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Molecular & Cellular Proteomics 7:71-87, 2008.
© 2008 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Investigating MS²/MS³ Matching Statistics

A Model For Coupling Consecutive Stage Mass Spectrometry Data For Increased Peptide Identification Confidence^*,S

Peter J. Ulintz^,, Bernd Bodenmiller^¶,||, Philip C. Andrews, Ruedi Aebersold^¶,**, and Alexey I. Nesvizhskii^,,¶¶

From the Departments of Biological Chemistry and Pathology and Bioinformatics Program, University of Michigan, Ann Arbor, Michigan, 48103, ^¶ Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, 8093 Zurich, Switzerland, ^** Institute for Systems Biology, Seattle, Washington 98103, and Faculty of Science, University of Zurich, 8057 Zurich, Switzerland

	ABSTRACT

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS AND DISCUSSION REFERENCES

 
Improvements in ion trap instrumentation have made n-dimensional mass spectrometry more practical. The overall goal of the study was to describe a model for making use of MS² and MS³ information in mass spectrometry experiments. We present a statistical model for adjusting peptide identification probabilities based on the combined information obtained by coupling peptide assignments of consecutive MS² and MS³ spectra. Using two data sets, a mixture of known proteins and a complex phosphopeptide-enriched sample, we demonstrate an increase in discriminating power of the adjusted probabilities compared with models using MS² or MS³ data only. This work also addresses the overall value of generating MS³ data as compared with an MS²-only approach with a focus on the analysis of phosphopeptide data.

Newer instrumentation supports alternative techniques for data generation that have the potential to improve peptide and protein identification. One such technique is three-stage mass spectrometry (MS³) in which peptide ions in an ion trap or ICR mass spectrometer are subjected to an additional stage of isolation and fragmentation. The faster acquisition times of newer linear trap instruments such as the LTQ provide the option of collecting MS³ spectra of abundant MS² peaks with overall cycle times similar to those of normal MS/MS² cycles on older three-dimensional trap instruments. As a result, a number of researchers are choosing to routinely collect MS³ spectra during LC-MS/MS runs that have the potential to provide additional information useful for peptide identification and characterization. This is deemed particularly important in the case of proteins identified by single peptides (10, 11) and for the analysis of phosphopeptides, the spectra of which are frequently dominated by a major fragment ion representing neutral loss of the phosphate group from the precursor peptide. Therefore, phosphopeptides have been analyzed by automated data-dependent triggering of MS³ acquisition whenever the dominant neutral loss ion of the appropriate mass is detected in an MS² spectrum (12–14). Fragmentation of the neutral loss ion typically provides significantly increased structural information via increased peptide bond cleavage. Similar approaches may be applied to other major neutral loss ions (e.g. loss of 64 Da from peptides containing methionine sulfoxide) and to excessive prolyl- or aspartyl-directed fragmentation. MS³ spectra have proven to be useful in top-down analysis as well both for protein identification and for characterization of specific sites of post-translational modification (15, 16).

Generally speaking, there are several ways of combining MS² and MS³ spectra from the same peptide to improve peptide identification. One strategy involves integrating matching MS² and MS³ spectra directly at the spectrum level, generating an "intersection spectrum" that contains only one type of ion, thus allowing simplified de novo sequencing of the peptide. This approach has been described by Zhang and McElvain (17), who demonstrated the usefulness of the technique in protein sequencing. Olsen and Mann (11) describe a custom scoring algorithm for MS³ spectra: their final score for a peptide is the product of the Mascot-generated MS² and the custom MS³ scores. In glycoproteomics, it is frequently the case that MS² and MS³ provide complementary structural information on a glycopeptide: information on the structure of side-chain carbohydrate moieties is generally obtained from the MS² spectrum, whereas amino acid sequence information is more readily obtained in the MS³ (18). In the top-down technique described by Zabrouskov et al. (16), sequence tags are extracted from MS³ spectra using a de novo algorithm and used to complement correlated MS² spectral data in a "hybrid" database search strategy implemented in the ProSight PTM search engine (19).

Related to the problem of MS²/MS³ spectrum integration, de novo sequencing-based algorithms have been described for combining pairs of spectra corresponding to unmodified and modified versions of the same peptide or pairs of spectra corresponding to the same peptide tagged with a light or heavy version of a labeling reagent (9, 20, 21, 23). However, although de novo sequencing approaches are promising, no computational tools are currently available that can be robustly applied in a high throughput environment. As a result, analysis of MS² and MS³ data is still largely carried out with a conventional database search approach using commercially available programs such as SEQUEST, Mascot, SpectrumMill, Phenyx, Paragon, or open source programs X! Tandem, Open Mass Spectrometry Search Algorithm (OMSSA), InsPecT, or ProbID (24–29).

Although all existing database search tools can be used to identify peptides from both MS² and MS³ spectra, automated analysis of those different types of spectra may not be identical. This often leads to the requirement that MS² and MS³ spectra be separated for processing. The main reason for this is that the measured precursor mass associated with MS³ spectra will not always correspond to the mass of an appropriate database peptide calculated using the same conventional rules that are applied in the case of MS² spectra. For example, in phosphopeptide analyses variable modifications of –18 Da due to loss of phosphoric acid from Ser or Thr residues need to be specified for MS³, whereas the normal +80-Da phosphorylation modification on Ser, Thr, and Tyr are used for MS². It is computationally inefficient, and an unnecessary source of false positive identifications, to perform a combined search that permits both the –18-Da loss for MS² spectra and the +80-Da addition for MS³ spectra.

Searching MS³ spectra separately from their parent MS² spectra essentially decouples the two sets of scans. Intuitively if analysis of successive MS² and MS³ scans results in matching peptide sequences, there is an increased confidence in both identifications. The work described here attempts to provide a general, statistically sound assessment of the confidence achieved by combining the search results of MS² and MS³ spectra from the same peptide. In contrast to aforementioned work, we assume a work flow in which the MS² and MS³ spectra are searched independently using a common search engine (namely SEQUEST in this work) and are independently statistically validated using PeptideProphet. We then recouple matching consecutive MS² and MS³ scans and adjust the peptide probabilities initially computed by PeptideProphet to account for the new "linked" MS²/MS³ information. We describe a model that produces an adjusted probability of peptide identification and demonstrate, using a data set of MS² and MS³ spectra generated using a control protein mixture, that such a correction can be used to better discriminate between correct and incorrect database search results. We also investigate ways to combine the adjusted MS² and MS³ probabilities to compute a single confidence measure for their corresponding unique peptide. We then further demonstrate the utility of our method using a phosphopeptide-enriched data set generated from Drosophila melanogaster samples on an LTQ linear ion trap instrument. Finally we compare runs in which both MS² and MS³ spectra are generated with an MS²-only method to address the overall benefit of generating MS³ data.

	EXPERIMENTAL PROCEDURES

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS AND DISCUSSION REFERENCES

 
Sample Preparation and Mass Spectrometry
Two experimental data sets of MS/MS spectra were used in this work to evaluate the statistical model and to investigate its utility in the analysis of phosphopeptide-enriched samples. All spectra were acquired using an ESI linear ion trap tandem mass spectrometer (Thermo Electron’s LTQ).

Nine-protein Mixture ("9-Mix") Sample
A mixture of nine commercially available protein standards (P68082, myoglobin of Equus caballus (horse); P00698, lysozyme C precursor of Gallus gallus (chicken); Q29443, serotransferrin precursor (transferrin) of Bos taurus; P18915, carbonic anhydrase 6 precursor of B. taurus (bovine); P12763, ₂-HS-glycoprotein precursor (fetuin-A) of B. taurus (bovine); P02754, β-lactoglobulin precursor of B. taurus (bovine); P62894, cytochrome c of B. taurus (bovine); P02666, β-casein precursor of B. taurus (bovine); P02769, serum albumin precursor (BSA) of B. taurus (bovine)) was digested using trypsin, and the resulting peptide mixtures were purified using reverse phase chromatography prior to mass spectrometric analysis. For the analysis of the peptides using mass spectrometry see "Mass Spectrometry." The final data set consisted of three LC-MS/MS runs with 58,081 MS/MS spectra in total.

