Improved peptide sequencing using isotope information inherent in tandem mass spectra

Transcription

1 RAPID COMMUNICATIONS IN MASS SPECTROMETRY Rapid Commun. Mass Spectrom. 2003; 17: Published online in Wiley InterScience ( DOI: /rcm.1119 Improved peptide sequencing using isotope information inherent in tandem mass spectra William R. Cannon 1 * and Kenneth D. Jarman 2 1 Computational Biosciences, Pacific Northwest National Laboratory, Richland, WA 99352, USA 2 Applied Mathematics, Pacific Northwest National Laboratory, Richland, WA 99352, USA Received 26 September 2002; Revised 2 June 2003; Accepted 2 June 2003 We demonstrate here the use of natural isotopic labels in peptides to aid in the identification of peptides with a de novo algorithm. Using data from ion trap tandem mass spectrometric (MS/MS) analysis of 102 tryptic peptides, we have analyzed multiple series of peaks within LCQ MS/MS spectra that spell peptide sequences. Isotopic peaks from naturally abundant isotopes are particularly prominent even after peak centroiding on y- and b-series ions and lead to increased confidence in the identification of the precursor peptides. Sequence analysis of the MS/MS data is accomplished by finding sequences and subsequences in a hierarchical manner within the spectra. Copyright # 2003 John Wiley & Sons, Ltd. The identification of peptides derived from complex mixtures of proteins is a prerequisite for several high-throughput proteomics technologies. 1 4 Typically, protein mixtures are digested with trypsin and the resulting peptides are sequenced using tandem mass spectrometry (MS/MS). The sequence information provided by MS/MS analysis ideally consists of the sequential residue masses, or more precisely the mass-to-charge ratios (m/z, in units of Th), of the peptide as it fragments along the peptide backbone. Recent developments in extending peptide-sequencing technologies include chemically and physically labeling the peptides so that either the N-terminal or C-terminal fragments resulting from collision-induced dissociation (CID) can be differentiated by m/z shifts of peaks in the spectrum. Chemical approaches have included derivatization of both the N- and C-terminal groups. 5 7 The prominent b- or y-ion series can then be more easily discerned as long as the spectrum of the unmodified or differentially modified peptide is also obtained. In one approach, carboxylic acid groups of glutamic acid, aspartic acid and the C-terminal carboxylic acid are methyl-esterified using either -OCH 3 or -OCD 3. 5 When the spectrum for a singly charged peptide that has been modified with the methyl ester only at the C-terminal carboxylic acid is compared with the spectrum of the analogous peptide with the deuterated methyl ester, the y-ion series of the latter appears shifted by 3 Th. In a second approach, two populations of peptides are obtained and one population is differentially modified with O-methylisourea on the C-terminal lysine. 7 When the spectra of the two populations are compared, y-ion peaks in the derivatized peptides then show up as shifted by the mass difference of the functional groups (42 Da for singly charged *Correspondence to: W. R. Cannon, Computational Biosciences, Pacific Northwest National Laboratory, Richland, WA 99352, USA William.Cannon@pnl.gov Contract/grant sponsor: The Office of Biological and Environmental Research in the US Department of Energy; contract/ grant number: 41966A. peptides). Although chemically labeling peptides can have additional advantages for quantitating peptides, for the purpose of sequencing peptides it requires additional experimental and computational steps that may slow down highthroughput sequencing. Physically labeling peptides involves introducing heavy isotopes during processing steps With mass resolutions dropping to one in 2000 (Dm/z/m/z) in the mass range observed by ion trap mass detectors, it is reasonable to expect the detection of isotopic distributions of a given peptide. Typically, proteins are digested with trypsin in both normal solution and solution containing 18 O-labeled water. Physically labeling peptides with isotopes has the advantage for peptide sequencing in that there is no need to obtain a second spectrum in order to observe spectral shifts in the C-terminal ion series since the tryptic digestion can be done in a mixture of 50% 18 O-labeled water. As a result, two y-ion peaks appear in the spectrum for each C-terminal fragment, one peak containing the naturally abundant distribution of oxygen and the other containing the isotopic distribution for the 18 O- labeled oxygen. The presence of two adjacent peaks for each y-ion fragment in the spectrum allows y-ions to be readily discerned from peaks due to other fragments. The net result is that for complete spectra, the sequence of the peptide can be easily determined from the mass differences of consecutive y- ion peaks. In addition to using isotopic labels to aid in the sequencing of a peptide, isotopic labels are used in high-throughput proteomic studies for the quantitation of peptides. 4,16 18 In these studies cell populations are grown on normal media and on 15 N-enriched media. The cell lysate from the two populations are pooled and analyzed by mass spectrometry and relative abundances are determined based on abundances of the 14 N- and 15 N-enriched peptides. Identification of the peptides still relies on MS/MS analysis. In order to analyze both 14 N- and 15 N-enriched peptides by currently available programs, the data must be analyzed twice, the first Copyright # 2003 John Wiley & Sons, Ltd.

