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Molecular & Cellular Proteomics 3:625-659, 2004.
© 2004 by The American Society for Biochemistry and Molecular Biology, Inc.
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From the Discovery Proteomics and Small Molecule Research Center, Applied Biosystems, Framingham, MA 01701; ¶ Biocrates Life Sciences, 66 Innrain, Innsbruck, Austria; and || Department of Medical Genetics and Microbiology, University of Massachusetts Medical School, Worcester, MA 01655-0122
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSCRUDE RESULTSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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One simple statistic for determining the relative success of a proteomics experiment is to count the number of correctly identified peptides and proteins. Because identification of peptides by electrospray is dependent on appropriate automated selection of precursor ions, repetition of experiments has frequently been observed to result in a higher number of identifications. In the matrix-assisted laser desorption/ionization (MALDI) approach, the output of the reversed-phase columns is deposited on the MALDI plate, so that time is much less of a limitation in selecting the most informative set of precursors for fragmentation. However, even in this approach, one can expect a larger number of identifications upon repetition because of slight changes in elution times, differences in matrix crystallization, experiment-specific loss of peptides prior to reversed-phase chromatography by absorption, or limitations on sample consumption by the acquisition process itself. For both approaches, another desired output is the expression ratio: the ratio of expression of each protein in the experimental sample versus the control. In these experiments, in addition to learning about the ramifications of the deletion of UPF1, we sought to determine what limitations there are in both identification and quantification that might apply to proteomics experiments in general. In particular, we sought to develop additional scoring parameters so as to more easily identify false positives in a semi-automatic fashion, while retaining borderline identifications that are consistent with what is known about correct identifications. To that end, we report here on the results of five complete ICAT experiments, starting from exactly the same samples, so that biological variability does not contribute.
In these experiments, we combined together identifications from two different instrument types that initially used two different search engines. After combining the data into a common relational database, we submitted all of the spectra corresponding to identified peptides to Mascot. In addition, we developed new measures of reliability of identification and quantification that proved useful in resolving discrepancies in identifications. These quantities are adapted from the parameters we recently described for peptide mass fingerprinting experiments (8). In addition to the percent intensity matched parameter, we describe a percent ChemScore matched parameter that semiquantitatively assesses what percentage of the critically important ion fragments were detected. A third parameter was the internal mass consistency of the fragment ions. We also describe a fourth parameter, called Fragment TriScore, that gives higher credit to spectra in which the fragments with the highest ChemScore are also the most intense ions. These parameters were combined in an overall MatchScore (MatSc) parameter that is useful in documenting the credibility of an identification, especially in marginal cases. The parent mass accuracy parameter was left separate as an independent measure of credibility of identification. Upon calibration, based on added calibrants or masses that can be used as calibrants because they can be confidently assigned, the mass of a precursor ion should not deviate from the theoretical peptide mass by more than 20 ppm.
In order to determine which factors were most significant in preventing additional protein identifications, a lot of attention was focused on spectra that were not readily identifiable. In many cases, these spectra could be attributed to chemically modified forms of the peptides that had already been identified many times and were therefore surely very abundant. A second class of these peptides corresponded to peptides with one noncanonical tryptic terminus from these same abundant proteins. An additional problem is spectra whose measured masses were several mass units different from the mass of the peptide to which they were matched. To quantify these problems, each of the spectra were classified in groups corresponding to chemical modification status, mass accuracy, and tryptic specificity.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSCRUDE RESULTSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Peptide Chemistry
ICAT Reagent Labeling Procedure—
Two 500-µg aliquots from each strain were resuspended in 6 M guanidine-HCl, 1% Triton X-100, 50 mM Tris HCl, pH 8.5 (buffer B). The proteins were then reduced by the addition of 10 µl of 50 mM tricarboxyethylphosphine and boiled at 100 °C for 10 min. After cooling for 5 min to room temperature, 1 mg of the acid-cleavable form of the ICAT light reagent, dissolved in acetonitrile (ACN), was added to the wild type, whereas 1 mg of the acid-cleavable form of the ICAT heavy reagent was added to the upf1 knockout sample. After incubation for 2 h at 37 °C, the two aliquots were combined and precipitated with acetone (6:1 volume of acetone:volume of sample). The precipitated proteins were centrifuged for 10 min at 13,000 x g, the acetone was decanted, and the pellet was resuspended in 100 µl of ACN. The sample was then diluted with 900 µl of 50 mM Tris, pH 8.5, 10 mM CaCl2, 20% ACN. Then 12 µg of porcine trypsin (Promega, Madison, WI) was added, the sample was incubated for 2 h at 37 °C, then another 12 µg of porcine trypsin was added, followed by overnight digestion.