Phosphopeptide Sample
This sample is a trypsin-digested, IMAC-enriched D. melanogaster whole cell lysate. The preparation of the phosphopeptide samples is described in detail in Bodenmiller et al. (30). Several mass spectrometry analyses of this sample were conducted both for analysis of performance of the probability model and to test the value of generating MS³ data.

Mass Spectrometry
An LTQ quadrupole linear ion trap mass spectrometer (ThermoElectron, San Jose, CA) was used with an HP 1100 solvent delivery system (Agilent, Palo Alto, CA) for the analysis of the D. melanogaster Kc167 cell cytosolic phosphoproteome. Peptides were loaded on a capillary (BGB Analytik, Böckten, Switzerland) reverse phase C₁₈ column (75-µm inner diameter and 11 cm of bed length with Magic C₁₈ AQ 5-µm 200-Å resin (Michrom BioResources, Auburn, CA)) and then eluted from the capillary column at a flow rate of 200–300 nl/min to the mass spectrometer through an integrated electrospray emitter tip. Peptides were eluted for each analysis from 12 to 33% acetonitrile in which the ions were detected, isolated, and fragmented in a completely automated fashion. The exact settings for MSⁿ acquisition were as follows.

Nine-protein Mixture—
In the first scan event, all peptides eluting from the column were recorded in MS mode. The most intense ion was selected for product ion spectrum (MS²) in the second event. An MS³ spectrum of the most intense peak in the MS² spectrum was automatically selected in the third scan event. The second and third events are then repeated two more times in the cycle, for the second and third most abundant MS¹ ions, for a total cycle of seven events. A threshold of 5000 ion counts was used for triggering an MS² attempt. Wide band activation was enabled for all MS² and MS³ scan events. MS² isolation width was set to 2.0 m/z, and MS³ isolation width was set to 4 m/z. For triggering an MS³ event the most intense ion had to be above 50 ion counts. No further restrictions were made for the selection of the MS³ precursor.

Phosphopeptide Sample—
All peptides eluting from the column were recorded in MS mode in the first scan event. The most intense ion was selected for product ion spectrum (MS²) in the second event. An MS³ spectrum of the most intense peak in the MS² spectrum, which for the phosphopeptide containing sample is in most cases the neutral loss peak (of 98 Da) from a serine/threonine phosphopeptide, was automatically selected in the third scan event. These three events form one complete cycle. A threshold of 20,000 ion counts was used for triggering an MS² attempt. Wide band activation was enabled for all MS² and MS³ scan events. MS² isolation width was set to 2 m/z, and MS³ isolation width was set to 3 m/z. For triggering an MS³ event the most intense ion had to be above 500 ion counts. No further restrictions were made for the selection of the MS³ precursor.

Phosphopeptide Sample: Additional Data Sets for Comparison of MS²-only with MS²/MS³ Methods—
For the MS²/MS³ data set the data-dependent MSⁿ spectra were acquired as follows. In the first scan event, all peptides eluting from the column were recorded in MS mode, and then the most intense ion was selected for product ion spectrum (MS²) in the second event. In the third event an MS³ spectrum was triggered specifically in the event of a phosphate neutral loss (–98 Da for singly, –49 Da for doubly, and –32.66 Da for triply charged peptides) in the MS² event. The second and third events are then repeated two more times in the cycle, for the second and third most abundant MS¹ ions, for a total cycle of seven events. For the MS²-only data set the data-dependent MSⁿ spectra were acquired as follows. In the first scan event, all peptides eluting from the column were recorded in MS mode, and then the three most intense ions were consecutively selected for product ion spectrum (MS²) for a total cycle of four events. Further settings for these samples were as follows: wideband activation was enabled for all MS² and MS³ scan events, MS² isolation width was set to 2 m/z, and MS³ isolation width was set to 4 m/z. For triggering an MS³ event in the MS²/MS³ data set the most intense ion had to be above 50 ion counts. No further restrictions were made for the selection of the MS³ precursor.

Database Searching and Analysis of Results
mzXML files were generated from ThermoFinnigan *.raw files using the ReAdW tool available in the Trans-Proteomic Pipeline (TPP) platform (31–33). MS² and MS³ peak list files in *.dta format were extracted separately from the mzXML files using mzXML2Other tool with the -level option.² For the 9-Mix data set, a custom fasta sequence file was constructed consisting of sequences corresponding to the proteins in the mixture and common contaminants appended to a reversed version of the International Protein Index human data set. Resulting *.dta files for the 9-Mix data set were searched with SEQUEST using the following parameters: peptide tolerance of 3.0 Da; b- and y-ion series; partial trypsin digestion, allowing for one missed cleavage site; a fixed modification of 57.02 Da was specified for cysteine; and a variable post-translational modification (PTM) of 16.0 Da was specified for methionine. MS³ data sets were searched using identical parameters. Note that partial trypsin specificity is required for searching MS³ spectra corresponding to the fragmentation of a selected y- or b-ion from the MS² spectrum. If sufficient computational resources are available, searching MS² spectra allowing for partially tryptic peptides can often be beneficial and result in additional identifications. However, doing so requires that the results are properly analyzed with a tool that accommodates tryptic termini information in the statistical model, such as PeptideProphet. In addition, a subset of MS³ spectra from this data set was also searched allowing for the C-terminal variable modification of –18.0 Da to accommodate the possibility that the MS³ precursor is a b-ion (11). The results indicated that including this modification does not significantly alter the overall performance; in fact, accommodating the variable modification decreases the number of identifications slightly (due to loss of a number of true peptide assignments because of increases in search space). Based on this, the C-terminal modification was not used in the final analysis of data presented in this study. The resulting data set contained 76,873 peptide assignments, counting 2+/3+ duplicates: 48,921 MS² (554 singly charged, 24,233 doubly charged, and 24,134 triply charged) and 27,952 MS³ (4582, 11,700, and 11,670 singly, doubly, and triply charged, respectively). Note that because of the charge state ambiguity (in the case of low mass accuracy data such as the data sets used in this work, the charge state of a multiple charged peptide ion cannot be reliably determined), most of the multiply charged spectra were searched twice, assuming 2+ or 3+ charge state. Furthermore due to a relatively small number of singly charged MS² spectra, all such spectra were left out of the subsequent analysis.

The database for the phosphopeptide-enriched samples consisted of all D. melanogaster sequences exported from the UniProt database (34), 26,311 entries total, to which the reversed set of sequences was appended. Parameters for the MS² search were as follows: peptide tolerance of 3.0 Da; partial trypsin digestion with one possible missed cleavage; fixed modification of 57.02 Da for cysteine; variable modifications of 80 Da were specified for Ser, Thr, and Tyr; and a maximum four PTMs per peptide. The MS³ spectra were searched with the same set of parameters except that variable modifications of –18 Da on Ser and Thr (instead of +80 Da) were specified to accommodate loss of phosphoric acid leading to a dehydroalanine or dehydrobutyric acid, respectively. SEQUEST database searching for the primary phosphopeptide data set (excluding the MS²/MS³ to MS²-only comparisons) resulted in 28,865 peptide assignments, counting 2+/3+ duplicates: 16,647 MS² (143 singly charged, 8483 doubly charged, and 8021 triply charged) and 12,218 MS³ (547, 5895, and 5776 singly, doubly, and triply charged, respectively).

The additional phosphopeptide-enriched data sets used for comparison of MS²/MS³ and MS²-only methodologies consisted of the following number of peptide assignments following SEQUEST database searching: Run 1 (A07_5205): 4915 MS² assignments (95 singly charged and 2410 each of doubly and triply charged) and 1897 MS³ assignments (31 singly charged and 933 each of doubly and triply charged); Run2 (A07_5206): 6450 MS² assignments (126 singly charged and 3162 doubly and triply charged); Run 3 (A07_5207): 4883 MS² assignments (103 singly charged and 2390 each of doubly and charged) and 1879 MS³ assignments (43 singly charged and 918 doubly and triply charged); and Run 4 (A07_5208): 6403 MS² assignments (159 singly charged and 3122 doubly and triply charged).

