Quantitative Comparison of Proteomic Data Quality between a 2D and 3D Quadrupole Ion Trap

Transcription

1 Quantitative Comparison of Proteomic Data Quality between a 2D and 3D Quadrupole Ion Trap Adele R. Blackler, Aaron A. Klammer, Michael J. MacCoss, and Christine C. Wu*, Department of Pharmacology, University of Colorado Health Sciences Center, Aurora, Colorado 80045, and Department of Genome Sciences, University of Washington, Seattle, Washington A 2D ion trap has a greater ion trapping efficiency, greater ion capacity before observing space-charging effects, and a faster ion ejection rate than a traditional 3D ion trap mass spectrometer. These hardware improvements should result in a significant increase in protein identifications from complex mixtures analyzed using shotgun proteomics. In this study, we compare the quality and quantity of peptide identifications using data-dependent acquisition of tandem mass spectra of peptides between two commercially available ion trap mass spectrometers (an LTQ and an LCQ XP Max). We demonstrate that the increased trapping efficiency, increased ion capacity, and faster ion ejection rate of the LTQ results in greater than 5-fold more protein identifications, better identification of low-abundance proteins, and higher confidence protein identifications when compared with a LCQ XP Max. In the past few years, there has been an explosion in technological advances improving the performance of commercially available mass spectrometers. A particularly powerful development has been the two-dimensional (2D) radio frequency (rf) quadrupole ion trap. Multipole-based 2D ion trap mass analyzers have been used extensively as part of hybrid mass spectrometers, including Fourier transform ion cyclotron resonance, time-of-flight, and triple quadruple mass analyzers. However, more recently, the 2D ion trap mass analyzer is being used as a stand-alone mass spectrometer instead of solely as part of a hybrid instrument. One commercially available 2D ion trap mass spectrometer is the ThermoElectron LTQ. This mass spectrometer is based on the standard three-dimensional ion trap, the ThermoElectron LCQ. Unlike the LCQ, which contains two end cap electrodes and a ring electrode, the LTQ is composed of a segmented hyperbolic quadrupole mass analyzer with three distinct axial segments. The quadruple mass analyzer creates a 2D rf field, and discrete dc voltages are applied to the different axial segments to contain the ions axially in the center segment of the mass analyzer. A slit is cut in two of the center rods to facilitate radial ion ejection and detection at two electron multiplier detectors. Unlike alternative 2D ion trap mass spectrometers, many of the scan functions and electronics on the LTQ operate in a fashion similar to the 3D ion * To whom correspondence should be addressed. Phone: christine.wu@uchsc.edu. University of Colorado Health Sciences Center. University of Washington. trapsusing resonance excitation for ion isolation, activation, and mass analysis. 1 Although the 2D ion trap operates in a fashion analogous to that of a conventional 3D ion trap, it has several significant performance advantages. First, there is no rf field in the ion injection axis for the ions to overcome before entering the trap. Therefore, the trapping efficiency from an external ion source is improved dramatically. This improved trapping efficiency results in a much faster relative ion injection time and contributes to a more rapid duty cycle. Second, the linear configuration of the mass analyzer results in a larger volume that improves the overall ion capacity before observing space-charging effects. This increased ion capacity is complemented by the radial ejection of ions, as opposed to axial ejection on a 3D ion trap. Radial ejection of ions facilitates the use of two detectors, one on each side of the mass analyzer, resulting in a 2 increase in total detection efficiency. Finally, the LTQ has a 3 increase in ion ejection rate relative to the LCQ while maintaining the same resolution. These three improvementssincreased trapping efficiency, ion capacity, and ion ejection ratesshould significantly improve the qualitative identification of peptides by automated µlc/ms/ms. To demonstrate these improvements, identical complex peptide mixtures of soluble Escherichia coli proteins were analyzed by datadependent µlc/ms/ms on ThermoElectron LTQ and LCQ XP Max mass spectrometers, and the resulting tandem MS spectra were searched using a normalized version of SEQUEST. The program DTASelect was used to apply thresholds that maximized the number of protein identifications while minimizing the false discovery rate (FDR) for both data sets. The LTQ data set resulted in a 5-fold higher number of protein identifications, including a higher percentage of identifications from low-abundance proteins, and increased spectral count and sequence coverage for identified proteins. METHODS Sample Preparation. E. coli (strain OP50) was cultured in LB media to log phase growth at 37 C. Bacteria were pelleted and lysed in 50 mm NH 4 HCO 3 ph 8.5 at 1000 psi using a French press. Total lysates were microfuged at rpm for 30 min at 4 C. The resulting supernatant was collected and assayed for protein concentration using the RC DC Protein Assay Kit (BioRad, Hercules, CA). Protein samples were reduced in 5 mm dithio- (1) Schwartz, J. C.; Senko, M. W.; Syka, J. E. J. Am. Soc. Mass Spectrom. 2002, 13, /ac051486a CCC: $33.50 xxxx American Chemical Society A Published on Web 01/17/2006 PAGE EST: 7.2