Ion Exchange Chromatography (IEX)—
The sample (1 ml) was diluted to 10 ml with 10 mM K3PO4, 25% ACN, pH 
2.5 (buffer C). In two batches, the sample was injected onto a 4.6 x 100 mm polysulfoethyl A cation exchange column at a flow rate of 1 ml/min. The high salt buffer contained 350 mM KCl, 10 mM K3PO4, 25% ACN, pH 2.5 (buffer D). Peptides were separated over four linear gradient segments using an Applied Biosystems Vision Work station (Applied Biosystems, Foster City, CA) in order to separate the peptides as efficiently as possible: 2 min to 10% buffer D, 15 min to 20% buffer D, 3 min to 45% buffer D, and 10 min to 100% buffer D. Seven to 23 fractions (see Table V, column No. IEX) consisting of 1.5 ml were collected beginning 4 min into the gradient. Prior to affinity chromatography, 250 µl of 100 mM Na3PO4 1500 mM NaCl, pH 10 was added to each fraction, which was sufficient to bring the pH to 7.2.
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Cleavage of Biotin—
Each eluate was dried completely using reduced pressure. A 200-µl aliquot of ICAT cleaving reagent from the ICAT reagent kit was added, followed by incubation at 37 °C for 2 h. Once again the sample was dried under reduced pressure until time for reversed-phase separation. At that time, each sample was resuspended in 100 µl of 2% ACN, 0.1% trifluoroacetic acid (TFA).
Mass Spectrometry
Electrospray Analysis—
Three or four dependent scans were collected per mass spectrometry (MS) scan using a QStarR Pulsar I (Applied Biosystems) equipped with a nanospray source using Analyst software. Typically, data was collected over 110 min at a flow rate of 0.3 µl/min, with 3-s parent scans from 300 to 1500 m/z, and with tandem MS (MS/MS) scans from 70 to 1500 m/z every 3 s. Information-dependent acquisition was used to select the most intense parent masses excluding singly charged ions and using dynamic exclusion to prevent the same parent mass from being chosen for fragmentation within a 45-s window. Samples were injected onto a capillary trap cartridge (Captrap, Michrom Bioresources, CA) at a flow rate of 20 µl/min to concentrate and desalt the samples. After 10 min, the trap cartridge was automatically switched in-line with the analytical column. Peptides were separated using a 75-µm x 15-cm reversed-phase C18 column, 3-µm particle size (PepMap; LC Packings, Hercules, CA), by means of an UltimateTM System (Dionex Corporation, Sunnyvale, CA). The gradient was typically from 5 to 30% buffer B, where buffer B is 85% ACN/10% water/5% n-propanol/0.1% formic acid/0.01% TFA and buffer A is 98% water/2% ACN/0.1% formic acid/0.01% TFA.
MALDI Analysis—
After cleavage, the peptides were separated using an Ultimate Chromatography system (Dionex-LC Packings, Hercules, CA) equipped with a Probot MALDI spotting device. A total of 50 µl of each digested protein fraction was injected and captured on a 0.3 x 5-mm trap column (3 µm, C18; Dionex-LC Packings) with a 0.1 x 150-mm resolving column (3 µm, C18; Dionex-LC Packings) connected in series. Peptides were resolved by dual-solvent gradient elution at a flow rate of 800 nl/min, using a gradient of 5–45% buffer B over 35 min, followed by a gradient of 35–90% buffer B over 5 min, where buffer A is 98% water, 2% ACN, 0.1% TFA and buffer B is 85% ACN, 10% water, 5% isopropanol, 0.1% TFA. Column effluent was monitored using a 3-nl ultraviolet flow cell and spotted directly onto a MALDI target using the Probot. Column effluent was mixed 1:2 with MALDI matrix (7.5 mg/ml 
-cyano-4-hydroxycinnamic acid dissolved in 75:25 ACN:water containing 0.15 mg/ml dibasic ammonium citrate) by means of a 25-nl mixing tee (Upchurch Scientific, Oak Harbor, WA) and was spotted onto the target in a 12 x 12 array at 20-s intervals. All MALDI spectra were acquired using a 4700 Proteomics Analyzer (Applied Biosystems) equipped with GPS Explorer version 1.0, and peaks were selected for MS/MS analysis using a strategy to collect as many useful spectra as possible without regard to heavy-to-light (HL) ratio (11). To accomplish this, the parent spectra were collected first, and masses were chosen for fragmentation from the spots in which they were most intense using the PeakPicker program (11).