Processing of MS² and MS³ Search Results
Search results for each LC-MS/MS run were generated by first producing an html result file using the out2summary tool, exporting one result file for each MS level, for each run: a total of six files for the 9-Mix data set and two files for the phospho data set. html results were then converted into pepXML format (31) using Sequest2XML. PeptideProphet (32) was run on each result set, generating probability scores for each search result that are added to the pepXML documents. For the phospho data sets, PeptideProphet was run with the "–l" option, which results in alternate processing of Cn scores marked with "*," results for which the top and second highest ranked peptide assignment to a spectrum have homologous sequences (>70% sequence identity). With this option on, PeptideProphet will use the Xcorr score of the first non-homologous lower scoring peptide match when computing Cn score of the best scoring peptide. This option is beneficial in the event that the search returns several identical results that differ only by modification site for a sequence as often occurs in phosphorylated peptide identifications.³ Resulting files were parsed and processed to generate all matching statistics using a custom set of scripts implemented in Python. Certain subsets of data were also exported into a local Mysql database to facilitate generation of specific statistics.

Linking MS² and MS³ Scans and Search Results
The spectra in these experiments were generated in an interlaced manner, i.e. the scan cycle on the instrument followed the format MS¹ MS² MS³ MS² MS³ MS² MS³ or MS¹ MS² MS³ with the MS² scans triggered in a data-dependent manner from the MS¹ and the MS³ scans triggered from the preceding MS². As a result, a set of linked MS²/MS³ scans were generated based on consecutive scan numbers. In the resulting data set, MS² scans with no consecutive MS³ were retained and designated as linked but as a link to a null MS³ identification. MS³ scans without preceding MS² scans should not occur physically but do in these data for several reasons: namely the corresponding MS² peak lists that produced no database search result are typically not reported. Also some spectra containing only a few peaks may be filtered out by the data conversion software. The small number of instances in which these "orphaned" MS³ scans are generated invariably result in incorrect peptide identifications and are eliminated from subsequent analysis.

Due to uncertainty with the charge state each multiply charged scan was searched twice (in both 2+ and 3+ charge state), resulting in multiple search results for each scan. Consideration needs to be given to potential links between MS² and MS³ search results for any pair of scan numbers. A +1 MS² search result may only be linked to an MS³ search result that is +1, and a +2 MS² scan may produce a link to a search result with either a +1 or +2 charge state. The double and triple charged SEQUEST search duplication, however, creates a situation in which a +3 MS² search result may produce two possible links to +2 and +3 MS³ search results for any pair of scan numbers. After generating all possible links, one pair of search results among all possible pairs for any two scan numbers (designated as the "unique pair") is selected based on whether the sequences of the two peptide identifications composing a pair are matching. Matching is defined here as whether or not the sequences are equal or whether one contains a subsequence of the other. For non-matching pairs and scan sets with more than one pair with matching sequences, the match pair with the highest summed PeptideProphet probability is designated as the unique pair. A schematic of all matching possibilities and selection of a unique pair is shown in supplemental Fig. 1.

	RESULTS AND DISCUSSION

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS AND DISCUSSION REFERENCES

 
Overview of the Probability Adjustment Method—
The overall methodology for our approach is outlined in Fig. 1. Data generated by the mass spectrometer are processed via the TPP following normal procedures and using SEQUEST, Mascot, or X! Tandem database search tools for peptide identification (the tools currently supported by TPP) up through generation of peptide probabilities from PeptideProphet (32). Analyses in this early stage of processing are conducted separately for MS² and MS³ data. To calculate an adjusted probability for all assignments, successive scans must be linked as described under "Experimental Procedures." The multiple potential matches resulting from the charge state ambiguity are reduced in the processing, and only the most probable matching pair for any two scan numbers is retained.

View larger version (49K): [in this window] [in a new window]   FIG. 1. Overview of methodology. MS2 and MS3 spectra are extracted from the raw data, and the spectra are assigned peptides using sequence database searching (SEQUEST or similar programs). The resulting peptide assignments are statistically validated using PeptideProphet, which calculates for each assignment in the data set a probability of being correct (applied separately for MS2 and MS3 data). MS2 and MS3 scan results are correlated based on scan number in which an MS3 spectrum is linked to an MS2 if its scan number is consecutive. Based on the overall matched data set, a Bayesian probability correction is applied to linked scan results individually for MS2 and MS3 spectra, resulting in adjusted probability scores. In the final step, the MS2 and MS3 scan results are combined, and a final probability is calculated for each scan number as representative of the peptide identification.

Linking MS² and MS³ Data: a Case Study of the 9-Mix Data Set—
This analysis is carried out using a mixture of purified proteins (nine-protein mixture data set) in which it is possible to confidently label peptide identifications as "correct" or "incorrect." Because this data set was searched against a database consisting of the sequences of the mixture proteins appended with a much larger reversed human protein sequence database, each spectrum could be assigned a correctness label based on whether the top SEQUEST hit for the spectrum was to one of the known protein entries. The method used was simply to label as incorrect any assignment of a peptide from a known incorrect database entry (reversed human protein sequence entries in this case), whereas all assignments of peptides to one of the sample proteins can be considered correct (32).

The procedure begins by linking consecutive MS² and MS³ scans using their scan numbers. Overall there were 48,921 MS² spectra and 27,952 MS³ spectra generated for the 9-Mix data set. Due to the uncertainty in the precursor charge state for LTQ spectra, many spectra are redundant; for any pair of consecutive MS²/MS³ scan numbers, there may be one or two SEQUEST search results generated for each MS level as described under "Experimental Procedures." Consequently an MS² search result may be linked to more than one MS³ search result. For the 9-Mix data set, there are 16,140 unique linked pairs in which the MS³ is not null. Among these, 89 have MS²/MS³ charge states of +1/+1, eight of which match correct protein sequences in the database (either one or both of the sequences match). For doubly charged MS² pairs, 3761 are +2/+2 and 4043 are +2/+1 of which 878 and 2020 are correct, respectively. For triply charged MS², for +3/+3 there are 4020 pairs of which 631 are correct, for +3/+2 there are 3777 pairs of which 1177 are correct, and for +3/+1 there are 450 pairs of which 111 are correct. In all, linked pairs in which the MS³ has one less charge than the MS² are more likely to be correct. However, linked pairs for which the MS³ is the same charge state as MS² account for 36% of the correct identifications.

Neutral loss of amino acids from the N and C termini is a common phenomenon and has been described previously (35, 36). Selecting linked pairs in which both MS² and MS³ sequences are labeled correct and of the same charge state (+1/+1, +2/+2, and +3/+3) allows us to identify examples of amino acid neutral loss. Our data confirms the conventional rules for amino acid neutral loss described in the literature. Virtually all examples correspond to N-terminal loss of 1–4 amino acid residues, most frequently N-terminal to a proline. 276 of 323 of the occurrences are doubly charged, three are singly charged, and the remaining 44 are triply charged. Most examples occur multiple times: in all there are one, 34, and nine unique neutral loss sequence examples for the singly, doubly, and triply charged cases, respectively. These examples are provided in supplemental Table 1.

After linking consecutive scans and selecting a unique linked pair, the peptide assignments are binned into sequence match categories dependent on whether a consecutive scan exists and, if so, whether the top scoring SEQUEST sequence result of the successive scans matches (Table I). Sequence match categories (referred to as match categories or simply "Match" later in the text) are defined as follows: 0, no consecutive scan; 1, consecutive scans, but MS² and MS³ sequences do not match; 2, consecutive scans, and MS³ sequence is a subset of the MS² sequence; 3, consecutive scans, and MS³ sequence is identical to the MS² sequence; and 4, consecutive scans, and MS² sequence is a subset of MS³ sequence. In the data set of unique pairs, 69% of all MS² spectra produced consecutive MS³ spectra (16,140). Of those consecutive pairs, 1458 (9%) had matching sequences in which the MS³ sequence was a subset of the MS² sequence. 116 MS³ spectra were orphaned because they did not have a preceding MS² scan and were discounted. We note that there were no instances of identical sequence matches between MS² and MS³ top scoring hits in the 9-Mix data set as may occur for neutral ion events in which only a side-chain moiety is lost from the otherwise intact peptide backbone (e.g. a phosphate). These losses are observed in other similar data sets, however, and do occur in the phospho-enriched data sets described later.

