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. Author manuscript; available in PMC: 2013 Sep 13.
Published in final edited form as: J Comput Biol. 2007 Oct;14(8):1025–1043. doi: 10.1089/cmb.2007.0071

HMMatch: Peptide Identification by Spectral Matching of Tandem Mass Spectra Using Hidden Markov Models

Xue Wu 1, Chau-Wen Tseng 1, Nathan Edwards 2
PMCID: PMC3772688  NIHMSID: NIHMS38764  PMID: 17985986

Abstract

Peptide identification by tandem mass spectrometry is the dominant proteomics workflow for protein characterization in complex samples. The peptide fragmentation spectra generated by these workflows exhibit characteristic fragmentation patterns that can be used to identify the peptide. In other fields, where the compounds of interest do not have the convenient linear structure of peptides, fragmentation spectra are identified by comparing new spectra with libraries of identified spectra, an approach called spectral matching. In contrast to sequence-based tandem mass spectrometry search engines used for peptides, spectral matching can make use of the intensities of fragment peaks in library spectra to assess the quality of a match. We evaluate a hidden Markov model approach (HMMatch) to spectral matching, in which many examples of a peptide's fragmentation spectrum are summarized in a generative probabilistic model that captures the consensus and variation of each peak's intensity. We demonstrate that HMMatch has good specificity and superior sensitivity, compared to sequence database search engines such as X!Tandem. HMMatch achieves good results from relatively few training spectra, is fast to train, and can evaluate many spectra per second. A statistical significance model permits HMMatch scores to be compared with each other, and with other peptide identification tools, on a unified scale. HMMatch shows a similar degree of concordance with X!Tandem, Mascot, and NIST's MS Search, as they do with each other, suggesting that each tool can assign peptides to spectra that the others miss. Finally, we show that it is possible to extrapolate HMMatch models beyond a single peptide's training spectra to the spectra of related peptides, expanding the application of spectral matching techniques beyond the set of peptides previously observed.

Keywords: computational molecular biology, mass spectroscopy, HMM, peptide identification, algorithms

1. Introduction

High-throughput peptide identification using tandem mass spectrometry (MS/MS) is a widely used technique and an important component of the rapidly growing field of proteomics. The peptide fragmentation spectra generated by these workflows exhibit characteristic fragmentation patterns that can be used to identify the peptide. The most frequently observed peptide fragments correspond to cleavage of the peptide backbone, revealing the amino-acid sequence of the peptide. With sufficient peptide backbone fragments, each backbone position is identified and the mass contribution of successive amino-acids can be observed.

The two dominant algorithmic techniques for peptide identification from tandem mass spectra both rely primarily on the masses of observed peaks. De novo peptide identification algorithms (Taylor and Johnson, 1997; Dancik et al., 1999; Chen et al., 2000; Bafna and Edwards, 2003; Ma et al., 2003; Frank and Pevzner, 2005) reconstruct the peptide sequence directly from the observed fragment ions. This approach works well when each peptide-backbone bond is represented by a fragment ion in the spectrum. Sequence database search engines (Yates et al., 1996; Perkins et al., 1999; Craig and Beavis, 2004; Geer et al., 2004; Tanner et al., 2005) use protein sequence databases to suggest peptide candidates to be matched to each spectrum. Given a peptide sequence, the masses of its potential fragments are determined, and candidate sequences may be scored on the basis of matches to the fragment masses. This approach has been enormously successful, despite the fact that the popular search engines make no attempt to predict the intensity of observed fragment ions. Even the search engines, such as SEQUEST, that use a theoretical spectrum to score peptides typically give all fragment ions the same intensity.

Various projects (Bafna and Edwards, 2001; Schutz et al., 2003; Zhang, 2004; Wan et al., 2006) have sought to predict the probability of observing an ion or to predict its intensity based on intrinsic properties of the peptide or peptide fragment, including the peptide sequence, the ion cleavage position, the ion-type (chemical bond cleaved), adjacent amino-acids, and peptide charge state. While these techniques show promise, they have yet to demonstrate a significant increase peptide identification sensitivity or specificity. In practice, these sophisticated models seem to identify relatively few additional spectra.

In other fields, where the compounds of interest do not have the convenient linear structure of peptides, fragmentation spectra are identified by comparing new spectra with libraries of identified spectra, an approach called spectral matching. This approach makes explicit use of the intensities observed in previously identified spectra. No attempt is made to predict, a priori, the spectrum of an unknown molecular species, but once observed, identified and recorded, the time-consuming task of identification need not be repeated. Using mass and intensity information, spectral matching assigns identifications with higher confidence than by using mass alone. Even a peptide fragmentation spectrum with only a few peaks might be strongly characteristic of that peptide, despite insufficient fragmentation to disambiguate similar peptide sequences.

Traditionally spectral matching approaches utilize a library of spectra and a spectral similarity measure that evaluates the quality of the spectrum-spectrum match (Stein, 1994, 1995; Stein and Scott, 1994). Library spectra may be real spectra, identified using traditional sequence database search tools, or artificial consensus spectra that summarize the most important features of spectra from a particular peptide. Spectral comparison functions have traditionally been based on the dot-product of a vectorized representation of each spectrum. Recently, a number of groups have described spectral libraries of high-quality real spectra with high-confidence identifications (Yates et al., 1998; Johnson et al., 2006; Frewen et al., 2006; Lam et al., 2007) and consensus spectra (Stein et al., 2006; Lam et al., 2006; Craig et al., 2006). Results indicate spectral matching can be both precise and efficient, identifying many spectra missed by sequence based search algorithms.