Peptide Identification Methods—
QStar files were submitted to the ProICAT software package (Applied Biosytems) for the process of both identification and quantification. The database used was Swiss-Prot release 36. ProICAT generates a set of tables housed in an Access relational database. From these tables, two new tables were generated using SQL queries: A table that contained one row for each mass spectrum (see Table III ) and a table that contained the peak list information from each mass spectrum (see Table VI). Although the HL ratio and peptide sequence were originally in distinct tables, these values were incorporated into Table III.
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A large number of spectra were examined manually (Fig. 1, Workflow 3). Special attention was paid to spectra that were derived from unmatched, intense MS/MS spectra (Fig. 1, Workflow 3, step 1a), or intense precursors that were not automatically matched (Fig. 1, Workflow 3, step 1b). We were also particularly interested in spectra that had precursor masses that appeared to belong to HL pairs that indicated differential expression (Fig. 1, Workflow 3, step 1c). Other spectra were selected for special attention because they matched to proteins that we did not expect to be abundant, or that contained unusual modifications, or did not conform to trypsin cleavage rules (Fig. 1, Workflow 3, step 1d). Some spectra were examined randomly as a spot check of the annotation process (Fig. 1, Workflow 3, step 1e). Some of the methods used to identify these spectra are listed in Fig. 1, Workflow 3, section 2. For some spectra, the precursor mass or charge state was adjusted (Fig. 1, Workflow 3, step 2b). In other cases, the peak list was refined manually because of inappropriate de-isotoping, or because some fragment ions appeared to be derived from substances other than peptides (Fig. 1, Workflow 3, step 2c). In this case, the peak list was usually not permanently altered, and MatSc was calculated using the peptide sequence that was determined manually.
After we had uncovered evidence for peptides with new modifications, selected high-performance liquid chromatography (HPLC) runs were resubmitted to Mascot, for example, with lysine-modified ICAT reagent (KICAT), oxidized CICAT, or N-terminal ICAT reagent modified as alternative variable modifications (Fig. 1, Workflow 4). Once again, MatSc_Calc was used as the criterion to resolve conflicts. Regardless of how many searches were performed, in the end, each spectrum was allowed to match to no more than one peptide sequence. Some spectra were manually placed into Y1 class 99 (see Table IVB), because manual examination of the spectrum indicated that a proposed sequence was implausible, yet MatSc was significant.
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For peak density filtration, in many instances, the most important ions for determining the correct sequence are relatively high in molecular mass but weak in overall intensity. To ensure that the peak list contains the most significant ions from all regions of the mass spectrum, the peak list was filtered to eliminate all but the six most intense masses every 100 amu. Later, these same processed peak lists were resubmitted to Mascot so that the Mascot scores listed were derived from these altered peak lists.
Percent Intensity Matched—
PIM is calculated beginning with the filtered peak list. Only masses that are greater than 200 amu and smaller than 50 amu below the precursor mass are allowed to contribute to PIM because most masses outside this range are not sequence specific (Table II, rules 6 and 7).
Calculation of Fragment Ion ChemScore—
The Fragment Ion ChemScore is designed to approximate the theoretical expected intensity for each ion type (see Refs. 13 and 14 for alternative methods to do this). In this article, the ChemScore for each fragment ion is calculated based on the rules listed in Table I. If it was desired, these assumptions could be tuned to the instrument type (electrospray versus MALDI), but in this article we have used the same settings for all spectra.
In this scheme, it is possible to count any desired ion type. In this article, we have counted only y ions, b ions, a ions, y-17 ions, b-17 ions, certain immonium ions, and y and b ions generated by neutral loss of 64 amu from oxidized Met.
The first principle is that y ions and b ions are the most important. y ions have been assigned an arbitrary score of 6000, versus 5000 for b ions, etc. (Table I, rules 9 and 10). The ChemScore for any ion that contains a Lys or Arg is further multiplied by 1.5-fold (Table I, rules 3 and 4). Because we and others (15, 16) have observed preferential fragmentation before Pro (in all ion series), and after Asp, and to a lesser degree Glu, the ChemScores of all such ions have been multiplied the factors described by Table I, rules 5, 7, and 8. In addition, the ChemScores of all ions C-terminal to Pro have been decreased by 1.5-fold (Table I, rule 6), because these ions are less frequently detected (13).