2 1794 W. R. Cannon and K. D. Jarman time using parameters for the natural 14 N-isotopic distribution and the second time using parameters for the 15 N- enriched isotopic distribution. In both of the cases of 18 O labeling for sequence analysis and 15 N labeling for quantitation purposes, many existing spectral analysis packages must be modified or run using a separate set of parameters in order to accommodate both isotope-labeled and unlabeled peptides. With regard to the mathematical and computational analysis of peptide sequences, many advances in de novo algorithms have been made in the last decade Graph theory approaches for analyzing spectra are a natural fit with the problem of how to use spectral peak information to build up the evidence for a particular peptide sequence. In the process of building up the evidence for the identification of a particular peptide, the peaks can be associated with known fragmentation ion types. Initial scoring functions were largely ad hoc and used empirically derived parameters for the various ion types that might arise from CID. Recently, work had been done on making the identification and scoring of the peptides more rigorous. 20 We demonstrate here the use of natural abundance isotopic labels that survive the centroiding process to aid in the identification of peptides with a de novo algorithm. The data are from electrospray ionization and ion trap MS/MS analysis of 102 tryptic peptides of charge þ2. Observation of isotopic peaks for the relatively abundant b- and y-series ions leads to an increased confidence in the identification of the precursor peptide. The computational analysis of the spectra consists of a novel method of exploiting the additional isotopic information that is not dependent on classification of product ions into the various ion-types such as y- or b-ions. In addition, since the computational method relies on finding a series of mass peaks within a spectrum that spells a peptide and does not rely on ion type classification per se, it is readily adaptable to proteomic studies in which peptides are differentially labeled with 14 N/ 15 N for quantitation purposes, 4,16 18 but MS/MS analysis of both 14 N- and 15 N-labeled peptides is required. That is, in addition to maximally exploiting isotope information that survives the peak centroiding process, the method also provides the ability to simultaneously analyze both 14 N- and 15 N-labeled peptides as well as other isotopically or chemically labeled peptides. METHODS Description of spectra Peptides were derived from Deinococcus radiodurans by tryptic digestion and mass analyzed in the laboratory of Richard Smith in the William R. Wiley Environmental Molecular Sciences Laboratory at the Pacific Northwest National Laboratory. 24 The CID spectra for the 102 peptides discussed herein were obtained using an electrospray ionization source feeding a Finnigan LCQ Classic ion trap. The spectra were all output in centroid mode. Independent identifications were performed using SEQUEST 25 in which tryptic peptides obtained from an organism-specific sequence database were examined. The range of SEQUEST scores for this data set ranged from 3.1 to 6.7. In addition, each peptide was reanalyzed multiple times on multiple days with the LCQ, and the mass of each peptide precursor ion was confirmed to within 1 part-per-million (ppm) by the use of an 11.5 Tesla ion-cyclotron resonance mass spectrometer and a 15% elution time tolerance. 26 Analysis of isotopic distributions Our approach to identifying the peptide fragment ion types and their isotopic distributions that are present in the mass spectral data follows that outlined by Dancik et al. 20 Let A be the set of amino acids and let m(a) represent the mass of the residue for each amino acid a 2 A. Let P represent a parent peptide, which consists of a string of N amino acids: P ¼ a 1...a N, here a i 2 A for i ¼ 1,..., N. Let P ij ¼ a i...a j be a partial peptide (1 i j N), and define the N-terminus and C-terminus partial peptides by P i ¼ P 1,i and P i ¼ P i,n, respectively, for (1 i N). The residue mass of a peptide P is: mp ð Þ ¼ XN i¼1 ma ð i Þ Likewise, the residue mass for the N-terminal or C- terminal peptide is defined as the sum of the respective residue masses: mp ð i Þ ¼ Xi ma ð k Þ and mp X N i ¼ k¼1 k¼i ma ð k Þ Now we can define ion types in terms of mass offsets from the peptide residue mass. Let d k be the mass offset associated with the kth ion type as shown in Table 1. The mass associated with the peptide fragment of ion type k is then given by either m(p i ) þ d k or m(p i ) þ d k for N-terminal and C-terminal peptides, respectively. Next, we want to automatically find the offsets d k due to peptide fragments that result from MS/MS fragmentation processes such as CID. We do this by examining spectra of 102 identified peptides. Let S ¼ {s 1,...s M } be a spectrum of M peaks that results from CID of the peptide P. Define the difference between a spectral peak and the residue mass of the peptide as d ij ¼ s j