Using the labeling of the data, the accuracies and sensitivities of the probability calculations could be determined. Toward this end, each linked pair of spectra can also be assigned a truth category based on the correctness of the peptide assignments to the MS² and MS³ scans. The truth category is a label indicating whether neither, both, or one of the matching scans has a correct label. The total numbers of scans in each truth category are shown in Table II. The number of unique pairs of search results in which both sequences were correctly assigned is 1509, corresponding to 6.4% of the total number of unique pairs of scans. A greater number of linked pairs (3316 total, 14.2%) have either the MS² only assigned correctly (2029) or only the MS³ assigned correctly (1287).

Probability Adjustment Calculation—
In automated analysis of mass spectrometry data, one of the most important tasks is the calculation of accurate and discriminative confidence measures for each peptide assignment to a spectrum produced by a database search tool. Toward that end, we seek to calculate a correction to the probability score that accommodates the increase in confidence resulting from matching MS² and MS³ spectra. The fact that matched consecutive MS² and MS³ spectra are more likely to be correct forms the basis for adjusting the probabilities of these spectra.

Calculation of probabilities for each peptide assignment in the data set, performed independently for MS² and MS³ data, represents the starting point in this analysis. PeptideProphet computes a probability for a peptide, designated here as p(+|D) by using the mixture model expectation maximization algorithm to model the distributions of various discriminant spectrum-level parameters, collectively represented here as D. The spectrum-level information D typically includes the discriminant database search score (a linear combination of the renormalized search scores reported by the database search tool used), the number of termini consistent with the specificity of the enzyme used to digest proteins, the number of missed internal cleavage sites, and the difference between the measured and the calculated precursor ion mass. In certain cases, additional parameters are included in the model such as the peptide pI value (37) or the presence of certain residues or sequence motifs in the sequence of the assigned peptide (e.g. the presence of a cysteine in the case of ICAT experiments or NX(S/T) motif in the case of experiments using glycopeptide enrichment strategies). PeptideProphet probabilities are reasonably accurate for both MS² and MS³ spectra. A plot displaying probability accuracies of PeptideProphet results for the 9-Mix data is provided in supplemental Fig. 2.

The approach used to accommodate the additional sequence matching information is similar to the method described previously (33) for adjusting probabilities to account for additional protein level information using the number of sibling peptides. The MS²/MS³ sequence match information is not available at the initial data analysis step but can be used to adjust the initial probabilities p(+|D) after linking the corresponding MS² and MS³ scans. Again the adjustment is performed separately for MS²- and MS³-level data. Given the sequence match category (Match) assignments for all linked spectra, the adjusted probability of a linked peptide assignment from a certain sequence match category, p(+|D, Match), may be calculated as

(Eq. 1)

where p(Match|+) and p(Match|–) represent the empirically derived probabilities of observing a peptide assignment in each match category among all (MS² or MS³) correct and incorrect peptide assignments in the data set, respectively. Note that this calculation assumes that the information derived from linking consecutive scans is independent of the identification information generated by a search engine. This is largely true. Normalized PeptideProphet SEQUEST discriminant score distributions for correct and incorrect peptide assignments to MS² spectra of doubly charged precursor ions, plotted separately for peptide assignments to MS² spectra belonging to different match categories, are shown in supplemental Fig. 3; score distributions are similar for all values of Match parameter, justifying the assumption of the independence between the discriminant database search score and Match parameter.

The probability distribution p(Match|+) may be calculated for each match category k as

(Eq. 2)

where N is the total number of (MS² or MS³) peptide assignments in the data set, and the sum is over all peptides i in each match category. The term p(Match|–) is calculated in a similar way. The overall proportion p(+) of correct assignment in the data set may be calculated as follows.

(Eq. 3)

The probabilities in Equation 1 and the Match parameter distributions in Equation 2 can be determined by starting with the initial PeptideProphet probability for each assignment, p(+|D_i), and the overall proportion, p(+). The probabilities and Match distributions can then be updated in an iterative manner. However, a single iteration was deemed to be sufficient for the data set used in this work.

Application of the Probability Adjustment Method to the 9-Mix Data Set—
Table III lists p(Match|+) and p(Match|–) distributions calculated using Equation 2 for the 9-Mix data set for both MS² and MS³ scans. It can be seen that, in the case of MS² spectra, a larger fraction of incorrect assignments have no consecutive matching scan. For all instances, the most likely sequence match category is category 1, corresponding to the case in which consecutive scans occur but with no matching sequence. This is perhaps intuitive in the sense that it might frequently be the case that either the MS² or the MS³ will produce an identifiable sequence but not both. The most obvious discriminating measure is the fact that for 30% of the correctly assigned MS² spectra (the top row in Table III) the linked MS³ spectrum was assigned a peptide sequence that is a subset of the MS² sequence as opposed to a 5% incidence for incorrect MS² identifications. If sequence matches are observed, identifications are thus much more likely to be correct; the same argument applies for MS³ scans preceded by MS² scans. Also noteworthy is the fact that for match category 1 pairs the probability of a correct identification is less than the probability of an incorrect identification. This will result in a probability penalty for consecutively linked scans without matching sequences. The penalty is small in this case, much smaller than the boost due to a consecutive matching scan, but is nevertheless an effect of the model.

An example of the probability adjustment procedure described above is illustrated in Fig. 2a using a pair of matching scans from the 9-Mix data set. MS² spectrum A06_7233_c.18651.18651 is first paired to MS³ spectrum A06_7233_c_18652.18652 by consecutive scan number. MS²-assigned peptide sequence TLNFNAEGEPELLMLANWRPAQPLK is then compared with MS³ sequence GEPELLMLANWRPAQPLK. Because the MS³ sequence represents a fragment of the MS² sequence, the linked pair is assigned to sequence match category 2. The adjusted probabilities are then calculated for each spectrum using Equation 1. In this instance, the initial PeptideProphet probability of 0.712 is adjusted to 0.995 for the MS² spectrum, and 0.832 is adjusted to 0.989 for the MS³ spectrum. A combined probability may then optionally be calculated for the linked pair as a new discriminating measure as discussed later in the text.

View larger version (37K): [in this window] [in a new window]   FIG. 2. Examples of MS2/MS3 linked pairs and the probability correction procedure. MS2 (left) and matching consecutive MS3 peak lists (right) are shown. The charge state of each spectrum is indicated in the upper left corner. a, example of the probability correction for a +3 MS2 +2 MS3 matched pair. b, a +2/+1 match pair for a phosphopeptide identification in which the y12 ion is selected for MS3. c, a +3/+1 identification; the y8 ion is selected for MS3. d, an example of a +2/+2 loss of the phosphate moiety in which the most abundant MS2 peak selected for MS3 is the doubly charged y13 – 98 Da.

In the development of the model, several (match category 2) cases were observed where both paired spectra had a low initial probability of being correct, but their probabilities became intermediate or even high values after adjustment. For example, the initial probabilities for peptide assignments to linked scans A06_7232_c.4362.4362.3 (MS² scan) and A06_7231_c.4363.4363.2 (MS³ scan) of 0.077 and 0.319 would get boosted to 0.827 and 0.830, respectively, if the probabilities were adjusted using the Match parameter distributions shown in Table II. Boosting such low probability assignments may be undesirable regardless of their match category. To address this, several approaches were investigated, including introduction of probability-dependent match categories. A very simple constraint that worked well in the case of the 9-Mix data set was to avoid any probability adjustment for category 2 matches if both initial MS² and MS³ probabilities were below a specified threshold, 0.5 in the case of these data. This was an optional feature that was investigated using the 9-Mix data set but not utilized for the phosphopeptide data sets as it was deemed a minor adjustment that did not significantly affect the overall results; specifically the number of entries in the 9-Mix data set that were affected by this exception was only 24 of a total 23,367 unique matches.