We have observed that y-17 and b-17 ions are more intense when the N-terminal amino acid (aa) is Gln, possibly because of facile elimination of ammonia by cyclization; thus such ions are given the same ChemScore as the corresponding y or b ion (Table I, rule 14). The remaining (y-17) and (b-17) ions are given 20-fold more credit than other -17 ions if they contained R, H, or Q (Table I, rule 15). We have also observed unusually intense y-17 and b-17 ions after ICAT reagent labeling and acid cleavage; thus ICAT reagent-labeled Cys-containing (CICAT) fragments are dealt with the same as R, H, and Q (Table I, rules 16 and 17).
Most neutral losses of water have not been considered here, except for the case of N-terminal Glu, which are often unusually intense (Table I, rule 18).
Because the b2 ion and a2 ion often seem to be more intense than other a and b ions, the ChemScore for the b2 ion has been multiplied by 1.5-fold, whereas the ChemScore for the a2 ion is multiplied by 6.6-fold (Table I, rules 19 and 20).
Finally, it has been observed that an ion having the molecular mass of the parent ion minus the C-terminal aa is often present, especially when the sequence contains an internal arginine (which in general makes fragmentation of any kind more difficult). In this report, the ChemScore of the b(n–1)+18 ion is counted the same as an ordinary b ion (Table I, rule 21).
In order to promote detection of ions, the timed ion selector in MALDI mode is typically relaxed so that small amounts of ions from the unselected HL pair are often detected. We decided to add these ions to the fragment list and assign them ChemScores 10-fold below the value for the ICAT reagent-labeled peptide that was selected (Table I, rule 22).
Differential ChemScore values have also been applied to the most common immonium ions. The immonium ion for His at 110 is usually the most reliable immonium ion, thus it was assigned a ChemScore of 100. This value is too low to have much impact on scoring, but these small values would break ties between peptides that otherwise seemed to be equally plausible. If the starting peptide samples were less complex, then the reliability of immonium ions for identification could be increased, but even small amounts of His-containing peptides appear to render the His 110 ion detectable (Table I, rules 25–40).
It has also been observed that when methionine sulfoxide is present, a second series of ions is observed that is 64 amu below the canonical y and b ions (Table I, rule 23). A similar neutral loss from the oxidized ICAT side-chain has also been observed (Table I, rule 24).
Calculation of Percent ChemScore Matched (PCM)—
All of the masses in the filtered peak list that match to predicted ions contribute to PCM. The total ChemScore for the sequence in question is the sum of the Fragment Ion ChemScores of all of the considered ions. The ChemScore Matched is calculated by summing the Fragment Ion ChemScores for those ions that were matched to masses in the filtered peak list within tolerance (Table I, rule 42). Thus, PCM is calculated by:
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where ti is the total number of ions considered, MFM is the number of ions matched, m is the set of matched ions, and n is the set of all ions. If two theoretical ions matched within tolerance to the same mass, then the Fragment Ion ChemScore for both ions was used. Because of the quantitative nature of the Fragment ChemScore index, PCM is not significantly affected by consideration of additional ions, so long as these additional ions get assigned low ChemScores. In contrast, consideration of large numbers of additional ions can by itself destroy the usefulness of the PIM term, because almost any mass can be explained by some ion.
Calculation of Peptide Fragment TriScore (FrT)—
It is to be expected that the ions with the highest ChemScore (ChS) should correspond to the most intense ions. In addition, the observed masses are more credibly identified if they match the calculated fragment ion mass within experimental accuracy. To make this quantitative, we define PpmMin as a lower limit of mass error in parts per million (ppm) (Table I rule 41). Ions that match to a tolerance below PpmMin get increasingly less additional credit for doing so. PpmMin is in principle an instrument-specific factor, although in this study a value of 400 ppm was used for both instruments. The upper limits for matching were set to 500 ppm for ions with >200 amu (Table I, rule 42 and 43) and to 0.5 amu for ions <200 amu (Table I, rule 44). To prevent a small number of ions from dominating FrT, it was arbitrarily decided to truncate ChS and intensity (Int) parameters to the fourth highest value (Table II, rules 8 and 9). To calculate FrT, the theoretical ion list is sorted by decreasing ChS, and the peak list is sorted by decreasing Int. To normalize the value of Frt to the intensity distribution observed, the MaximumFrT was calculated as follows:
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where i is the number of elements in the shorter of the two lists. If i < 5, then MaximumFrT is calculated using:
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The MatchedFrT was then calculated for each matched fragment according to:
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where ThIM is the number of matched ions, and the list is sorted by decreasing ChS x Int. As with MaximumFrT, the equation for MatchedFrt may need to be reduced to suit the number of elements. Finally, FrT was calculated according to:
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so that the peptide would receive a value of 100 if the intensity distribution of ions exactly matched the theoretical distribution postulated above, and with a mass accuracy of <<PpmMin. Significantly lower scores result whenever the intensity distribution of ions does not conform to expectations. Note that FrT can still have a high value (near 100) if a small number of ions are detected, so long as these ions are predicted to be the most intense. This is one reason why no match is considered plausible unless the sum of b and y ions matched exceeds 3.