We believe that while the dot-product spectral comparison and consensus spectrum techniques are quite effective, significant information contained in the experimental spectra is being discarded. The dot-product, for example, weights the contribution of each peak of the experimental mass spectrum only by its intensity (without regard to its consistency), while the consensus spectrum presumes that a single intensity value is sufficient to represent the range of intensities observed in the experimental spectra. We propose, instead, employing a hidden Markov model (HMM) (Rabiner, 1989) approach to spectral matching to resolve these deficiencies. Using hidden Markov models allow examples of a peptide's fragmentation spectrum to be summarized in a generative probabilistic model, making it possible to weight the contribution of each ion according to its likelihood of observation and intensity variation.

Hidden Markov models have proved to be a powerful technique in statistical machine learning, and have been used extensively in de novo gene finding and protein family clustering and prediction (Krogh et al., 1994; Eddy, 1998; Finn et al., 2006). In particular, the use of hidden Markov models for protein families, as in Pfam (Finn et al., 2006), suggests that they may find application in spectral matching. Given a multiple alignment of protein sequences known to belong to the same protein family, the Pfam hidden Markov model represents a statistical consensus for the amino acids observed at each position. Conserved positions tightly constrain protein family membership, while the contribution of highly variable positions are down-weighted. The model also permits the insertion or deletion of amino-acids as necessary to align diverged sequences at positions of significant conservation. The Pfam hidden Markov model, as a statistical signature of a protein family, is more sensitive than sequence alignment with any single member of the family, and more specific than aligning against all protein family members in turn.

Figure 1 summarizes the statistical properties of the low-mass region of a set of spectra with high-confidence identifications to the peptide DLATVYVDVLK. This complex figure shows the distribution, using a vertical box-plot, of normalized peak intensities before log10 intensity transformation (see Methods section, Spectrum Normalization), for each spectrum, in various m/z value regions. Regions denoted I0, I1, … represent m/z value regions between singly charged b and y ions of peptide DLATVYVDVLK, while the regions denoted b1, b2, … and y1, y2, … represent m/z values within 0.5 Da of the corresponding b and y ions. Above each box-plot is the average frequency of selected peaks, per spectrum, in each m/z region. While there is much consistency in the intensity of some ions, others show considerable variation, and some b and y ions are completely absent. We will use hidden Markov models to capture this complexity.

FIG. 1.

FIG. 1

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Normalized intensity distribution (before log10 transformation) of low-mass peaks in m/z regions between (I0, …) and near singly charged b (b1, …) and y (y1, …) ions from training spectra for peptide DLATVYVDVLK. Average peak frequency, per spectrum, in each m/z region is indicated above each normalized intensity box-plot.

We train hidden Markov models on collections of mass spectra confidently assigned to a specific peptide by the widely used X!Tandem (Craig and Beavis, 2004) sequence database search engine. The hidden Markov models are then used to evaluate additional mass spectra, and the results compared with X!Tandem, Mascot (Perkins et al., 1999), another popular sequence database search engine, and NIST's MS Search software (Mallard et al., 2005), a dot-product based spectral matching search engine recently updated to search libraries of peptide fragmentation spectra (Stein et al., 2006). We point out that this approach is different from PepHMM (Wan et al., 2006), which uses a single hidden Markov model to evaluate the quality of any peptide-spectrum match. Our results show that the HMM MS/MS Match (HMMatch) approach identifies many mass spectra left unidentified by Mascot and X!Tandem and is more flexible and robust than MS Search. In the remainder of this paper we describe the HMMatch design, and then present an experimental evaluation of HMMatch for peptide identification.

2. Methods

Mass spectra libraries

Public LC-MS/MS datasets derived from human samples are downloaded from their respective data repositories. Sources include PeptideAtlas (Desiere et al., 2006) and Human Proteome Project (HUPO) Plasma Proteome Project (PPP) (Omenn et al., 2005). In all, more than 2.3 million spectra are stored locally for searching, representing 722 data files, and about 33 different labs or projects. Tandem mass spectra are re-searched using conservative search parameters, such as 1 missed cleavage, tryptic N and C-terminals, methionine oxidation and cysteine alkylation modifications only, precursor mass tolerance 2 Da, and fragment mass tolerance 0.4 Da. We use a computational grid of approximately 250 Linux CPUs managed by the Condor scheduling infrastructure, and the X!Tandem search engine (Craig and Beavis, 2004) for re-searching public datasets. The Mascot search engine (Perkins et al., 1999), tied to a single processor, is used for benchmarking, comparison studies, and to confirm specific identifications.

Training spectra

The results of re-searching public LC-MS/MS spectra are stored in a relational database and frequently observed peptides are identified. A small set of peptides (in a particular charge state) with many high-confidence peptide identifications are chosen, and a HMMatch model is constructed for each such peptide. For each peptide model, we extract the set of MS/MS spectra with precursors within 2 Da of the peptide's theoretical monoisotopic m/z value.