The improved discriminatory power of the adjusted probabilities, calculated using the p(Match|+) and p(Match|–) distributions shown in Table III (after the empirical correction described above), is indicated in Fig. 3, which shows receiver-operator characteristic curves for the data. The performance of the model is evaluated separately for MS² and MS³ spectra. The false positive error rate is plotted as a function of the sensitivity attainable by selecting a variable probability threshold. Sensitivity in this case is defined as the ratio of the number of correct peptide assignments to MS² (Fig. 3a) or MS³ scans (Fig. 3b) with a probability greater than or equal to a specific probability threshold and the total number of correct assignments to MS² (4870) or MS³ (1256) spectra, respectively. Similarly the false positive error rate is calculated as the fraction of incorrect matches in the total number of spectra above each probability threshold. Note that there is redundancy between the MS² and MS³ peptide assignments, so summing the total possible number of correct peptide identifications from both MS² and MS³ scans would not reflect the total number of unique identifications.

View larger version (20K): [in this window] [in a new window]   FIG. 3. Performance of MS2 and MS3 scores with probability adjustment. Error rate of MS2 (a) and MS3 (b) scores are shown as a function of sensitivity for initial (dashed) and adjusted (solid) probabilities. Inset panels are enlarged areas of the plots for the 0–10% error rate range.

Combining MS² and MS³ Probabilities—
The result of the probability adjustment procedure described above is now two adjusted probabilities for each unique linked pair of scans, one each for MS² and MS³. Possibilities for best utilizing both of these scores in selection of correct and incorrect identifications are now explored. Ideally a combined scoring approach would provide a greater discriminatory power for selecting correct and incorrect identifications than a subsequent counting of unique matches based on MS² and MS³ taken individually. Two possibilities for utilizing both scores are examined,

(Eq. 4)

(Eq. 5)

where p_MS² and p_MS³ are the adjusted probabilities for the MS² and MS³ scans, respectively, for the same linked pair. The first option is appropriate when the two probabilities can be considered independent and has been utilized (in a different context, i.e. for combining the evidence from different peptides) for the protein identification problem (33, 38). p_comb reflects the probability that at least one of the two peptide assignments, either to the MS² or to the MS³ spectrum, is correct. However, it is obvious that MS² and MS³ spectra, and therefore the probability scores p_MS² and p_MS³ of those spectra, are not fully independent measurements of a peptide in that identical ions will be measured in both spectra. An alternative approach is to select the assignment with the highest probability, p_max, thus reducing the likelihood of possible overestimation of the final probability. p_max has been used in other similar situations, e.g. in selecting among several alternative equivalent peptides (assignments of the same peptide to multiple MS/MS spectra) in the ProteinProphet protein probability score (33) and in Mascot protein-level scoring (24).

Fig. 4, a and b, show the results of counting the number of correct peptide assignments above specified probability thresholds, utilizing all possible scores calculated for a linked pair as the discriminating measure: initial MS², initial MS³, adjusted MS², adjusted MS³, p_max, and p_comb. Displayed are the results on the set of all unique linked pairs. A comparison of the initial and adjusted probability results for MS² and MS³ again demonstrates an increase in the number of selectable correct peptide assignments at any probability threshold as a result of the probability adjustment. Both p_max and p_comb scores perform similarly and provide improved discrimination as compared with the individual measures. Obviously the primary reason for the performance increase is the fact that the combined score permits the possibility of selecting either the MS² or the MS³ for any linked pair, thus permitting a pair to be selected as correct if either probability is above threshold. At the 99% probability threshold, for example, the adjusted MS², adjusted MS³, p_max, and p_comb probabilities correspond to 3141, 1050, 3775, and 3807 correct peptide identifications, respectively. Fig. 4c provides a measure of the rate of false positives on these data for the most interesting thresholds. The same performance trends are evident: including roughly 40 false positives, specifically 40, 41, 39, and 39 for adjusted MS², adjusted MS³, p_max, and p_comb measures, respectively, results in selection of 1806, 4139, 4594, and 4762 correct identifications. In all, p_comb provides the most discriminative measure.

View larger version (31K): [in this window] [in a new window]   FIG. 4. Discriminating power and accuracy of computed probabilities. a, total number (num) of correct peptide assignments is plotted as a function of minimum (min) probability threshold for MS2 and MS3 spectra alone, both initial and adjusted, and both pmax and pcomb scores. b, same as a, enlarged in the region of minimum probability threshold 0.9–1.0. c, number of correct peptide assignments as a function of the number of incorrect assignments plotted separately for MS2 (green) and MS3 (blue) initial (dashed) and adjusted (solid) probabilities as well as the combined pmax (red) and pcomb (purple). d, probability accuracy of the adjusted MS2, MS3, pmax, and pcomb probabilities.

Phosphopeptide Data Set Results—
One of the main motivating factors in collecting MS²/MS³ data is to increase the confidence levels and the total number of phosphopeptide identifications. The identification of phosphopeptides from MS² spectra is challenging because spectra recorded using an ion trap mass spectrometer often exhibit one or more dominant neutral loss peaks of 98 Da, whereas the occurrence and intensity of the other fragment ions (containing peptide sequence information) may be impaired. To investigate potential improvement in discrimination as a result of the probability adjustment on a phosphopeptide-enriched data set, a data set of MS spectra from a single LTQ injection of an IMAC-enriched D. melanogaster sample was selected for detailed analysis in this work. The data were acquired in a data-dependent mode with MS³ scans triggered for the most abundant peak of the MS² spectra that in the case of this sample mostly corresponds to the neutral loss peaks: –98.00 (–116.00), –49.00 (–58.00), and –32.60 (–36.66) Da from the precursor as explained under "Experimental Procedures." Because the sample in this case is a complex protein mixture, a precise labeling of peptide identifications as correct or incorrect is not possible. Instead only the composite false discovery rates (FDRs) (a single measure for each filtering threshold) can be estimated by counting the number of matches to reversed sequences.

The methodology for generating adjusted probability scores for this data set is analogous to the 9-Mix data set. Top scoring MS² and MS³ SEQUEST peptide assignments are linked based on consecutive scan numbers, and the top scoring pair for consecutive scans is selected. Note that if MS³ spectra are triggered based on neutral loss peaks charge state ambiguity between matching pairs can potentially be reduced. This fact is not exploited in our analysis; rather we maintain the same procedure for allowing all possible charge pairs in a match. The match pairs are then classified into sequence match categories as described above. The same four sequence match categories are used: 0, no consecutive match; 1, consecutive match but no matching sequence; 2, matching sequences with MS³ sequence a subset of MS² sequence; and 3, matching sequences with MS³ sequence identical to MS². In this data set, there were only two instances of scans that would correspond to the sequence match category 4, matching sequences with MS² sequence a subset of MS³ sequence. Again this category was eliminated for simplicity. We note that the additional constraints imposed by the data-dependent triggering of these data and the resultant database searching provisions would allow us to generate additional useful sequence match categories, corresponding to whether the site of modification of a match is identical between the two sequences. We observed a number of instances in these data where the sequences matched but the sites of modification of the match did not, indicating ambiguity in the localization of the modified residues. A larger data set would allow a more rigorous analysis of these types of results (39, 40).

SEQUEST searching of this data set produced 16,647 and 12,218 results for the MS² and MS³ data sets, respectively, corresponding to 7547 unique matching pairs of searched results. Of these, 6270 had non-null MS³ assignments. Counts for the four sequence match categories are shown in Table IV. Most significant is the fact that the sequence match category corresponding to neutral loss-only pairs (match category 3) is no longer null; rather it is the more abundant category among the two representing matching sequences with 313 unique matches.

Although a true sensitivity measure for these data is impossible, it is possible to evaluate the relative performance of the various probability measures by examining the number of reversed database matches. The decoy database method is increasingly being used as an effective means of estimating false positive rates in database searching when other methods of error rates estimation cannot be readily performed (41, 42). At any given probability threshold, the number of matches to reversed sequences can be calculated and compared with the total number of peptide assignments above that threshold to derive an estimate of the FDR (42). A measure of the performance of the various model probabilities on these data is shown in Fig. 5a. The figure plots the estimated number of correct identifications as a function of FDR. These data are generated by ranking all peptide assignments in order of decreasing probability. The number of assignments of peptides from the forward database (n_f) having a probability equal or greater than the probability of the nth top ranking reverse entry (n_r) is counted, and the estimated false discovery rate is determined as n_r/n_f. The estimated number of correct assignments is similarly measured as n_f – n_r. This analysis is done separately for each of the initial and adjusted probability measures: MS² and MS³ initial and adjusted as well as the combined probability measures p_comb and p_max. A version of these data in table form is provided in supplemental Table 2, which presents estimated false positive percentages and number of forward match counts for inclusion of one, two, five, 10, 50, and 100 reversed matches as well as the number of those forward entries that are identified as containing phosphorylation sites.