The FrT term acts to buffer against devaluation of the PIM term by random matching of intense masses to minor ion types, because when this takes place, although PIM increases, FrT decreases significantly.
Internal Calibration of MSMS Spectra—
In order to improve the internal accuracy of the fragment ions for the MALDI spectra, for each tentative identification, masses were selected to be used to calibrate the remaining fragments. To accomplish this, two masses corresponding to y or b ions were sought that were as intense as possible, and also well separated so that a slope measurement derived from them would be as accurate as possible. The rules to find appropriate masses are listed in Table II (rules 1–5). First, a low-molecular-mass fragment was sought that had a mass greater than 0.2 times the mass of the parent ion, and no greater than 0.4 times the mass of the parent ion. The second mass was then selected from the fragments >0.6 times the parent mass using the rules in Table II. A two-point calibration was then performed. If no appropriate masses could be found, then no calibration was performed.
Intensity Weighted Mass Error (ppw)—
So that matches to low-intensity ions do not distort the mass error term, intensity-weighting was performed. Because the immonium ion region was often poorly calibrated after this procedure, no fragments less than a value of 200 amu were allowed to contribute to ppw.
Calculation of Overall Score (MatSc)—
The overall score (MatSc) is a compound index that includes contributions from many of the parameters that can be used to judge the quality of an identification. It is not yet clear how these parameters should be optimally combined, as each of the individual parameters have limitations under certain conditions (like if the peak list is too large). Sophisticated mathematical techniques have been used by others to optimize the weighting of such parameters (17).
In this article, MatSc is calculated according to:
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where PpmMin is the minimum ppm value below which matches are of no greater significance. In these calculations, a PpmMin of 400 ppm was used. This seems rather high, but there were a few identifications that by all other criteria appeared to be correct for which this value was appropriate. In most instances, MatSc would have higher discriminating power if PpmMin were around 50 ppm. Note that the PpmMin term can break ties between peptides that are identical except for Lys versus Gln (0.036 amu difference) even when PpmMin is 400 ppm.
Calculation of SeqString—
The SeqString describes how the ions that match the proposed peptide are dispersed along the length of the sequence of the peptide. It is not used for spectrum classification, but is a useful visual guide. A similar scheme has been used previously by others (David Fenyo, personal communication). Each ion type is assigned a score, as listed in Table I, rules 9–13, column SeqString. Each peptide bond in the sequence is assigned the sum of those scores, with the exception that the sum must not exceed a value of 9. The most meaningful way to interpret the SeqString is to place it in TrueType font directly over the sequence to which it corresponds, shifted by half a space.
For example, the SeqString 9004555929 might correspond to the sequence ACDEFGHILMK. It could be displayed as:
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This would indicate that both b and y ions were found that support the AC peptide bond, that no ions were found to corroborate the CD and DE bonds, a b ion corroborates the EF bond, etc.
Counting y and b Ions—
Probably the simplest way of assessing the quality of a database identification is to count the number of y and b ions matched (see Ref. 18 for an alternative way to do this). If this number is small (e.g. 
3), then the identification is uncertain. If three y + b ions match, and the peak list contains only three or four masses, then the identification might be correct. If the peak list is much larger, then the number of b and y ions matched is no longer so useful, and it must be balanced with a term like % Intensity Matched. In this report, four or more y + b ions were required for all classifications of identified spectra. Table II, rules 10–12 list several additional considerations that apply to counting ions.
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CRUDE RESULTS |
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TOPABSTRACTMATERIALS AND METHODSCRUDE RESULTSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Table S6 contains each of the peak lists that were automatically extracted for each of the 73,009 spectra in Table S3. This is an enormous table, and three examples of peak lists corresponding to two distinct spectra (one of which was obtained manually from the peak list in Table S6) are shown in Table VI.
Tracking—
The tracking fields include such parameters as spectrum number SpID; instrument type EM, where E designates electrospray and M designates MALDI; experiment number Ex; elution time (electrospray) or well number (MALDI), designated TiW; the ion exchange fraction number Fr; and a plate number (MALDI) or replicate number (electrospray) Pla. An experiment consists of all data from the same initial ICAT reagent labeling reaction and is instrument-specific. In some cases, replicate electrospray HPLC runs were performed on the same IEX fraction, resulting in the need for Pla. Because in some cases one HPLC run was collected on more than one plate, there is also an HPLC run index EID. Whenever the same spectrum was submitted to more than one database search, MatSc_Calc was used to resolve sequence discrepancies, using SpID as a key field.