These spectral sets are partitioned into 5 classes based on their X!Tandem search results: High confidence identifications of the model peptide (denoted HC), low confidence and non-significant identifications of the model peptide (LC), high confidence identifications of a non-model peptide (HC-Other), low confidence and non-significant identifications of a non-model peptide (LC-Other), and spectra with no peptide identification with E-value less than 1 (Unknown). We use a 10−4 X!Tandem E-value threshold to delineate high-confidence identifications from other peptide identifications. Approximately half of the model peptides' high confidence identifications are selected, at random, for training (HC-Train), while the remainder are reserved for testing (HC-Test).

E-value computation

While the E-values of high-scoring peptide identifications of model peptides are easy to extract from Mascot and X!Tandem search results, E-values from low-scoring and non-significant scores are more difficult to obtain, as the search engines output only the top few best scoring peptides for each spectrum. To obtain E-values with respect to the model peptides of weak identifications, we constructed a special decoy protein sequence database, following (Elias et al., 2005). This sequence database consists of reversed protein sequences of the IPI-Human (Kersey et al., 2004) protein sequence database, plus (forward) protein sequences that contain the model peptides. The size of this decoy database is consistent with IPI-Human, and high-confidence identifications by X!Tandem and Mascot have similar E-values as for a IPI-Human search (data not shown). We also modified the X!Tandem source code to output all peptide identifications with hyperscores within 80% of the best scoring peptide. Lastly, we increased the precursor mass tolerance parameter to 4.5 (6.5) Da for spectra from charge 2 (3) peptide spectra datasets. Using these techniques, we are able to obtain valid E-value estimates for many spectra with respect to the model peptides, even when the identifications are very weak or missing from our original re-search results. We use these E-value estimates in the Comparative Performance section below.

MS Search match factor computation

NIST's MS Search (Mallard et al., 2005) match factors were computed using the MS Search search engine DLL via a Python module written to enable convenient command-line scripting. We construct a reference library for each model peptide, containing the (single) consensus spectra for the peptide from NIST's peptide fragmentation library (Stein et al., 2006). A command-line Python script is used to compute the match factor of all query spectra against the single peptide consensus spectrum reference libraries. Match factor parameters include precursor mass tolerance 2 Da, and fragment mass tolerance 0.8 Da. OMSSA and Q-tof scoring were not used. We use these match factor values in the Comparative Performance section below.

Spectrum normalization

While the fragmentation spectra of peptides subject to collisionally induced dissociation are widely believed to be reproducible under a variety of experimental and instrumental conditions, this reproducibility is difficult to observe without considerable normalization of each spectrum. For a spectrum represented by a list of peaks, positive real-valued (m/z,int) pairs, ordered by m/z, the techniques described in Stein (1994), Stein and Scott (1994), Yates et al. (1998), Wan et al. (2006), Craig et al. (2006), Frewen et al. (2006), Lam et al. (2006, 2007), and Stein et al. (2006) include intensity normalization relative to the base peak or rank, m/z binning and blurring, transformations such as the square root or logarithm of peak intensity, and elimination of insignificant ions by intensity or ranking. There seems little consensus, to date, on spectrum normalization techniques for spectral matching, although the work of Wolski et al. (2005) provides some basis for evaluating the many possibilities.

We have tested a number of normalization procedures and have selected an approach that seems to give good results in our experiments. We do not claim a comprehensive or exhaustive examination of the possibilities, but we plan to revisit these issues in future work. We have restricted ourselves to spectrum normalization techniques that can be carried out without knowledge of the model peptide or global properties of the training spectra, with the hope that our techniques might be useful in other settings.

Intensity normalization

The absolute value of peak intensities varies significantly between mass spectra of the same peptide, due to peptide abundance variation and different instrument technologies. We normalize the intensity of each peak in the peak list by the 3rd most intense peak. The normalized intensity of the 1st and 2nd peaks are reset to 100%. We use the 3rd most intense peak, rather than the base peak, to avoid creating spurious variation due to a non-reproducible base peak (often the precursor ion).

Peak selection

To ensure that only the most informative peaks are used in HMMatch training, we keep only the top 10 peaks with normalized intensity at least 1%. The selection of a small number of peaks ensures that the significant variation in the number of small, insignificant, “grass” peaks in each spectrum is eliminated. The resulting fixed length peak list ensures that HMMatch scores for different spectra are consistent in magnitude. We have found that this aggressive peak selection does not compromise HMMatch's performance.

Intensity transformation

Normalized intensities are transformed using the base 10 logarithm, which helps to moderate the larger variance observed in more intense peaks. After transformation, retained normalized intensity values range from 0 to 2.

Spectrum discretization

Normalized spectra are discretized for use with the discrete-valued HMMatch hidden Markov model, described next.

Normalized peak intensities are discretized with respect to 4 equal-sized bins between the minimum and maximum normalized intensity values. For the spectrum normalization described above, normalized intensities range from [0, 2], resulting in normalized intensity bins [0.0, 0.5], (0.5, 1.0], (1.0, 1.5], and (1.5, 2.0]. We denote these intensity bins ℐ.

