approach to screening of complex diseases, we evaluated the approach in a controlled dietary intervention study. In this study, MALDI-TOF spectra were generated using samples from a randomized controlled trial. During separate feeding periods, 38 participants ate a basal diet devoid of fruits and vegetables and a basal diet supplemented with cruciferous (broccoli) family vegetables. Serum samples were obtained at the end of each 7-day feeding period and treated to remove large, abundant proteins. MALDI-TOF spectra were analyzed using peak picking algorithms and logistic regression models. Our bioinformatics methods identified two significant peaks at m/z values of 2740 and 1847 that could classify participants based on diet (basal vs. cruciferous) with 76% accuracy. The 2740 m/z peak was identified as the B-chain of alpha 2-HS glycoprotein, a serum protein previously found to vary with diet and be involved in insulin resistance and immune function.     

A signature-based method to distinguish time-of-flight secondary-ion mass spectra from biological samples

Time-Of-Flight Mass Spectrometry (TOF-SIMS) was used to determine elemental and biomolecular ions from isolated protein samples. We identified a set of 23 mass-to-charge ratio (m/z) peaks that represent signatures for distinguishing biological samples. The 23 peaks were identified by Singular Value Decomposition (SVD) and Canonical Analysis (CA) to find the underlying structure in the complex mass-spectra data sets. From this modified data, SVD was used to identify sets of m/z peaks, and we used these patterns from the TOF-SIMS data to predict the biological source from which individual mass spectra were generated. The signatures were validated using an additional data set different from the initial training set used to identify the signatures. We present a simple method to identify multiple variables required for sample classification based on mass spectra that avoids overfit. This is important in a variety of studies using mass spectrometry, including the ability to identify proteins in complex mixtures and for the identification of new biomarkers.     

New approach for characterization of lysosulfatide by TLC, fast atom bombardment mass spectrometry and NMR spectroscopy

Thin layer chromatography of lysosulfatide showed anomalous Rf-values in contrast with such lysosphingolipids as glucopsychosine and galactopsychosine with neutral, acidic, and alkaline developing solvents. This was thought to be due to the presence of oppositely charged sulfate and amino groups in the lysosulfatide. In the negative mode of fast atom bombardment mass spectrometry, the lysosulfatide showed the pseudo molecular ion (M-H)- peak at m/z 540 and sulfate ion peak at m/z 97, whereas in the positive mode, it showed not only the pseudo molecular ion (M+H)+ peak at m/z 542, but also the major peaks of protonated psychosine at m/z 462 and fragment ions of dehydrated sphingosine at m/z 282 and 264, 13C-NMR signals of all carbons of lysosulfatide were determined by using distortionless enhancement by polarization transfer. The difference in chemical shifts of ring carbons of galactose residue between lysosulfatide and galactopsychosine was largest at C-3 (downfield shift), thereby indicating the location of the sulfate group to be at C-3 of galactose. This conclusion is supported by the 1H-NMR spectra of the lysosulfatide and galactopsychosine. Thus, the chemical structure of lysosulfatide was confirmed by fast atom bombardment mass spectrometry and 13C- and 1H-NMR spectroscopy. Furthermore, 13C-NMR signals of C-1 to C-5 of the sphingosine moiety showed significantly different chemical shifts between the lysosulfatide and galactopsychosine. These differences suggested that C-1 to C-5 of sphingosine might be influenced by intramolecular or intermolecular interaction between the sulfate group of the galactose residue and the amino group of sphingosine.(ABSTRACT TRUNCATED AT 250 WORDS)     

pParse: a method for accurate determination of monoisotopic peaks in high-resolution mass spectra

Determining the monoisotopic peak of a precursor is a first step in interpreting mass spectra, which is basic but non-trivial. The reason is that in the isolation window of a precursor, other peaks interfere with the determination of the monoisotopic peak, leading to wrong mass-to-charge ratio or charge state. Here we propose a method, named pParse, to export the most probable monoisotopic peaks for precursors, including co-eluted precursors. We use the relationship between the position of the highest peak and the mass of the first peak to detect candidate clusters. Then, we extract three features to sort the candidate clusters: (i) the sum of the intensity, (ii) the similarity of the experimental and the theoretical isotopic distribution, and (iii) the similarity of elution profiles. We showed that the recall of pParse, MaxQuant, and BioWorks was 98-98.8%, 0.5-17%, and 1.8-36.5% at the same precision, respectively. About 50% of tandem mass spectra are triggered by multiple precursors which are difficult to identify. Then we design a new scoring function to identify the co-eluted precursors. About 26% of all identified peptides were exclusively from co-eluted peptides. Therefore, accurately determining monoisotopic peaks, including co-eluted precursors, can greatly increase peptide identification rate.     