View larger version (20K): [in this window] [in a new window]   FIG. 5. Performance of probability scores on the phosphopeptide data set. The number (num) of correct identifications estimated using the decoy database method is plotted as a function of FDR estimated using the decoy database search method. a, results for the phosphopeptide data set; b, results for the non-phosphorylated identifications only in the phosphopeptide data set. For MS2 and MS3 results, dashed lines indicate initial and solid lines indicate corrected probability scores.

The results discussed above for this sample have focused on the total number of identifications, the majority of which are phosphopeptides. An equivalent plot of the results, but including only ranked non-phosphorylated identifications from the phosphopeptide data set, is shown in Fig. 5b. In general, the same trends can be seen; the model improves the assignment scores of unmodified peptides as well.

Example MS² and MS³ Spectra from the Phosphopeptide Data Set—
To understand the underlying reasons for improved identification confidence, it is informative to briefly revisit the example shown in Fig. 2. These spectra are representative illustrations of matched MS² and MS³ phosphopeptide spectra of various precursor charge states. Several spectral features are of interest. Fig. 2b shows an example of a +2/+1 match pair. The threonine in position 3 of the sequence matching the MS² spectrum is phosphorylated. The large y₁₂ peak corresponding to a fragmentation N-terminal to a double proline was selected by the instrument for MS³. This is a general characteristic of the singly charge spectra corresponding to correct identifications in these data: the majority are proline-directed with a Pro identified in the first position. Although the fragmentation is reasonable in this MS³ spectrum, a large fraction of singly charged spectra exhibit poor fragmentation with one or two major peaks corresponding to Pro, Asp, or occasionally Glu cleavage dominating. This is not surprising due to the relatively low energy imparted to singly charged ions via CID in a trap instrument; typically the most facile fragments are the most readily observable. As can be seen, many of the same ions occur in both spectra. However, the shorter sequence and the absence of the phosphorylated residue in the MS³ simplify the spectrum and increases confidence in the identification. Fig. 2c shows a +3/+1 phosphopeptide example. +3/+1 instances are rarer than the +2/+1 (see supplemental Fig. 3), and the same trends occur. The MS³ spectrum shown is a proline-directed fragmentation event with Asp-directed fragmentation peaks dominating the spectrum.

Fig. 2d is an example of a +2/+2 phosphopeptide ion. The peak selected for MS³ corresponds to the doubly charged y₁₃ peak with a –98-Da loss of the phosphate moiety. Although many identical ions are identified in both spectra, there is a significant difference in the fragmentation pattern with several ions observable in MS³ that are not readily observable in MS².

Data Set Dependence of Probability Adjustment—
Because the two primary data sets used in this work differ significantly in terms of sample complexity, it is also informative to compare these two data sets with respect to the MS²/MS³ matching statistics and the degree to which the initial peptide probabilities are adjusted to account for the sequence match information. The Match parameter distributions p(Match|+) and p(Match|–) vary between the data sets, reflecting the differences in the sample complexity and data set size. This is illustrated in Fig. 6, which plots the logarithm of the ratio p(Match|+)/p(Match|–) for each match category k for both data sets. A ratio greater than 1 (log ratio greater than 0) indicates the region where the probabilities are boosted after adjustment for Match information, whereas a ratio less than 1 (log ratio below 0) indicates that the Match adjustment reduces the probability that a peptide assignment is correct. Although the overall trend is similar for both data sets, significant differences exist in the amount of adjustment. For example, the penalty applied to a peptide assignment to a MS² spectrum with no subsequent MS³ spectrum (match category 0) is approximately twice as high in the case of the phosphopeptide-enriched data set than in the 9-Mix data set. On the other hand, the amount of probability boost for peptide assignments in the Match = 2 category is higher in the case of the 9-Mix data set. A better understanding of these results requires analysis of the MS²/MS³ linking statistics for a larger data set. However, it is clear that the amount of probability adjustment in each sequence match category is data set-dependent. Thus, it is advantageous to use statistical methods for combining MS²- and MS³-level data that can learn the appropriate amount of probability adjustment from the data itself, such as the method presented in this work.

View larger version (18K): [in this window] [in a new window]   FIG. 6. Degree of probability score adjustment by sequence match category for the 9-Mix and phosphopeptide data sets.

LC-MS/MS analysis was performed on two additional IMAC-enriched whole cell D. melanogaster tryptic digests using a Thermo LTQ as described under "Experimental Procedures." Each sample was separated into two equal fractions that were run individually using the MS²/MS³ run method or the MS²-only method. MS² and MS³ peak lists were extracted from the raw data file and searched separately using SEQUEST. Final SEQUEST reports were then combined into two final result sets for each pair of experiments, one set for the MS²/MS³ and one for the MS²-only data. These four result sets were then analyzed using Peptide/ProteinProphet.

To compare results at both the peptide and protein levels, individual identifications for each of the two final result sets were grouped based either on unique peptide sequence or protein accession numbers. The union, intersection, and differences between the MS²/MS³ and MS²-only runs were calculated. The results are displayed as Venn diagrams in Fig. 7 for both pairs of experiments. Given that there was significant variation between the number of peptide and protein identifications of the same run method, the two pairs of experiments were not combined to reduce the effect of instrument sampling rate variability in peptide identification, providing a more fair assessment of differences between the two methods. The top pair of Venn diagrams indicate the number of unique proteins identified by each method. Proteins were included in a set if they participated in an identified protein group (see Ref. 33) with a group probability of at least 0.95. Proteins from the same group (indistinguishable proteins given the sequences of identified peptides) were counted as a single entry. The lower set of Venn diagrams shows unique peptide identifications. Peptides were included in these sets if their modified sequences were unique, i.e. two peptides with any modification or sequence differences were considered two unique peptides for the main figure. PeptideProphet probability scores of 0.95 or above were required for inclusion. Peptide uniqueness can be defined by a number of standards, however; and the number of identifications listed in each area of the Venn diagram may be overestimated depending on the definition. The breakout boxes for each of the peptide sets indicate the number for each region of the Venn diagram under four alternative definitions of peptide uniqueness. Under the Type 1 definition, peptides identified from consecutive MS² and MS³ scans that differ only by the loss of one or more phosphate groups on one of the residues (i.e. MS³ was triggered on the neutral loss) were considered identical and counted as one. Under the Type 2 definition, peptides that differ at the N or C terminus by one or more amino acid residues (e.g. due to a missed cleavage) were considered identical, e.g.

6

where S+80 indicates a phosphorylated Ser residue. Under the Type 3 definition, peptides were counted as identical if they had the same sequence, but the modification site was ambiguous (residues identified as being phosphorylated are within three amino acid sequences of each other) as follows e.g.

7

Under the Type 4 definition, peptides were counted as unique based on the sequence alone; e.g.

8

were considered identical sequences. Although these four definitions do not include all possible types and permutations that occur, using them to count peptides allows a more comprehensive comparison between the data sets.

View larger version (22K): [in this window] [in a new window]   FIG. 7. Comparison of MS2/MS3 and MS2-only experimental runs. Two equivalent pairs of runs are shown, labeled Run1–Run4. Venn diagrams display overlap between MS2/MS3 (left) and MS2-only (right) data sets based on unique identifications at the peptide and protein levels. All identifications are based on a 95% probability threshold. The top diagrams display protein identifications based on unique UniProt entry name. The numbers represent the number of ProteinProphet protein groups that have a protein group probability equal to or greater than 0.95. The lower diagrams show the same for peptide identifications based on peptide sequence using initial PeptideProphet probability scores. Peptide identifications with the least stringent, most inclusive uniqueness criteria are shown in the main figure. Counts for each region of the diagram utilizing more stringent uniqueness criteria are shown in the boxes, labeled as "- Type".