Each m/z value in the peak list is transformed from a positive real-valued number to a symbol describing a region of the m/z axis. Let M = (m1, …, mk) be an ordered sequence of the theoretical m/z values of expected or commonly observed ions of a peptide and ε be a suitable mass tolerance for matching observed peaks with the elements of M. We add the values m0 = −ε and mk+1 = +∞ to M for notational convenience. The m/z axis is then partitioned into 3k + 1 regions: RjL=[mj−ɛ,mj] for j = 1, …, k, RjH=(mj,mj+ɛ] for j = 1, …, k, and Rj,j+1 = (mj + ɛ, mj+1 − ɛ) for j = 0, …, k. Figure 2 shows these regions on the m/z axis. We use ε = 0.5 Da throughout. We denote the set of all such regions ℛ.

FIG. 2.

FIG. 2

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Discretization of m/z axis into regions. Valid m/z region emissions, for each non-silent HMM hidden state, also shown.

Hidden Markov model

The HMMatch hidden Markov model for peptide fragmentation spectra is based on the protein family hidden Markov model used by the Pfam database, and is shown in Figure 3.

FIG. 3.

FIG. 3

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The HMMatch hidden Markov model for peptide fragmentation mass spectra.

Hidden states

The hidden Markov model represents each of a peptide's commonly observed or expected ions, with m/z value mJ ∈ M, by a hidden state Sj. We use states representing singly charged b and y ions for 2+ peptides, but find that adding some doubly charged b and y ions and dropping rarely observed ions improves predictive performance for 3+ peptides. These hidden states, represented by squares in Figure 3, are ordered as in M. Insertion states Ij,j+1 (diamonds in Fig. 3) represent additional, unexpected, or unmodeled ions between expected m/z values mj, mj+1 ∈ M. The absence of expected m/z values is modeled by silent delete states, represented by circles in Figure 3. The silent begin and end states conceptually represent the minimum and maximum m/z values, 0 (m0 ∈ M) and +∞ (mk+1 ∈ M), respectively.

Emission probabilities

Each non-silent state emits a discrete valued m/z value-intensity pair (m/z, int) from ℛ × ℐ. We model the m/z value and intensity emission probabilities as independent, so that Ps(m/z, int) = Ps(m/z)Ps(int) for each non-silent state S. Each ion state Sj, for mj ∈ M, emits peaks such that m/z∈{RjL,RjH}. Similarly, each insertion state Ij,j+1 between states Sj and Sj+1, for mj, mj+1 ∈ M emits peaks such that m/z∈{RjH,Rj,j+1,Rj+1L}. Figure 2 shows the valid m/z regions that may be output by each non-silent state.

Transition probabilities

Non-zero transition probabilities are shown as directed edges in Figure 3.

Model training

We train the hidden Markov model using the Baum-Welch (Baum, 1972) algorithm, as implemented in the LAMP_HMM (DeMenthon and Vuilleumier, 2003), applied to discretized, normalized spectra from HC-Train. Intensity emission probabilities for expected m/z value and insertion states are initialized according to peaks from the training spectra in the m/z regions RjL∪RjH and Rj,j+1, respectively, with small pseudo-counts added for unobserved intensity values. Non-zero m/z value emission probabilities are initialized with probabilities proportional to the size of the m/z value region they represent. All transition probabilities from state S in Figure 3 are initialized to 1/degree (S).

HMMatch scores

Once trained, the HMMatch hidden Markov model is used to assess the extent that unknown spectra are consistent with the training spectra. Unknown spectra are normalized and discretized, as above, and the Viterbi algorithm is used to compute the probability of the most likely path through the hidden Markov model consistent with the unknown spectrum. We report the Viterbi distance: −log10(p), where p is the probability of the Viterbi path, as the HMMatch score.

Statistical significance of HMMatch scores

While we observe good separation between the HMMatch scores of spectra from the model peptide of interest and spectra from other peptides, the magnitude of the Viterbi distance changes with the training set, normalization procedure, model parameters, and model peptide, which makes comparison of HMMatch scores impossible. To normalize the Viterbi distance scores, we construct a statistical significance model by comparison to the distribution of HMMatch scores of “random” spectra. Once determined, for each HMMatch model, this null-model is used to transform HMMatch scores to p-values.

Random spectra

Generating random spectra for statistical significance models must be done with care. Naively generated random spectra are so unlike peptide MS/MS spectra that even poor HMMatch scores appear statistically significant. We require that the random spectra look enough like true peptide fragmentation spectra that poor HMMatch scores are not statistically significant, while ensuring that good HMMatch scores are still unlikely to be observed.

We extract the intensity and m/z value properties of discretized, normalized training spectra independently. Each peak list contains 10 peaks, 3 of which have normalized intensity 100%, by construction. The discrete intensities of the remaining peaks in each training spectrum are tabulated to construct an empirical probability density for peak intensity.

The probability density of the discretized training spectra m/z values is determined from the m/z values of the top N most intense peaks in each spectrum, for some N ≥ 10. As with the intensities, the m/z regions are tabulated to construct an empirical probability density. For small N, the probability density strongly favors m/z regions corresponding to very abundant ions, while for large N, the probability density weights m/z regions according to their size. If the probability density favors abundant ions from the training set too heavily, even for large N, we add pseudo-counts to the m/z regions. As the pseudo-counts increase, the m/z regions are sampled according to their size and all information about the m/z values of the abundant ions in the training spectra is lost.