m(p i ). If a difference d ij corresponds to an ion type offset d k shown above, then it will be a common feature in the mass spectral data sets that we examine. A histogram of the differences d ij will indicate which value of d ij corresponds to ion type offset d k by a frequency of appearance that is above the background. De novo analysis of mass spectra Each peak in a mass spectrum is a node in a spectrum graph, and all nodes are labeled by the m/z value of the corresponding peak. The graph is ordered from lowest to highest based Table 1. Ion types in terms of mass offsets: d k is the mass offset from the peptide residue mass associated with the kth ion type. The mass associated with the peptide fragment of ion type k is then given by either m(p i ) þ d k or m(p i ) þ d k for N-terminal and C-terminal peptides, respectively k Ion type a b b-nh 3 b-h 2 O y y-nh 3 y-h 2 O Offset, d k

3 Peptide sequencing using isotope information in MS/MS spectra 1795 on the mass-to-charge (m/z) value of the node. Edges between nodes are allowed if the m/z difference between nodes is equal to the monoisotopic mass of an amino acid within a margin of error. After construction of the graph, all paths through the graph are found in a manner following a depth-first search. This process is rapid, because, unlike other implementations of spectrum graphs, 20,22 we do not expand the graph by adding additional nodes for each possible ion type that can be derived from peptide fragmentation. All nodes are initially colored white. The initial node used in finding the paths is the one with the lowest m/z value. This node and all nodes subsequently visited from this node are colored black. After a search starting with the initial node has been completed, the next white node is chosen and another search is initiated. This is done until all nodes have been visited. Since the graph is sorted by m/z values, searching from lowest to highest will spell out all N-terminal peptides (that is, a-, b- and c-type ions) as they appear, but will spell out C-terminal peptides (that is, x-, y-, and z-type ions) in reverse. Construction of a hierarchical sequence graph for scoring Next, a sequence graph is constructed in which each node is labeled with one of the sequences or strings found above. Edges are placed between nodes if a node s sequence or string label is a substring of another node s sequence label. In this manner, and as shown in Fig. 1, child-parent relationships between nodes are established in a sequence graph. From each parent node, all child nodes can be examined for scoring. An amino acid sequence A k of length l occurs randomly with probability p lþ1, as there are lþ1 peaks that make up A k. This amino acid sequence can appear either forward or backward in the spectrum, and the number of ways A k can appear in this spectrum is given by M. The number of ways A k can appear n times in the spectrum is M!/ (n!(m-n)!). The probability that A k appears exactly n times is then given by: M! PA k appears n times ¼ p lþ1 n 1 p lþ1 M n n! ðm nþ! Subsequences that appear in the sequence tree (Fig. 1) for A k are accounted for by conditioning the each subsequence on the immediate supersequence in the sequence tree. The overall score reflects the likelihood that the sequence A k would arise by chance alone. Further details on the scoring algorithm are presented elsewhere. 27 RESULTS Natural abundance isotope distributions in MS/MS spectra We have employed a computational model developed by Dancik et al. 20 to discover isotopic information contained within MS/MS spectra. The method was originally used to discover prominent ion types in MS/MS spectra regardless of the particular fragmentation process employed. As noted by Bartels, 19 the mass of each fragment is the sum of the residue masses and a mass offset characteristic of each ion type which implicitly includes a mass value for either the C- or N- terminus moiety. For example, b-series ions have a mass offset of 1 Da due to the presence of a hydrogen atom beyond what would be expected for the sum of the residue masses plus the N-terminal hydrogen atom. Likewise, for tryptic peptides, y-series ions have a mass offset of þ19 Da from what would be expected for the sum of the amino acid residues. The offset is due to a C-terminus mass of þ17 Da for the terminal hydroxyl group, an additional hydrogen on the N-terminus, and the ionizing proton on the lysine or arginine side chain. Figures 2(A) and 2(B) show scans of ion type offsets, from the N- and C-terminus, respectively, for 102 data sets from a Figure 1. An example sequence tree. Top nodes correspond to parent sequences and subnodes correspond to partial sequences of the parent node. Each sequence or subsequence corresponds to at least one path through the spectrum graph. Each parent node is connected to its children, forming a sequence hierarchy, which is used later for scoring. Each node contains information on all paths through the spectrum graph that is consistent with the node label or sequence.