Improving mass spectrometry peak detection using multiple peak alignment results

Mass spectrometry data are often corrupted by noise. It is very difficult to simultaneously detect low-abundance peaks and reduce false-positive peak detection caused by noise. In this paper, we propose to improve peak detection using an additional constraint: the consistent appearance of similar true peaks across multiple spectra. We observe that false -positive peaks in general do not repeat themselves well across multiple spectra. When we align all the identified peaks (including false-positive ones) from multiple spectra together, those false-positive peaks are not as consistent as true peaks. Thus, we propose to use information from other spectra in order to reduce false-positive peaks. The new method improves the detection of peaks over the traditional single spectrum based peak detection methods. Consequently, the discovery of cancer biomarkers also benefits from this improvement. Source code and additional data are available at: http://www.ece.ust.hk/ approximately eeyu/mspeak.htm.     

Characterization of fatty amides produced by lipase-catalyzed amidation of multi-hydroxylated fatty acids

Novel multi-hydroxylated primary fatty amides produced by direct amidation of 7,10-dihydroxy-8(E)-octadecenoic acid and 7,10,12-trihydroxy-8(E)-octadecenoic acid were characterized by GC-MS and NMR. The amidation reactions were catalyzed by immobilized Pseudozyma (Candida) antarctica lipase B (Novozym 435) in organic solvent with ammonium carbamate. The mass spectra of the underivatized products exhibited characteristic primary amide peaks at m/z 59 and m/z 72 that differed in peak intensities. Other peaks present were consistent with cleavage next to the hydroxyl groups. The mass spectra of the silylated amidation products showed the correct molecular weight and the typical fragmentation pattern of silylated hydroxy compounds. The mass spectra, together with proton and 13C NMR data, suggest that the products of lipase-catalyzed direct amidation of 7,10-dihydroxy-8(E)-octadecenoic acid and 7,10,12-trihydroxy-8(E)-octadecenoic acid are, 7,10-dihydroxy-8(E)-octadecenamide and 7,10,12-trihydroxy-8(E)-octadecenamide acid, respectively. Amidation of multi-hydroxylated fatty acids had increased the melting point, but reduced the surface active property of the resulting primary amides.     

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra (“good connections”), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra (“bad connections”), and minimizing the number of assigned peaks that have no matching peaks in the other spectra (“edges”). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a larger number of residues. On the other hand, when there are multiple equally good assignments that are significantly different from each other, the modified NSGA-II is less efficient than MC/SA in finding all the solutions. This problem is solved by a combined NSGA-II/MC algorithm, which appears to have the advantages of both NSGA-II and MC/SA. This combination algorithm is robust for the three most difficult chemical shift datasets examined here and is expected to give the highest-quality de novo assignment of challenging protein NMR spectra.     

Failure to verify the presence of pyrroloquinoline quinone (PQQ) in bovine plasma amine oxidase by gas chromatography/mass spectrometry

The presence of covalently bound pyrroloquinoline quinone (PQQ) in bovine plasma amine oxidase (BPAO) was examined by the use of gas chromatography/mass spectrometry. The enzyme was subjected to proteolysis with proteinase in the presence of [U-13C]PQQ as an internal standard. After isolation and derivatization of PQQ with phenyltrimethylammonium hydroxide, molecular peaks at m/z 448 and 462 were used for detection of PQQ and [U-13C]PQQ, respectively, by selected ion monitoring (SIM). In the SIM profile, although the sample extract obtained from BPAO treated with proteinase clearly showed the peak at m/z 462 for the internal standard, there were no peaks detectable at m/z 448, showing the absence of PQQ in the proteolysis digest of BPAO. Thus, our results do not support the claim that BPAO contains covalently bound PQQ in its structure.     

Difference and consensus of whole cell Salmonella enterica subsp. enterica serovars matrix-assisted laser desorption/ionization time-of-flight mass spectrometry spectra

AIM: Application of MALDI-TOF MS for characterization of strains of Salmonella enterica subsp. enterica. METHODS AND RESULTS: Whole cells were analysed by MALDI-TOF MS. Spectra with a maximum of 500 mass peaks between (m/z) 0 and 25000 were examined for consensus peaks manually and by a computer software algorithm. Consensus peaks were observed by both methods for spectra of Salmonella enterica serovars Derby, Hadar, Virchow, Anatum, Typhimurium and Enteritidis. CONCLUSIONS: Differences in numbers of consensus peaks in spectra obtained by manual and computer comparison indicated that development of the software involving statistical analysis of peak accuracy is necessary. SIGNIFICANCE AND IMPACT OF THE STUDY: Development of an analysis system for peak profiles in whole cell MALDI-TOF MS spectra to enable intra and interlaboratory comparison.     