Parent Ions—
The parent ion fields include the recalibrated parent ion mass CalMW; its intensity IntP; the mass of the corresponding peptide sequence PepMW; and the difference between these two masses in ppm Df. A fifth field defines the mass bin MB of the parent ion, which is obtained by dividing the parent ion mass by 1.0005, and then rounding to the nearest integer. MB is especially useful for comparing the results of multiple experiments, or adjacent ion exchange fractions, and is particularly useful as an index. Because a large number of identifications were made to peptides where CalMW-PepMW was nearly equal to small integers, another pair of fields were calculated to classify these spectra. The mass bin class field MBC corresponds to the integer itself. When MBC = 1, CalMW is about 1 amu larger than PepMW. This happens if the peptide is deamidated, or if CalMW was incorrectly assigned to the second isotope of the isotope cluster, rather than to the monoisotopic mass. This is more likely to happen at higher peptide molecular masses where the monoisotopic mass is harder to distinguish. Because deamidation is more interesting than a mistake in de-isotoping, the mass difference in ppm DfC was calculated, assuming an ideal mass difference of 0.984 amu, which is the mass difference between an acid and an amide. If the explanation is de-isotoping, then the mass difference should be 1.0033, which is the mass of the extra neutron in C13. Spectra were also classified according to mass accuracy class MAc, where class 1 is defined as 20 ppm for MALDI spectra or 80 ppm for electrospray spectra. Class MAc 2 is defined as 80 ppm (MALDI only), whereas class 3 is defined as 0.5 amu. Spectra of class 4 are off by more than 3.5 amu, because any mass difference between 0.5 amu and 3.5 amu would be grouped in a different MBC prior to calculation of MAc. Identifications with MAc 4 may be correct if the wrong isotope cluster was assigned to the spectrum, or nearly correct if the peptide contains an unassigned chemical modification.
In most cases, in the MALDI experiments, masses that were identified with high confidence were used to calibrate the spot in which the MS/MS spectrum was collected, as well as immediately adjacent spots. Such masses have a value of 1 in the Cal field. Thus, if the ppm for the parent ion is 0, it was probably used as a calibrant. In a few cases, the ppm difference rounded to 0 to four significant figures even when the mass in question was not used as a calibrant.
MS/MS Ions—
The MS/MS fields include the Mascot score Sc; the overall match score MatSc, defined above; the number of y and b ions matched yb; SeqString defined above; and the four major components of MatSc: namely, Percent ChemScore Matched PCM, Percent Intensity Matched PIM, intensity-weighted average ppm deviation ppw, and Fragment TriScore FrT. The number of masses detected prior to peak filtering MFD, after peak filtering MFU, the number of masses matched MFM, and the number of theoretical ions matched ThIM are also listed. Note that it is possible for more than one theoretical ion to match the same mass, and vice versa. IntD lists the sum of the intensity of the ions counted in MFU. Note that all of these scores are dependent on peak detection. In an ideal world, it would be possible to detect peaks reliably and reproducibly. In these experiments, the peak lists for the MALDI data are usually reliable; that is, upon manual inspection, the peaks deposited in the database appear to reflect faithfully what one would expect from careful inspection of the raw spectra. For the electrospray data, however, it is difficult to define peak detection, de-isotoping, and de-charging parameters that result in a good peak list for all spectra. For this reason, we spent a lot of time validating electrospray identifications, as is regular practice. We hope to develop more robust peak extraction methods in the future.