4 1796 W. R. Cannon and K. D. Jarman Figure 2. Histogram of ion type offsets for 102 data sets from a Finnigan LCQ ion trap. (A) Abundance of mass shifts from the mass of N-terminal peptide fragments. The prominent peak at approximately þ1 u corresponds to the presence of b-series ions in the spectra. (B) Abundance of mass shifts from the mass of C-terminal peptide fragments. The prominent peak at þ19 u corresponds to the presence of y-series ions in the spectra. Finnigan LCQ ion trap that were collected on the same instrument. The residue mass itself would have an offset of zero in Figs. 2(A) and 2(B), respectively. As expected, no peak occurs at zero indicating that peptide fragments in the spectrum that correspond to unmodified residue masses of peptide fragments were not seen. The highest peak in the N- terminal scan shown in Fig. 2(A) appears at approximately 1 Da above the residue mass at zero and corresponds to the presence of b-series ions. In addition, peaks due to loss of water and ammonia from b-series ions can be seen at approximately 17 and 16 Da, respectively, and peaks at approximately 34 and 35 Da likely correspond to loss of combinations of water and ammonia. Likewise, in Fig. 2(B), the highest peak in the C-terminal scan occurs at 19 Da and corresponds to y-ions. Peaks near 1 and 2 m/z in Fig. 2(B) again correspond to loss of water and ammonia from the y- series ions. Figures 3(A) and 3(B) show the same scans, but now focused in around the regions due to the b- and y-ions, respectively. As can be seen, both of the major peaks in the scans show significant shoulders located 1 and 2 Da up from the major peak apices. The shoulder peaks indicate that naturally abundant isotopes can commonly be identified using the LCQ mass spectrometer. This is not unexpected since the contributions of isotopes to the relative abundance increase with increasing number of atoms in the molecular formula (and hence increasing mass). For an average peptide of 1200 Da and molecular formula C 53 H 97 N 15 O 16, 28 the monoisotopic peak is the most abundant, the first isotope peak is expected to be 65% as abundant as the monoisotopic peak, and the second isotopic peak is expected to be 24% as abundant as the monoisotopic peak. Next, we show how this additional information due to the naturally occurring isotopes can be exploited in analyzing the spectra. The spectrum for the first example is shown in Fig. 4. The full-length parent peptide is ALEALQSNPK, as determined independently by SEQUEST (Xcorr ¼ 3.57 with the second highest scoring peptide having a Xcorr ¼ 1.72 and a deltcn of 0.52), and by our own sequence comparison and database search of tryptic peptides from the organism-specific sequence database. Additional MS/MS spectra were collected on this peptide which resulted in SEQUEST Xcorr scores up to 4.1, confirming the identity of this peptide. Furthermore, the mass of the parent peptide was confirmed to be to within 1 ppm with an 11.5 Tesla ioncyclotron resonance mass spectrometer. 26 Figure 3. Magnification of histogram shown in Fig. 2 around (A) the b-series ions and (B) the y-series ions. The resolution of the spectra is high enough to observe mass shifts due to isotopes in both ion series. The observed isotope distribution matches the expected distribution for peptides with masses in the order of 1000 u.