Once the empirical distribution of m/z values and intensities from the training spectra are established, random spectra are generated by choosing 10 peaks according to the discrete intensity and m/z region probability densities. Three peaks are assigned intensity 100%, with the rest drawing their intensity independently from the empirical intensity distribution. Each of the peaks draws their m/z value from the empirical m/z value distribution independently of each other, and their intensity values.

The parameters of this random spectrum generation technique serve to favor, or discount, the selection of abundant training spectra m/z values. We initially set the pseudo-counts to zero and adjust the parameter N until the distribution of the HMMatch scores of random spectra matches the distribution of the HMMatch scores of HC-Other spectra. Pseudo-counts are used if no value of N is sufficient.

Score distribution

We find that the normal distribution, with appropriately chosen mean and variance, is a good fit to the empirical shape of the HMMatch scores from random spectra. The mean and variance of HMMatch scores of 1000 random spectra are easily computed, and are used to transform HMMatch scores to normal distribution z-scores to estimate p-values.

3. Results And Discussion

Peptide and spectra dataset selection

We select 8 peptide/charge state combinations from the relational database of identified spectra. The peptides, their m/z value, and the number of spectra in each category is shown in Table 1. Peptides were selected from those with the most high-confidence identifications in the relational database.

Table 1. Model Peptides and Spectral Dataset Sizes.

Peptide m/z z HC-train HC-test LC HC-other LC-other Unknown
DFLAGGVAAAISK 610.34 2 36 23 59 48 879 22,481
DFLAGGIAAAISK 617.35 2 33 36 14 113 804 5001
DLATVYVDVLK 618.35 2 36 40 61 159 772 4615
AVMDDFAAFVEK 671.82 2 110 82 143 205 820 5543
LNDLEDALQQAK 679.35 2 28 28 45 169 741 7591
AVM*DDFAAFVEK 679.82 2 56 50 52 116 723 7585
SHCIAEVENDEMPADLPSLAADFVESK 992.12 3 27 25 140 162 1317 6208
SHCIAEVENDEM*PADLPSLAADFVESK 997.45 3 40 40 125 128 1192 6074
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Cysteines alkylated with iodoacetamide.

*

Oxidized methionine.

Training and performance evaluation

After training the HMMatch models using the Baum-Welch algorithm, we evaluate the performance of each model on each spectrum class. Given the relatively small number of training examples for each peptide, we must be sensitive to the possibility of over-fitting the model. We carefully considered this issue in the hidden Markov model design, particularly in the spectrum discretization and emission probability independence assumption, and in our initialization of intensity emission probability distributions. Plotting the distribution of HMMatch scores (Fig. 4) for the HC-Train and HC-Test spectra shows that there is no evidence of over-fitting. In each case, the distribution of training scores match the distribution of testing scores. Furthermore, we see that there is excellent separation between the HMMatch scores of the spectra with high-confidence identifications to the model peptide (HC-Test) compared with that of other peptides (HC-Other). This implies that HMMatch has similar specificity as X!Tandem with respect to spectra with high-confidence identifications.

FIG. 4.

FIG. 4

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HMMatch scores for spectra with high-confidence identifications (X!Tandem E-value < 10−4). Star, high-confidence train (HC-Train); circle, high-confidence test (HC-Test); diamond, high-confidence other (HC-Other).

The time to train each HMMatch model varies depending on the number of states, the number of training spectra, and the number of Baum-Welch iterations required for convergence. Table 2 shows these statistics for each model. Most of the HMMatch models were trained in less than 10 seconds, with the longest training time less than one minute.

Table 2. Time for HMMatch Training.

Peptide Time (sec) Iterations States Spectra
DFLAGGVAAAISK 6 2 81 36
DFLAGGIAAAISK 5 2 81 33
DLATVYVDVLK 4 2 69 36
AVMDDFAAFVEK 16 2 75 110
LNDLEDALQQAK 3 2 75 28
AVM*DDFAAFVEK 8 2 75 56
SHCIAEVENDEMPADLPSLAADFVESK 16 2 120 27
SHCIAEVENDEM*PADLPSLAADFVESK 44 2 141 40
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Computation of the HMMatch score is also quite fast. Table 3 shows the time to compute HMMatch scores for each peptide's spectra. Viterbi distance evaluation time depends primarily on the number of states in the hidden Markov model. While more time-consuming than the sequence and dot-product based search engines, the time to compute HMMatch scores is not prohibitive, even for very large spectral datasets, despite the use of a generic hidden Markov model codebase that does not take advantage of our model's sparsity.

Table 3. Time to Compute HMMatch Scores.

Peptide Time (sec) Spectra Spectra/s
DFLAGGVAAAISK 586 23,526 36.47
DFLAGGIAAAISK 143 6001 41.11
DLATVYVDVLK 90 5683 62.46
AVMDDFAAFVEK 137 6903 48.96
LNDLEDALQQAK 175 8602 48.87
AVM*DDFAAFVEK 172 8582 48.49
SHCIAEVENDEMPADLPSLAADFVESK 629 7879 13.33
SHCIAEVENDEM*PADLPSLAADFVESK 919 7599 8.45
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We evaluate the sensitivity of HMMatch by plotting the distribution of HMMatch score p-values for spectra with weak or no identifications (Fig. 5). A large proportion of the spectra with weak identifications to the model peptide (LC) have very small p-values, demonstrating higher confidence than X!Tandem for these peptides. A few weak identifications to other peptides (LC-Other) have quite significant HMMatch scores which suggests the weak X!Tandem identifications are incorrect. Lastly, a considerable fraction of the spectra with no identification with E-value ≤ 1 (Unknown) have significant HMMatch scores. We have manually examined a number of these cases and discuss them below. In each case we find that spectra with significant HMMatch scores are an excellent match to high confidence spectra from the model peptide.