HL Fields—
The HL fields include the measured heavy-to-light ratio HL. In some cases, the ratio was not measurable because no HL partner was detected. HL_S summarizes how many possible HL partners were detected. HL_S has six digits, each of which can be either 0 or 1. If the first position = 1, then a potential HL partner was detected about 9.03 amu above the parent mass. The remaining five positions of HL_S correspond to the presence or absence of a peak 18, 27, –9, –18, and –27 amu above or below the parent mass in question. Under ideal circumstances, the HL pair would be nonambiguous, meaning there would be only one non-zero digit in HL_S, which would correspond to the number of CICAT residues in the peptide. In this case, the binary field HL_1 is set to 1. We have included as "identified" (Y1 class 1, see below) only those peptides in which HL_S is consistent with the number of Cys residues in the corresponding sequence. A second parameter, Ippm, is the ppm difference between the observed masses of the HL pair and the theoretical mass difference of the HL pair (not shown in Table III). When the HL pair was ambiguous, the value for Ippm listed corresponds to listed sequence. A final HL parameter is the HLI_S. It is similar to HL_S, but lists instead whether there are any interfering masses within 2 amu of an HL pair at +9, +18, +27, -9, -18, or -18. When HL_S is nonambiguous, and HLI_S is all zeroes, then the ratio of the HL pair is more reliable (depending on the intensity of the peaks), and the binary field HLI_1 is set to 1. When HL_S is ambiguous, the exact value of the HL ratio may be unknowable, because more than one peptide may contribute to the intensity of either the c0 form of the ICAT reagent or the c9 form of the ICAT reagent. This problem is lessened if it turns out that the ambiguous ICAT reagent-labeled mass belongs instead to a nonoverlapping HL pair. For example, if HL_S was 111,000 and the corresponding peptide had one Cys, the potentially confounding masses could correspond to a second unrelated HL pair whose masses were 18 and 27 amu above the first peptide’s mass. Because there are slight differences in HL ratio between experiments due to mixing inaccuracies, the HL values must be normalized. In the five experiments listed here, these normalization constants were small (between 0.855 and 1.11, see Table V, column Nor).
Peptide and Protein Fields—
These fields indicate which peptide sequence Seq was identified, and which protein was matched to that sequence, which is designated by (usually) a Swiss-Prot accession number Acc. The amino acid preceding the peptide is listed in < (SeqA in Access), whereas the amino acid following the peptide is listed in > (SeqZ in Access). If field "<" is empty, then the peptide was N terminal; similarly for field ">" and C-terminal peptides. When modified amino acids were detected, the normal capital letter abbreviation for the amino acid is altered in field Seq, whereas DSeq corresponds exactly to the sequence in the database (not shown in Table III). Table IVA lists all of the single-letter amino acid codes for altered amino acids in column Sym, where column RMW lists the molecular mass difference between the modified aa and the natural aa, or in the case of modified CICAT peptides the molecular mass difference between the modified and unmodified form of the CICAT residue. For example, "C" refers to the c0 form of the ICAT reagent, whereas "c" refers to the c9 form. "Z" refers to unmodified Cys. If an Asn or Gln has been deamidated, it is labeled as the corresponding acid residue in Seq, but not in DSeq. In some cases, the accession numbers are PIR accession numbers from csc-fserve.hh.med.ic.ac.uk/delphos.html (referred to hereafter as DelPhos) because there was no corresponding protein in Swiss-Prot. Table IVA also lists how many spectra are classified into each grouping, either at high confidence (field HiMac), at high confidence but also considering different MBC bins (field High), or at any confidence (field Low).
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Overall Fields—
Two final parameters, Y1 and Y2, group spectra into classes of overall reliability. The most important class is Y1 class 1, which corresponds to the highest confidence category of CICAT peptides. Table IVB lists each of these classes. Note that most of the classifications of spectra according to fields Y1 and Y2 depend on the classifications in fields ChM, MAc, NT, HL_S, HL, yb, MatSc, or Sc. Y1 classes 8 and 9 are special exceptions that correspond to Cys-modified tryptic peptides that derive from trypsin or human keratin, respectively, and therefore do not contribute to any of the yeast peptide or protein statistics. Column No. lists how many spectra are in each Y1 class. Field Y2 groups spectra according to how they are modified. Y2 class 1 refers to CICAT peptides, whereas Y2 class 3 correspond to Lys-modified (KICAT) peptides. Y2 class 2 refers to peptides that are not modified at all, whereas Y2 class 4 have an unalkylated Cys. In most cases, this appears to be a result of ISD. Y2 class 5 are spectra matched to peptides with chemical modifications that correspond to more than one of classes 1–4, whereas Y2 class 0 have fewer than three y or b ions matched and are therefore essentially unmatched. Note, however, that such a spectrum could be matched with high confidence due to co-migration with second spectrum with high Mascot score (Sc) and yb. In addition, in MALDI experiments, such spectra could become identifiable after subsequent MS/MS experimentation.