5 Peptide sequencing using isotope information in MS/MS spectra 1797 Figure 4. MS/MS spectrum for the peptide ALEALQSNPK. (A) Major peaks identified. The mass differences between consecutive peaks in a series such as the y- or b-ion series correspond to the mass of amino acid residues. Isotopic resolution of ion series peaks, as demonstrated in Fig. 2, enables the peptide sequence to be discovered multiple times in the spectrum. (B) Close-up view of isotopic resolution of y 6 - and b 7 -ions and peaks due to loss of water and ammonia. The de novo algorithm determined that the sequence of this peptide is partially spelled out by five series of peaks, as shown in Table 2. The partial sequence EALQSN was obtained by both the y-series ions and a series of peaks that occur consistently at a value 1 Da greater than the y-series ions. For the most part, the intensities of peaks in the latter series are the same order of magnitude as that seen for peaks in the y-series ions. As one might expect, the natural isotopic distribution is modified by the peak centroiding process, so it is not straightforward to compare natural distributions with those that survive the centroiding process. However, we do expect to be in the same ball park the distributions should not differ by an order of magnitude (when an isotope peak is actually called out by the centroiding process). As expected, the relative peak heights shown in Fig. 4 and Table 2 are in approximate agreement with the expected isotopic distribution for the peptide. That is, as the number of elements in the peptide increases, the monoisotopic peak decreases in relative intensity and the higher mass peaks increase in relative abundance. For the full-length peptide ALEALQSNP with a nominal mass of 1070, the first isotopic peak above the monoisotopic peak is expected to be approximately 60% of

6 1798 W. R. Cannon and K. D. Jarman Table 2. ALEALQSNPK-associated peaks. The sequence EALQSNP and four subsequences of at least five peaks each are present in the spectrum shown in Fig. 4. Isotopic resolution of ion series peaks enables the observation and analysis of these multiple instances of sequences as related families, as shown in Fig. 1 Amino acid m/z Relative intensity Ion type E y8 A y7 L y6 Q y5 S y4 N y3 Peak y2 E y8 þ1 A y7 þ1 L y6 þ1 Q y5 þ1 S y4 þ1 N y3 þ1 Peak y2 þ1 P b9 N b8 S b7 Q b6 L b5 A b4 E b3 Peak b2 P b9 þ1 N b8 þ1 S b7 þ1 Q b6 þ1 L b5 þ1 Peak b4 þ1 N b S b Q b L b Peak b the abundance of the monoisotopic peak. The second isotopic peak above the monoisotopic peak would be expected to be approximately 20% of the abundance of the monoisotopic peak. In addition, an isotopically shifted series of peaks was obtained by the de novo algorithm adjacent to the b-series ion peaks. While the b-series peaks spell out the partial sequence EALQSNP, the isotopically shifted series spell out the shorter partial sequence LQSNP. Again, the magnitudes of the peak heights approximately reflect the natural abundance of the isotopes. The presence of b-series ions that have additionally undergone neutral loss of ammonia makes up a series of five peaks that spell out NSQL. Other shorter partial sequences that are consistent with the parent ALEALQSNPK were also obtained by the de novo algorithm but are not shown as they are more likely to arise by random chance alone and are less informative. Using a minimum sequence length of 2 in the sequence tree (Fig. 1), 79% of the peaks in Fig. 4 are accounted for. The spectrum for the second example is shown in Fig. 5. The full-length parent peptide is ANHWLAQGAQPTDTAR, as determined independently by SEQUEST (Xcorr ¼ 4.34, with the second highest scoring peptide having a score of 0.96 and a deltcn of 0.78), along with our own sequence comparison and database search of tryptic peptides from the organism-specific sequence database. Additionally, multiple MS/MS data were collected and confirmed for this peptide on different days that resulted in SEQUEST scores up to 5.1. Again, the mass of the parent peptide was confirmed to be to within 1 ppm using a 11.5 Tesla ion-cyclotron resonance mass spectrometer 26. As shown in Table 3, the subsequence WLAQGAQ of the parent was obtained by the de novo algorithm for the y-series ions, b-series ions and additional series of peaks occurring 1 Th above both the y-ion series and b-ion series peaks. Again, the relative peak heights of the isotopic fragments are in approximate