FIG. 5.

FIG. 5

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HMMatch p-values of low-confidence and unknown spectra (X!Tandem E-value > 10−4). Star, low-confidence (LC); circle, low-confidence other (LC-Other); diamond, unknown.

We also test the performance of HMMatch as the number of training spectra is reduced. We construct HMMatch models for the peptides LNDLEDALQQAK and DFLAGGIAAAISK using training sets consisting of 5, 10, 20, and 40 randomly selected HC spectra. For peptide LNDLEDALQQAK, the distribution of HMMatch scores for HC-Test and HC-Other overlap a little for 5 training spectra, but are completely separated for 10, 20, and 40 training spectra. For peptide DFLAGGIAAAISK, the HC-Test and HC-Other scores are well separated for all training set sizes from 5 to 40. As with all machine-learning techniques, HMMatch training is more effective as the number of training examples increases, however, these experiments demonstrate that good performance is possible even with a relatively small number of training spectra per peptide.

Comparative performance

In the absence of a sufficiently rich dataset with known correct peptide identifications to establish performance in terms of sensitivity and specificity, we compare HMMatch to sequence database search engines X!Tandem and Mascot, and spectral matching search engine MS Search from NIST with consensus spectra from the NIST library of peptide fragmentation spectra. We use precision-recall statistics to compare the tools' peptide identification scores, and manually examine some of the cases where HMMatch is able to confidently identify spectra the other tools cannot.

Comparative precision-recall curves

Each tool orders the spectra in each dataset according to some spectrum-peptide match score. For X!Tandem and Mascot, this is the E-value, for NIST's MS Search this is the match factor, and for HMMatch this is the HMMatch score described above. Given a reference labeling as positive or negative with respect to the model peptide, we can evaluate, with respect to any spectrum-peptide match score threshold, the extent to which the partition of the spectra using this threshold is consistent with the reference labels. The spectrum-peptide match score and threshold and the reference labels partition the spectra into true-positive (TP), false-positive (FP), false-negative (FN), and true-negative (TN) sets. Precision, then, is defined as |TP|/(|TP|+ |FP|), while recall is defined as |TP|/(|TP|+ |FN|). Perfect correspondence with the reference labels, for a particular match score and threshold, results in 100% precision and 100% recall. The precision-recall curve, which plots the precision-recall statistics for all thresholds, captures the extent to which the ranking of spectra by some match score is consistent with the reference partition of the spectra into positive and negative examples. Perfect correspondence with the reference labels results in a square precision-recall curve.

We compare each tool to the others by using each tool and some spectrum-peptide score threshold, in turn, to construct synthetic reference labels. As such, these precision-recall statistics and curves do not represent true measures of sensitivity and specificity, instead they capture the extent that each tool's spectrum-peptide match score ranks the spectra in an order that is consistent with the synthetic reference. We show a complete set of precision-recall curves for the synthetic reference based on X!Tandem E-value and significance threshold 0.01 in Figure 6. To summarize the comparative behavior more comprehensively, we show the % recall at 99% precision for each tool averaged across the eight datasets, with respect to synthetic reference labels derived from each tool's spectrum-peptide match score in Table 4.

FIG. 6.

FIG. 6

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Precision-recall curves for Mascot (dash-dotted), MS Search (dotted), and HMMatch (solid) for synthetic reference labels defined by X!Tandem E-value threshold 0.01. Training spectra and spectra with no X!Tandem E-value for the model peptide are excluded.

Table 4. Average Percentage Recall at 99% Precision for Eight Model-Peptide Spectrum Datasets with Respect to Various Synthetic Reference Labels.
Reference Threshold Positive/Negative X!Tandem Mascot MS search HMM
X!Tandem (E-value) 0.1 93/12 — 59% 54% 53%
0.05 90/15 — 56% 54% 54%
0.01 79/26 — 48% 37% 43%
0.001 61/44 — 38% 14% 32%
Mascot (E-value) 0.1 38/67 38% — 33% 40%
0.05 34/71 42% — 35% 34%
0.01 26/79 45% — 26% 33%
MS search (match factor) 0.8 52/63 46% 61% — 60%
0.9 62/53 36% 55% — 49%
0.95 72/43 32% 42% — 46%
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Training spectra and spectra with no reference score excluded.