Overall these results indicate that generation of MS³ data may result in a decrease in the number of unique peptide and protein identifications. However, several additional comments are necessary for more objective evaluation of the benefits of acquiring MS³ data. First the probabilities used in the comparison presented above (Fig. 7) were the original probabilities generated by the PeptideProphet and ProteinProphet tools. The probability correction procedure described in this work should permit the selection of a greater number of peptides (and therefore proteins) at a fixed FDR, which would potentially mitigate the loss of sequence coverage. Furthermore if the goal of the study is to identify as many unique modification states as possible, MS³ data may improve the results. It should also be mentioned that the phosphopeptide data sets used in this work were of high quality (high degree of phosphopeptide enrichment), resulting in sufficiently strong intensity MS signal of phosphopeptide ions and relatively good MS² fragmentation. On the other hand, it is possible that in other data sets (e.g. no or poor phosphopeptide enrichment), the relatively low abundance of phosphorylated peptides would lead to less intense MS signal and less interpretable MS² spectra, thus making benefits of acquiring MS³ data more apparent.

Concluding Remarks—
The generation of MS³ information is common in directed areas of proteomics such as phosphopeptide identification. Whether generation of MS³ information is the best strategy or not is partially dependent on the overall goals of the experiment. Data generated from a complex phosphopeptide-enriched sample suggest that generation of MS³ spectra can potentially result in an increased number of unique phosphorylation site identifications. On the other hand, the cycle time spent on generation of MS³ data does appear to detract from the overall number of unique peptides (by sequence only) and proteins identified in such an experiment. Also although MS² spectra in which neutral loss peaks are dominant are still observed in current generation trap instruments, these spectra appear to frequently contain better backbone fragmentation than older equivalents due to increased ion capacity of the trap. Nevertheless in experiments in which MS³ data have been generated, MS²/MS³ matching information from the entire experiment can be used to adjust the probabilities of the individual peptide assignments, which has the effect of compensating for the reduced number of MS² spectra.

In cases in which a very high certainty in a mapped phosphorylation site is needed, MS³ experiments are highly valuable as exemplified in the mapping of phosphorylation sites for which biological follow-up experiments are performed. Also in cases in which neither measurement time nor the amount of phosphopeptide samples are limiting factors, the measurement of MS³ spectra is advantageous. In fact, in an experimental setup that aims to maximize the number of identified phosphorylation sites from a complex sample, one efficient strategy is to first perform MS² experiments and then target specifically the unidentified phosphopeptide ions using MS²/MS³ measurements (22, 44).

Generally speaking, much of proteomics data analysis relies on the scores and probabilities produced by automated search algorithms. It is thus important that any probability measure is accurate and makes use of all available information, particularly in situations where the targeted peptide identifications are rare, e.g. for phosphopeptides and/or when proteins are identified by a reduced number of peptides (such as an analysis in which N-terminal peptides are enriched). Here we have described methods for translating the additional information obtained by matching coupled peptide assignments to MS² and MS³ spectra into a combined probability score, improving the ability to discriminate between true positive and false positive identifications. We have demonstrated an increase in sensitivity and a corresponding decrease in the error rate of selecting correct identifications as a result of the adjusted probability using a mixture of known standard proteins and applied the method to a complex phosphopeptide-enriched data set, demonstrating an improved discrimination between correct and incorrect peptide assignments for that sample.

The goal of this study was to describe a relatively simple but valid mechanism for adjusting probabilities of peptide identifications in scenarios in which standard database searching has been performed on MS²/MS³ data sets. An alternative computational strategy for accommodating MS³ information is to merge MS² and MS³ spectra into a single spectrum prior to database searching. Full investigation of the relative merits of pre-database search, spectral merging approaches versus a post-database search probability adjustment procedure such as the one discussed here is beyond the scope of this work but is the subject of current investigation. Other methodologies, such as merging spectra from differently charged precursors of the same peptide, could likely be utilized to improve peptide identification as well.

As instrumentation continues to improve the speed and accuracy of tandem MS measurements, the ability to generate complementary information such as MS³ spectra for any given ion will become increasingly practical. Methods for accommodating this information are consequently useful and can significantly improve the quality of the results generated by automated processing of mass spectrometry data.

Data and Code Availability—
mzXML and raw data files and processed unique linked pair data for both the 9-Mix and phospho samples are available on line via the Tranche system (ProteomeCommons). The software used in this work was developed in Python. Python modules were implemented making use of the code library available with the InsPecT software package by the University of California San Diego Computational Mass Spectrometry Research Group (28). All code modules generated by our group for this project are available upon request.

	ACKNOWLEDGMENTS

 
We thank Steven Tanner and the University of California San Diego Computational Research Group for the free availability of their code. All annotated spectra in this manuscript were generating using the Label.py and MakeImage.py modules available in the InsPecT library.

	FOOTNOTES

 
Received, March 23, 2007, and in revised form, September 12, 2007.

Published, MCP Papers in Press, September 13, 2007, DOI 10.1074/mcp.M700128-MCP200

¹ The abbreviations used are: MS² or MS/MS, tandem mass spectrometry; MS³, three-stage mass spectrometry (MS/MS/MS); FDR, false discovery rate; PTM, post-translational modification; TPP, Trans-Proteomic Pipeline.

² Seattle Proteome Center, Institute for Systems Biology: tools.proteomecenter.org/software.php.

³ The default option in PeptideProphet is to set SEQUEST Cn score to 0 to reduce the probability that the best scoring peptide assignment to a spectrum is correct when the second best scoring peptide has high sequence homology.

^* This work was supported in part by NCI, National Institutes of Health (NIH) Grant CA-126239 (to A. I. N.), by NIH/National Center for Research Resources-National Resource for Proteomics and Pathways Grant P41-18627 (to P. C. A.), and with funds from NHLBI, NIH under Contract N01-HV-28179 (to R. A.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

^S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.

^|| Recipient of a fellowship by the Boehringer Ingelheim Fonds.

^¶¶ To whom correspondence should be addressed: Dept. of Pathology, University of Michigan, 4237 Medical Science I, Ann Arbor, MI 48109. Tel.: 734-764-3516; E-mail: nesvi{at}med.umich.edu

	REFERENCES

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS AND DISCUSSION REFERENCES

 

1. Hager, J. W. (2002 ) A new linear ion trap mass spectrometer. Rapid Commun. Mass Spectrom. 16, 512 –526[CrossRef]

2. Aebersold, R., and Mann, M. (2003 ) Mass spectrometry-based proteomics. Nature 422, 198 –207[CrossRef][Medline]

3. Aebersold, R., and Goodlett, D. R. (2001 ) Mass spectrometry in proteomics. Chem. Rev. 101, 269 –295[CrossRef][Medline]

4. Nesvizhskii, A. I. (2006 ) Protein identification by tandem mass spectrometry and sequence database searching. Methods Mol. Biol. 367, 87 –120

5. Nesvizhskii, A. I., and Aebersold, R. (2005 ) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 4, 1419 –1440[Abstract/Free Full Text]

6. Sadygov, R. G., Cociorva, D., and Yates, J. R., III (2004 ) Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book. Nat. Methods 1, 195 –202[CrossRef][Medline]

7. Steen, H., and Mann, M. (2004 ) The ABC’s (and XYZ’s) of peptide sequencing. Nat. Rev. Mol. Cell. Biol. 5, 699 –711[CrossRef][Medline]

8. Nesvizhskii, A. I., Roos, F. F., Grossmann, J., Vogelzang, M., Eddes, J. S., Gruissem, W., Baginsky, S., and Aebersold, R. (2006 ) Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell. Proteomics 5, 652 –670[Abstract/Free Full Text]

9. Pevzner, P. A., Mulyukov, Z., Dancik, V., and Tang, C. L. (2001 ) Efficiency of database search for identification of mutated and modified proteins via mass spectrometry. Genome Res. 11, 290 –299[Abstract/Free Full Text]