agreement with the expected isotopic distribution for the peptide. For the full-length peptide ANHWLAQ- GAQPTDTAR with a nominal mass of 1736, the first isotope of the peptide is expected to be approximately 92% as abundant as the monoisotopic peptide. The second isotope of the peptide is expected to be approximately 47% of the abundance of the monoisotopic peptide. In the series of peaks that consists of the first heavy isotope of the y-ion series (yionþ1 Th), the peak corresponding to (y 10 þ 1 Th) is missing. The effect of this is to break the 7-mer peptide consisting of eight peaks into a 2-mer of three peaks and a 3-mer consisting of four peaks. This reduces the significance of this series of peaks somewhat, but a method of accounting for the fulllength 7-mer, or the observation of any incomplete sequence, is discussed below. In addition to the sequence WLAQGAQ, the partial sequence WLA corresponding to the b-series ions at 17 Th was also observed which corresponds to loss of ammonia from b-series ions. Again, other shorter partial sequences that are consistent with the parent were also found but are not shown, as they are more likely to arise by random chance alone and are less informative. Using a minimum sequence length of 2 in the sequence tree (Fig. 1), 77% of the peaks in Fig. 5 are accounted for. DISCUSSION As can be seen from the data in Fig. 2 the presence of isotopic shifts in the y- and b-ion series are nearly as common as the y- and b-ions themselves and much more common than the a-, c-, x-, and z-ions and ions due to loss of neutral fragments such as ammonia or water. This may seem odd in that it leads one to wonder why b-ions would have a different isotope distribution from those of (b-h 2 O) or (b-nh 3 ) ions. And in fact the isotopic distributions of the peptide fragments are the same, barring small differences due to loss of water or ammonia. However, centroided mass spectrometer output does not report actual isotope distributions, but rather distributions that have been filtered because of the centroiding process. It is likely that, since there is more information regarding b-ions than (b-h 2 O) or (b-nh 3 ) ions in the spectra before the centroiding is done, the centroiding process can detect and process these isotopic peaks of the b-ions better than isotopic peaks of the (b-h 2 O) or (b-nh 3 ) ions. That is, there is likely more information in the raw spectrum (profile mode) for b-ions than for (b-nh 3 ) or (b-h 2 O) ions simply because b-ions are more abundant.

7 Peptide sequencing using isotope information in MS/MS spectra 1799 Figure 5. MS/MS spectrum for the peptide ANHWLAQGAQPTDTAR. (A) Major peaks identified. Again, isotopic resolution of ion series peaks, as demonstrated in Fig. 2, enables the peptide sequence to be discovered multiple times in the spectrum. (B) Closeup view of isotopic resolution of y 6 -, b 5 - and b 6 -ions along with peaks due to neutral loss of water and ammonia from these same ions. As a result, and as demonstrated in Tables 2 and 3, peptide sequencing from MS/MS data using the de novo analysis method described herein can be obtained in favorable cases with high confidence without resorting to chemical derivatization or isotopic enrichment. Although the method requires further development for use with precursor ions that have charges of either þ1orþ3, the method may result in a significant advantage as proteomic research becomes high throughput, since chemical derivatization of peptides increases the number of steps in the peptide/protein identification pipeline and requires twice as many spectra to be recorded, analyzed and stored. Furthermore, with unit m/z resolution of mass detectors available and advanced analysis tools, the need to isotopically label the C-terminus of peptides with 18 O is alleviated; 18 O-labeling, however, does provide additional information regarding which peaks in the spectrum are due to N-terminal fragments and which are due to C-terminal fragments. This is important for manual interpretation of the data, but less so for automated interpretation such as de novo methods. The computational method, used to analyze the data, naturally takes advantage of the additional presence of isotope information by calculating the probability that the peptide sequence or subsequences would show up in the data purely by chance. Any over-represented sequences in the sequence hierarchy (Fig. 1) will then score much higher.