Simultaneous determination of urinary androgen glucuronides by high temperature gas chromatography-mass spectrometry with selected ion monitoring

An efficient procedure is described for the simultaneous determination of 9 androgen glucuronides including androsterone, etiocholanolone, 11-ketoandrosterone, 11-ketoetiocholanolone, 11beta-hydroxyandrosterone, 11beta-hydroxyetiocholanolone, and dehydroepiandrosterone (DHEA) in 3-glucuronide form and dihydrotestosterone (DHT) and testosterone in 17-glucuronide form from urine specimens. The method involves solid-phase extraction of the urinary steroids using Serdolit PAD-1 resin, with subsequent conversion to methyl ester-trimethylsilyl (Me-TMS) ether derivatives for the direct analysis by gas chromatography-mass spectrometry (GC-MS) using high temperature MXT-1 (Silcosteel-treated stainless steel) capillary column. Upon split injection of Me-TMS steroids at 330 degrees C into the MXT-1 capillary column initially maintained at 300 degrees C then programmed to 322 degrees C at 2 degrees C/min, each androgen glucuronide was well separated in excellent peak shape. The characteristic ions at m/z 217 constituting the base peaks in the electron-impact (20 eV) mass spectra for most steroids permitted their sensitive detection by GC-MS with selected-ion monitoring (SIM), whereas base peak ion at m/z 271 was used for the SIM of dehydroepiandrosterone-3-glucuronide. The detection limits for SIM of most of the steroids were 15 pg except for the 3-glucuronides of 11-ketoandrosterone and 11-ketoetiocholanolone, which could be detected down to 20 pg. The SIM responses were linear with correlation coefficients varying from 0.981 to 0.993 in the concentration range of 20 to 3000 ng/ml for the androgens studied. When applied to urine samples, the present method allowed rapid screening for the 7 androgens in their glucuro-conjugated forms simultaneously with good overall precision and accuracy within the normal concentration ranges of 15.1 to 3124.6 ng/ml.     

Analysis of Human Proteome Organization Plasma Proteome Project (HUPO PPP) reference specimens using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectrometry: multi-institution correlation of spectra and identification of biomarkers

We report on a multicenter analysis of HUPO reference specimens using SELDI-TOF MS. Eight sites submitted data obtained from serum and plasma reference specimen analysis. Spectra from five sites passed preliminary quality assurance tests and were subjected to further analysis. Intralaboratory CVs varied from 15 to 43%. A correlation coefficient matrix generated using data from these five sites demonstrated high level of correlation, with values >0.7 on 37 of 42 spectra. More than 50 peaks were differentially present among the various sample types, as observed on three chip surfaces. Additionally, peaks at approximately 9200 and approximately 15,950 m/z were present only in select reference specimens. Chromatographic fractionation using anion-exchange, membrane cutoff, and reverse phase chromatography, was employed for protein purification of the approximately 9200 m/z peak. It was identified as the haptoglobin alpha subunit after peptide mass fingerprinting and high-resolution MS/MS analysis. The differential expression of this protein was confirmed by Western blot analysis. These pilot studies demonstrate the potential of the SELDI platform for reproducible and consistent analysis of serum/plasma across multiple sites and also for targeted biomarker discovery and protein identification. This approach could be exploited for population-based studies in all phases of the HUPO PPP.     

Gas chromatography/mass spectrometry of bacterial amines

Bacterial amines were examined by gas chromatography/mass spectrometry. Under electron impact all trifluoroacetamides exhibited peaks at m/z 69 due to [CF3]+. Many trifluoroacetamides also showed peaks at m/z 97 corresponding to the [COCF3]+ ion fragment. The spectra of n-alkyl and aralkyl trifluoroacetamides were consistent with the spectra and their interpretations in the earlier literature. Molecular ions were of low abundance for all alkyl trifluoroacetamides having alkyl chains longer than two carbon atoms. Chemical ionization gave molecular weight information in all cases. Most peaks observed were molecular addition products, e.g. [M + H]+ and [M + NH4]+. Application of chemical ionization mass spectrometry to analysis of bacterial amines revealed the production of beta-phenylethylamine, n-decylamine, 1,4-diaminobutane and 1,5-diaminopentane by Clostridium histolyticum; whereas both Clostridium bifermentans and Clostridium oedematiens produced beta-phenylethylamine. The latter organism also produced a peak with a retention time similar to that of an authentic amylamine derivative.     

Post alignment clustering procedure for comparative quantitative proteomics LC-MS data

Comparative LC-MS is a powerful method for detailed quantitative comparison of complex protein mixtures. Dedicated software is required for detection, matching, and alignment of peaks in multiple LC-MS datasets. However, retention time shifts, saturation effects, limitations of experimental accuracy, and possible occurrence of split peaks make it difficult for software to perfectly match all chromatograms. We describe a procedure to assess the above problems and show that dataset quality can be enhanced with the aid of cluster analysis.     

A robust biomarker discovery pipeline for high-performance mass spectrometry data

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.     

Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer

A classification and regression tree (CART) model was trained to classify 41 clinical specimens as disease/nondisease based on 26 variables computed from the mass-to-charge ratio (m/z) and peak heights of proteins identified by mass spectroscopy. The CART model built on all of the specimens (no cross-validation) had an error rate of 4/41 = 10%. The CART model suggests that mass spectra peaks in the 8000-10,000, 20,000-30,000, 45,000-60, 000, and >125,000 m/z ranges may be valuable in distinguishing between the disease/nondisease specimens. The area under the receiver operating characteristics curve was 0.80 +/- 0.07 for leave-one-out cross-validation.