The precision-recall curves and statistics show only a moderately good correspondence between the ranking imposed by any pair of tools. From Table 4 we see that for high precision identification, the HMMatch score is more like X!Tandem and Mascot than MS Search, particularly for the smaller E-value synthetic label thresholds. For spectral matching synthetic labels from MS Search, HMMatch shows a similar degree of correspondence as Mascot, with X!Tandem showing quite a bit less. The precision-recall curves in Figure 6 show there is no tool that is uniformly more consistent with X!Tandem than the others. For four of the eight datasets, the HMMatch score recalls the most spectra at 100% agreement with X!Tandem, Mascot and MS Search recall the most for two each of the remaining datasets. Peptide SHCIAEVENDEMPADLPSLAADFVESK shows considerable disagreement between the search engine tools and the spectral matching based tools, with strong agreement between the spectral matching based tools. On the other hand, peptide AVM*DDFAAFVEK shows considerable difference between the spectra matching based tools, as compared to X!Tandem. On the strength of the precision-recall curves and statistics, we can conclude that HMMatch shows a similar level of concordance with the other tools as they do to each other. We can also observe, again, that HMMatch does not seem to be overtrained or biased towards the X!Tandem identifications, despite the use of these identifications in HMMatch training.

Comparative case-study spectra

Our datasets contain many spectra with highly significant HMMatch scores but with poor scores from X!Tandem and Mascot. HMMatch confidently identifies, with HMMatch score p-value < 10−5, 3537 spectra with both Mascot and X!Tandem E-values greater than 0.05. By way of comparison, NIST's MS Search confidently identifies, with match factor threshold 0.9, only 673 spectra with both Mascot and X!Tandem E-values greater than 0.05. We examined a number of these spectra manually to determine whether or not HMMatch was likely correct and to try to explain the poor scores from other tools.

A considerable proportion (350) of the spectra were correctly identified by HMMatch and misidentified by the search engines due to peptide candidate selection issues. We observed a large number of misidentified spectra with precursor molecular weight outside of the allowed search engine precursor tolerance of 2 Da. The spectral matching techniques also use a precursor tolerance of 2 Da, but it is applied to the m/z value of the precursor. When the search engine parameter is increased to 4 Da (6 Da) for charge 2 (charge 3) peptides, these spectra are usually confidently identified as the corresponding model-peptide by X!Tandem and Mascot. So, while these spectra are confidently identified by HMMatch, they are not a good demonstration of the strength of HMMatch scores as compared to the peptide sequence based scoring. However, it does suggest that the usual 2 Da precursor tolerance used for sequence searching is perhaps too aggressive. Nevertheless, increasing the precursor tolerance increases Mascot and X!Tandem E-values by a similar factor, further reducing their sensitivity.

Another class of spectra correctly assigned by HMMatch, but with poor X!Tandem and Mascot scores, are noisy spectra with many extraneous peaks, as in the fragmentation spectrum of peptide DLATVYVDVLK in Figure 7a. This spectrum, confidently identified by HMMatch with p-value 7.341 × 10−12 has Mascot E-value 1.07 and X!Tandem E-value 0.0013. While X!Tandem's E-value is less than typical significance thresholds, the E-value does not indicate the same degree of confidence as HMMatch. The sequence database search engines have trouble with these spectra as the extra peaks tend to match fragment ions whether or not the correct peptide is being evaluated. The MS Search spectral matching search engine computes a match factor of 0.844 for this spectrum-peptide combination, well below match factors for other high-confidence peptide identifications. The dot-product based match factor is affected by noisy spectra, as documented by Mallard et al. (2005). We believe that the aggressive peak selection strategy used for spectrum normalization makes the HMMatch robust with respect to the presence of extraneous noise peaks, without compromising identification performance.

FIG. 7.

FIG. 7

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Case-study fragmentation spectra of peptide DLATVYVDVLK. (Top) Mascot E-value, 1.07; X!Tandem E-value, 0.0013; MS Search match factor, 0.844; HMMatch p-value, 7.341 × 10−12. (Bottom) Mascot E-value, 5.7; X!Tandem E-value, 0.16; MS Search match factor, 0.994; HMMatch p-value, 1.738 × 10−12.

The fragmentation spectrum, also of peptide DLATVYVDVLK, in Figure 7b represents another interesting failed identification by Mascot and X!Tandem that is correctly identified by HMMatch. The HMMatch score has significance 1.738 × 10−12, while MS Search computes a match factor of 0.994. Mascot, on the other hand, computes an E-value of 5.7 and X!Tandem an E-value of 0.16, neither of which is statistically significant. Close examination of the search engine results revealed that the spectrum received poor scores due to the fragment ion mass tolerance parameter. The X!Tandem and Mascot searches were conducted using a fragment ion match tolerance of 0.4 Da, a relatively conservative fragment tolerance appropriate for the ion trap spectra that makes up the majority of publicly available MS/MS spectra. However, for the spectrum of Figure 7b, b4 and y3 ions, amongst others, were observed more than 0.4 Da from their theoretical value, which was sufficient to drive down the search engine scores. Increasing the fragment tolerance increases Mascot and X!Tandem E-values, further reducing their sensitivity. HMMatch uses a fragment tolerance of 0.5 Da, and matched b4, but didn't even use the peak that matched y3 as it was outside of the 10 most intense peaks. The MS Search match factor was computed using the default bin-size of 0.8 Da, so the measurement error apparent in these fragment ions did not affect its score. We believe that the use of peak intensity by HMMatch makes it possible to use a larger fragment ion tolerance and more aggressive peak selection, without sacrificing identification performance.