8 1800 W. R. Cannon and K. D. Jarman Table 3. ANHWLAQGAQPTDTAR-associated peaks. With the exception of a single missing peak due to the þ1 isotope at y 10, the subsequence WLAQGAQ occurs four times in the spectrum shown in Fig. 5 because of the isotopic resolution of peptide fragments. In addition, smaller subsequences such as WLA, shown here associated with neutral loss of ammonia from the b-ions, can occur multiple times in the spectrum Amino acid m/z Relative intensity Ion type W Y13 L y12 A y11 Q y10 G y9 A y8 Q y7 Peak y6 W Y13 þ1 L Y12 þ1 A Y11 þ1 Q (Missing) G Y9 þ1 A Y8 þ1 Q y7 þ1 Peak y6 þ1 Q b10 A b9 G b8 Q b7 A b6 L b5 W b4 Peak b3 Q b10 þ1 A b9 þ1 G b8 þ1 Q b7 þ1 A b6 þ1 L b5 þ1 W b4 þ1 Peak b3 þ1 A b L b W b Peak b It should be noted that the problem being addressed here is the peptide-sequencing problem and not peptide identification. The peptide-sequencing problem is to derive the sequence of a peptide from the spectrum alone. The peptide-identification problem is a significantly easier problem of identifying the peptide from a list of candidate peptides that best explains the MS/MS spectrum. In the former approach, no prior knowledge of peptide sequence information is used and all distances between pairs of peaks in the spectrum are examined to see if they match the mass of an amino acid. A significant problem for all current de novo sequencing methods is that up to 50% of MS/MS spectra do not contain sufficient information for a complete sequence to be found. 29 For this reason, peptide sequencing de novo is also more computationally intensive than peptide identification from a list of possible peptides. One prudent scenario for high-throughput proteomic pipelines would be to use peptide identification software as a first pass to reliably identify as many peptides as possible, and to use peptidesequencing algorithms to tackle problematic spectra from which a peptide could not reliably be identified. One advantage of peptide sequencing compared to peptide identification lies in the inherent flexibility of the former. Clearly, peptide sequencing has the ability to discover sequences that are not present in sequence databases used as input to peptide identification programs. Furthermore, since this method does not rely specifically on the association of a peak with a particular ion series but rather on the presence of peaks that spell a peptide sequence or its subsequence multiple times, it can be adapted to analyze spectra that are derived from peptides with post-translational modifications. In fact, neither the modification nor the mass shift due to the modification need be known, but can potentially be determined from the analysis, as done in the program SALSA. 30 One complication in the analysis of peptides with post-translational modifications is that the chemical moieties, such as phosphate groups, are labile and can fragment off resulting in mass shifts in ion series peaks. The hierarchical nature of the chemical sequence of the peptide, including post-translational modifications, should allow this analytical method to be easily adapted to the problem. The main disadvantage of this approach as implemented here is that there is more reliance on sequential peak information. That is, an individual peak cannot be accounted for by itself. Instead, each peak must be part of a sequence of peaks, although the sequence can be as small as two. However, this reliance on sequential peaks can be alleviated somewhat by inferring a ghost parent sequence from the common child of two intermediate sequences. That is, missing parents in the sequence tree shown in Fig. 1 will not be observed by searching paths in the spectrum graph but can be inferred from the existence of their children. Consider the parents ABCDE in Fig. 1. If this peptide is not actually observed in the spectrum graph due to a missing peak, its presence can be inferred from its immediate children, ABCD and BCDE. The problem then is to align peptides ABCD and BCDE in order to infer ABCDE. However, this alignment is done automatically in the sequence tree through the common child BCD. The net effect of this is to allow for the presence of a parent sequence even though consecutive ion series peaks were not found for the entire sequence. This may somewhat alleviate the problem of incomplete peak information in MS/ MS spectra. Further research is needed into the problem of sequencing or identifying a peptide using de novo methods when faced with incomplete data. Acknowledgements This research was funded by the Office of Biological and Environmental Research in the US Department of Energy under contract 41966A and performed in the William R. Wiley Environmental Molecular Sciences Laboratory at the Pacific Northwest National Laboratory. The EMSL is funded by the Office of Biological and Environmental Research in the US Department of Energy. PNNL is operated by Battelle for the US Department of Energy under contract DE-AC06-76RLO 1830.

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