Data Processing
Low Stringency Identifications—
The first task in extracting information from proteomics experiments is to determine which identifications are to be considered reliable. We chose to start with identifications using either ProICAT (for electrospray samples) or Mascot (for MALDI samples). Normally, threshold criteria are chosen that appear to exclude the bulk of the incorrect assignments while retaining the bulk of the correct assignments. In these experiments, we were particularly interested in studying the borderline identifications, and therefore have collected statistics on many identifications that would normally be discarded. We decided upon four minimal requirements for tentative identification: 1) at least four y and b ions must match within 300 ppm; 2) the sequence must be at least 6 aa long; 3) MatSc must exceed 5000; and 4) the tentative sequence must match the spectrum in question better than any other considered sequence. This last requirement requires some judgment regarding which chemical modifications are at all plausible for consideration. Generally a chemical modification is considered reasonable only if the same modification has been found on a peptide that was matched with high confidence to a high-quality spectrum from within the same experiment, or alternatively if the unmodified form of the peptide in question is known to be so abundant that nearly any chemical modification seems plausible. There is a special category of spectra that we excluded from further consideration: they consist of spectra that by the criteria of MatSc and yb seem to match a certain sequence, yet manual inspection of the spectrum indicates that the identification is not credible (Y1 class 99). In some cases, this results because the spectrum is so weak that the process of extracting a peak list failed. In other cases, examination of the spectrum indicates that the substance that was fragmented does not correspond to a peptide at all and is probably derived from contamination. In a few cases, the spectrum seems to correspond to a peptide, but has intense ions that cannot be explained easily by the proposed sequence. This process has been selectively applied to the data, with special attention paid to spectra that uniquely define proteins. Therefore, there are surely still examples of spectra that are matched to peptide sequences that can be shown by manual examination to correspond to other sequences, or substances other than peptides. Next, we classified spectra as 1) ICAT reagent modified on Cys (CICAT), 2) not ICAT reagent labeled, 3) ICAT reagent modified on Lys (KICAT), or 4) incompletely alkylated. These classifications correspond to Y2 classes 1–4.
Using the criteria for tentative identification described above, there are 12,249 identifications in Y2 class 1, corresponding to 2181 distinct CICAT peptides, or 1029 distinct proteins, some of which are surely misidentifications (Fig. 2A).
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It is not possible to derive the optimized list of proteins until all of the relevant data are consolidated together (see Ref. 19 for a discussion of this issue). In this report, the relevant data include identifications from all five experiments. Another complication is that some peptides are virtually identical by mass spectrometry (I versus L, and combinations of amino acids that add up to the same mass without distinguishing backbone fragment ions). Because of this, there is a danger that two distinct peptide sequences could get mapped to indistinguishable MS/MS spectra. To derive an optimized protein list, we submit the entire list of identified CICAT peptides to the Mascot search engine in the form of theoretical y ion spectra. Mascot then automatically selects the smallest number of distinct protein sequences that can explain the spectra. Later, the sequences are mapped to the other tracking fields like codon bias using the ICAT dictionary.
To generate our theoretical ICAT dictionary, we started with the yeast proteome at genome-ftp.stanford.edu/pub/yeast, updated 1/2003. In this database, there are 23,308 distinct Cys-containing peptides with no missed trypsin cleavage sites that contain at least 6 aa and have a MW of <4000. Of these, 481 (2%) are encoded by more than one distinct protein. The ICAT dictionary has a degeneracy field that enumerates how many genes encode each peptide. After the list of these sequences was generated, the sequences were grouped using an SQL query so that each unique sequence would be paired with the protein accession number that corresponded to the protein with the highest codon bias, as well as a number that listed how many distinct genes encoded the peptide. In some cases, it was necessary to add new sequences to the ICAT dictionary to accommodate additional sequences that were derived from other databases, for example, the Mascot nonredundant database. The final protein list contains Swiss-Prot accession numbers whenever possible, and PIR-derived "S" numbers from the Mascot nonredundant database when the proteins in question were not in Swiss-Prot. Extensive use was made of the web site csc-fserve.hh.med.ic.ac.uk/delphos.html to resolve questions of protein identity. At this web site, one can
20, MAc = 1, MBC = 0, 0.2 < HL < 5. Stringency others L, the number of low-stringency identifications where Y2 = 0, 2, or 3. Stringency others H, the number of high-stringency identifications where Y2 = 0, 2, or 3 (unmodified, KICAT, or incompletely alkylated); no HL restriction. N, the number of high-stringency identifications where Y2 = 0 (unmodified peptides). X, the number of high-stringency KICAT identifications where Y2 = 2. Z, the number of high-stringency identifications where Y2 = 3 (incompletely alkylated peptides). Met, the number of high-stringency CICAT identifications that also contained unmodified Met. MetOx, the number of high-stringency CICAT identifications that also contained methionine sulfoxide. % Ox, the percentage of high-stringency CICAT identifications in which Met was oxidized (Y2 = 1). CICAT_Ox, the number of high-stringency spectra containing oxidized CICAT (ChM = 11).