Model extrapolation

Traditional spectral matching, which uses dot-product based similarity scores, essentially treats the reference spectra as bitmapped images with no semantic content. The use of artificial consensus spectra, which implicitly encodes semantics by retaining only frequently observed or expected peaks and permits asymmetric variants of the dot-product based similarity measures, is the approach adopted by NIST for its GC/MS (Stein and Scott, 1994) and peptide fragmentation spectral libraries, as well as the MS Search spectral matching search engine.

The construction of a probability model, such as HMMatch, to abstract and semantically summarize the behavior of a set of peptide fragmentation spectra makes it possible to extrapolate the model to spectra from other, related, peptides. Two peptides that differ by a single amino-acid or by the addition or removal of a post-translational modification will, in many cases, have similar normalized intensities, once an appropriate offset is applied to some of the ions. For example, the peptides DFLAGGVAAAISK and DFLAGGIAAAISK differ by a Val to Ile substitution, a mass shift of +14.02 Da, which affects the m/z value of all the fragment ions that contain the changed residue, including b7, …, b12 and y7, …, y12, and of course the precursor. Other than this mass shift, however, the peptide fragmentation spectra of these peptides are very similar. Figure 8 shows the spectral box-plot of HC-Train spectra, defined as for Figure 1, for each of these peptides. This figure, which plots the distribution of intensities in each of the discrete m/z regions of ℛ, abstracts away the shift introduced by the amino-acid substitution and makes the similarity apparent.

FIG. 8.

FIG. 8

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Normalized intensity boxplots for peaks in each m/z value region from HC-Train spectra of DFLAGGVAAAISK and DFLAGGIAAAISK.

Given the semantic abstraction of our trained HMMatch model, it is straightforward to compute the HMMatch scores of spectra of one peptide using the HMMatch model of the other. Our set of spectra includes three such pairs: DFLAGGVAAAISK and DFLAGGIAAAISK, AVMDDFAAFVEK and AVM*DDFAAFVEK; and SHCIAEVENDEMPADLPSLAADFVESK and SHCIAEVENDEM*PADLPSLAADFVESK. The last two pairs differ by an oxidized methionine (+15.99), indicated by *, while the first pair has an amino-acid substitution, as already described. For each peptide, we compute the p-value of the HMMatch score of HC-Test spectra using both the original HMMatch model and the extrapolated model of its twin. Figure 9 plots the original p-value vs the extrapolated p-value for each of the six paired peptides. The line y = x is added to provide a visual aid for estimating the number of HC-Test spectra with increased or decreased significance.

FIG. 9.

FIG. 9

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Comparison of p-values of HC-Test spectra scored with the peptide's HMMatch model and the extrapolated HMMatch model of its related “twin” peptide.

We see that while the p-values are somewhat scattered, most spectra with statistically significant HMMatch scores computed using the correct model are still statistically significant with respect to the extrapolated twin peptide's HMMatch model. In fact, a good number of the HC-Test spectra are more significant (lie above the y = x line) when scored using the twin's model. The success of this model extrapolation suggests that HMMatch models may be created and used to identify peptides from a specific isoform or modification, even when their spectra have not previously been identified by other tools. Similarly, it may be possible to train a single HMMatch model using fragmentation spectra from two related peptides, useful when there are too few high-confidence training spectra for each individual peptide.

While many related peptides have similar (up to mass shift) spectra, this is clearly not true for all amino-acid substitutions (in particular, the insertion of a basic residue) and post-translational modifications. While we cannot necessarily predict which related peptides will have similar spectra, the statistical significance computation ensures that we do not accept extrapolated model assignments when they are unlikely to be correct.

A larger problem for HMMatch is the linear, ordered, structure of the expected ion hidden states. If the masses of expected or common fragments ions after the mass shift is applied are no longer correctly ordered, then it is unclear how we should adjust the hidden Markov model's transition probabilities to compensate. For each of the peptide pairs above, the mass shift is small enough that this is not an issue.

4. Conclusion

In this paper, we have demonstrated a novel approach, HMMatch, to peptide identification, using a hidden Markov model to summarize the statistical variation and consensus in a peptide's fragmentation spectra and applying this model to the identification of unassigned spectra. Our results indicate that HMMatch has good specificity and superior sensitivity, compared to sequence database search engines such as X!Tandem. The HMMatch design achieves good results from relatively few training spectra, is fast to train, and can evaluate many spectra per second. A statistical significance model permits HMMatch scores to be compared with each other (and with other peptide identification tools) on a unified scale. HMMatch shows a similar degree of concordance with X!Tandem, Mascot, and NIST's MS Search, as they do with each other, suggesting that each tool can assign peptides to spectra that the others miss. Finally, we have shown that it is possible to extrapolate HMMatch models beyond a single peptide's training spectra to the spectra of related peptides, expanding the application of spectral matching techniques beyond the set of peptides previously observed.

We believe that as the popularity of protein characterization by tandem mass spectrometry grows and the public repositories of peptide fragmentation spectra to increase in size, covering a larger proportion of more organisms' proteomes with more examples of mass spectra from a variety of instruments, the hidden Markov model approach to spectral matching will become increasingly useful in the analysis of peptide fragmentation spectra.

Acknowledgments

We are grateful for the open source search engine X!Tandem and the public release of LC-MS/MS datasets via the PeptideAtlas and HUPO PPP projects, without which this work would not have been been possible. This work was supported by NIH/NCI grant CA126189 and USDA cooperative agreement 5812757342.

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