10. Adachi, J., Kumar, C., Zhang, Y., Olsen, J. V., and Mann, M. (2006 ) The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol. 7, R80[CrossRef][Medline]

11. Olsen, J. V., and Mann, M. (2004 ) Improved peptide identification in proteomics by two consecutive stages of mass spectrometric fragmentation. Proc. Natl. Acad. Sci. U. S. A. 101, 13417 –13422[Abstract/Free Full Text]

12. Beausoleil, S. A., Jedrychowski, M., Schwartz, D., Elias, J. E., Villen, J., Li, J., Cohn, M. A., Cantley, L. C., and Gygi, S. P. (2004 ) Large-scale characterization of HeLa cell nuclear phosphoproteins. Proc. Natl. Acad. Sci. U. S. A. 101, 12130 –12135[Abstract/Free Full Text]

13. Bodenmiller, B., Mueller, L. N., Pedrioli, P. G. A., Pflieger, D., Jünger, M. A., Eng, J., Aebersold, R., and Tao, W. A. (2007 ) An integrated chemical, mass spectrometric and computational strategy for (quantitative) phosphoproteomics: Application to Drosophila melanogaster Kc167 Cells. Mol. Biosyst. 3, 275 –286[CrossRef][Medline]

14. Gruhler, A., Olsen, J. V., Mohammed, S., Mortensen, P., Faergeman, N. J., Mann, M., and Jensen, O. N. (2005 ) Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell. Proteomics 4, 310 –327[Abstract/Free Full Text]

15. Macek, B., Waanders, L. F., Olsen, J. V., and Mann, M. (2006 ) Top-down protein sequencing and MS3 on a hybrid linear quadrupole ion trap-orbitrap mass spectrometer. Mol. Cell. Proteomics 5, 949 –958[Abstract/Free Full Text]

16. Zabrouskov, V., Senko, M. W., Du, Y., Leduc, R. D., and Kelleher, N. L. (2005 ) New and automated MSn approaches for top-down identification of modified proteins. J. Am. Soc. Mass Spectrom. 16, 2027 –2038[CrossRef][Medline]

17. Zhang, Z., and McElvain, J. S. (2000 ) De novo peptide sequencing by two-dimensional fragment correlation mass spectrometry. Anal. Chem. 72, 2337 –2350[Medline]

18. Demelbauer, U. M., Zehl, M., Plematl, A., Allmaier, G., and Rizzi, A. (2004 ) Determination of glycopeptide structures by multistage mass spectrometry with low-energy collision-induced dissociation: comparison of electrospray ionization quadrupole ion trap and matrix-assisted laser desorption/ionization quadrupole ion trap reflectron time-of-flight approaches. Rapid Commun. Mass Spectrom. 18, 1575 –1582[CrossRef][Medline]

19. LeDuc, R. D., Taylor, G. K., Kim, Y. B., Januszyk, T. E., Bynum, L. H., Sola, J. V., Garavelli, J. S., and Kelleher, N. L. (2004 ) ProSight PTM: an integrated environment for protein identification and characterization by top-down mass spectrometry. Nucleic Acids Res. 32, W340 –W345[Abstract/Free Full Text]

20. Frank, A., and Pevzner, P. (2005 ) PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal. Chem. 77, 964 –973[Medline]

21. Goodlett, D. R., Keller, A., Watts, J. D., Newitt, R., Yi, E. C., Purvine, S., Eng, J. K., von Haller, P., Aebersold, R., and Kolker, E. (2001 ) Differential stable isotope labeling of peptides for quantitation and de novo sequence derivation. Rapid Commun. Mass Spectrom. 15, 1214 –1221[CrossRef][Medline]

22. Domon, B., and Aebersold, R. (2006 ) Mass spectrometry and protein analysis. Science 312, 212 –217[Abstract/Free Full Text]

23. Regnier, F. E., and Liu, P. (2002 ) An isotope coding strategy for proteomics involving both amine and carboxyl group labeling. J. Proteome Res . 1, 443 –450[CrossRef][Medline]

24. Perkins, D. N., Pappin, D. J., Creasy, D. M., and Cottrell, J. S. (1999 ) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551 –3567[CrossRef][Medline]

25. Craig, R., and Beavis, R. C. (2004 ) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466 –1467[Abstract/Free Full Text]

26. Eng, J. K., McCormack, A. L., and Yates, J. R., III (1994 ) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976 –989[CrossRef]

27. Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L., Xu, M., Maynard, D. M., Yang, X., Shi, W., and Bryant, S. H. (2004 ) Open mass spectrometry search algorithm. J. Proteome Res. 3, 958 –964[CrossRef][Medline]

28. Tanner, S., Shu, H., Frank, A., Wang, L. C., Zandi, E., Mumby, M., Pevzner, P. A., and Bafna, V. (2005 ) InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. Anal. Chem. 77, 4626 –4639[Medline]

29. Zhang, N., Aebersold, R., and Schwikowski, B. (2002 ) ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data. Proteomics 2, 1406 –1412[CrossRef][Medline]

30. Bodenmiller, B., Mueller, L. N., Mueller, M., Domon, B., and Aebersold, R. (2007 ) Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nat. Methods 4, 231 –237[CrossRef][Medline]

31. Keller, A., Eng, J., Zhang, N., Li, X. J., and Aebersold, R. (2005 ) A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1, 2005.0017

32. Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R. (2002 ) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383 –5392[Medline]

33. Nesvizhskii, A. I., Keller, A., Kolker, E., and Aebersold, R. (2003 ) A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646 –4658[Medline]

34. Bairoch, A., Apweiler, R., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., Martin, M. J., Natale, D. A., O’Donovan, C., Redaschi, N., and Yeh, L. S. (2005 ) The Universal Protein Resource (UniProt). Nucleic Acids Res. 33, D154 –D159[Abstract/Free Full Text]

35. Salek, M., and Lehmann, W. D. (2003 ) Neutral loss of amino acid residues from protonated peptides in collision-induced dissociation generates N- or C-terminal sequence ladders. J. Mass Spectrom. 38, 1143 –1149[CrossRef][Medline]

36. Martin, D. B., Eng, J. K., Nesvizhskii, A. I., Gemmill, A., and Aebersold, R. (2005 ) Investigation of neutral loss during collision-induced dissociation of peptide ions. Anal. Chem. 77, 4870 –4882[Medline]

37. Malmstrom, J., Lee, H., Nesvizhskii, A. I., Shteynberg, D., Mohanty, S., Brunner, E., Ye, M., Weber, G., Eckerskorn, C., and Aebersold, R. (2006 ) Optimized peptide separation and identification for mass spectrometry based proteomics via free-flow electrophoresis. J. Proteome Res. 5, 2241 –2249[CrossRef][Medline]

38. MacCoss, M. J., Wu, C. C., Yates, J. R., III (2002 ) Probability-based validation of protein identifications using a modified SEQUEST algorithm. Anal. Chem. 74, 5593 –5599[Medline]

39. Olsen, J. V., Blagoev, B., Gnad, F., Macek, B., Kumar, C., Mortensen, P., and Mann, M. (2006 ) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635 –648[CrossRef][Medline]

40. Villen, J., Beausoleil, S. A., Gerber, S. A., and Gygi, S. P. (2007 ) Large-scale phosphorylation analysis of mouse liver. Proc. Natl. Acad. Sci. U. S. A. 104, 1488 –1493[Abstract/Free Full Text]

41. Elias, J. E., and Gygi, S. P. (2007 ) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207 –214[CrossRef][Medline]

42. Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J., and Gygi, S. P. (2003 ) Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J. Proteome Res. 2, 43 –50[CrossRef][Medline]

43. Li, X., Gerber, S. A., Rudner, A. D., Beausoleil, S. A., Haas, W., Villen, J., Elias, J. E., and Gygi, S. P. (2007 ) Large-scale phosphorylation analysis of -factor-arrested Saccharomyces cerevisiae. J. Proteome Res. 6, 1190 –1197[CrossRef][Medline]

44. Picotti, P., Aebersold, R., and Domon, B. (2007 ) The implications of proteolytic background for shotgun proteomics. Mol. Cell. Proteomics 6, 1589 –1598[Abstract/Free Full Text]

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