2 theitol for 30 min at 60 C and alkylated in the dark in 15 mm iodoacetamide for 30 min at 25 C. The protein sample was then adjusted to 0.1% Rapigest (Waters Corp., Milford, MA) and 2 mm CaCl 2 and digested using modified trypsin (Roche, Indianapolis, IN) at a ratio of 1:30 enzyme/protein overnight at 37 C with shaking. Digestion was terminated by acidification with 200 mm HCl and incubation at 37 C for 45 min. The sample was microfuged for 4 min at rpm, and the supernatant was collected for proteomic analysis. Microcapillary liquid chromatography-tandem mass spectrometry (µlc/ms/ms). Identical samples were analyzed five separate times on both an LCQ XP Max and an LTQ ion trap mass spectrometer. Both instruments had the ThermoElectron Ion Max electrospray source replaced with identical in-house-constructed microspray sources and were interfaced with identical Agilent 1100 binary HPLC and autosampler systems. Each protein digest (10 µg) was loaded from the autosampler onto a fused-silica capillary column (100-µm i.d.) packed with 15 cm of Luna C18 material (Phenomonex) mounted in the microspray source using flow from the HPLC pump. The flow during the loading was split prior to the autosampler from 150 to 2 µl/min. After 25 min of loading, the location of the split was changed from upstream of the autosampler to immediately distal the microcapillary column using the divert valve on both mass spectrometers. The restriction of the running split was less than the restriction of the loading split reducing the flow through the column from 2 µl/min to 500 nl/min. Mass spectra were acquired using data-dependent acquisition with a single full mass scan followed by three MS/MS scans. Each MS/MS scan acquired was an average of three microscans on the LCQ and two microscans on the LTQ. Data Analysis. MS/MS spectra from each analysis were searched using no enzyme specificity on a 96-node G5 Beowulf cluster against an NCBI E. coli-protein database concatenated to sequences of common contaminants and a shuffled decoy database 2 using a normalized implementation of SEQUEST. 3 The resulting peptide identifications were assembled into proteins using DTASelect, 4 thresholds were adjusted, and the FDR monitored using the decoy database. Default thresholds cutoffs were made using the following parameters: normalized crosscorrelation score for +1, +2, and +3 charge peptides of 0.28, DeltCN value of 0.15, include only fully tryptic peptides, allow loci with a single peptide identification, peptides must have >4 amino acids, 20% of the predicted fragment ions must be accounted for within the spectrum, remove protein identifications that were subsets of others, and remove ambiguous identifications. Using DTASelect, these filters can be achieved using the following command line parameters: d.15 -y 2 -p1-i0.2 -o -Smn 4 -a false. The FDR was estimated by dividing the number of protein identifications mapping to proteins from the shuffled decoy database by the number of nonredundant proteins mapping to the unshuffled protein sequences. Microarray Data. Publicly available microarray data were used as a proxy measure of abundance for the proteins identified (2) Finney, G.; Merrihew, G.; Klammer, A.; Frewen, B.; MacCoss, M. J. In 53rd ASMS Conference on Mass Spectrometry; San Antonio, TX, (3) MacCoss, M. J.; Wu, C. C.; Yates, J. R., 3rd. Anal. Chem. 2002, 74, (4) Tabb, D. L.; McDonald, W. H.; Yates, J. R., 3rd. J. Proteome Res. 2002, 1, Table 1. Summary of Data Collected for Five Independent Runs on Both the LTQ and LCQ XP Max a total spectra MS and MS/MS no. of MS/MS spectra no. of protein IDs FDR LTQ ( 1079* ( ( 7* 0.25 ( 0.12 LCQ XP Max 5789 ( ( ( ( 0.16 a Data are expressed as mean ( standard deviation. *P < by tandem mass spectrometry. The normalized fluorescent values of mrna were obtained from the web site, asap.ahabs.wisc.edu/annotation/php/experiment_data.php? GenomeID)MG1655%20. The data set chosen was PALSP49 LB, log phase growth, 1 MG All gene comparisons were made by querying the NCBI E. coli strain K12 accession number. Only genes that were present in both the microarray and proteomics data sets were used in the analysis. Signal-to-Noise Comparison. Glu-Fibrinopeptide (Sigma) was infused continuously at a rate of 10 fmol/min in 50:50:0.1 MeOH/H 2 O/formic acid. A total of 1000 MS/MS spectra were collected using a single microscan on the doubly charged precursor ion (m/z ) 786.3) using both the LCQ XP Max and an LTQ mass spectrometer. The maximum injection time was adjusted on both instruments to ensure that the ion trap was filled to capacity for all scans. RESULTS AND DISCUSSION A single peptide digest was prepared by digesting 100 µg of the E. coli soluble protein fraction. This fraction was then analyzed by running 10 µg of this sample five separate times on both an LCQ XP Max and an LTQ mass spectrometer. The HPLC apparatus and separation conditions were identical, and the separation was reproducible using a simple flow splitter and inhouse-constructed microcapillary columns. To confirm that HPLC separation did not contribute significantly to differences observed between the two instruments, the retention time separation for the tryptic peptide TTDVTGTIEL- PEGVEMVMPGDNIK was compared within the context of the unfractionated mixture between all 10 µlc/ms/ms analyses (5 LCQ XP Max and 5 LTQ analyses). For all 10 analyses, this peptide eluted between and min. The chromatographic retention time error for this analysis was 0.50 and 0.23% RSD for the LCQ XP Max and LTQ, respectively. Furthermore, the interinstrument separation was extremely reproducible with a combined 0.58% RSD. This observation suggests that the intraand interinstrument chromatographic separation was comparable between instruments and is not likely a significant contribution to the observed differences in peptide and protein identifications between the two mass spectrometers. Comparison of Protein Identifications between Instruments. Table 1 summarizes the mass spectrometry results from the multiple analyses between the two mass spectrometers. This table shows the average number of total spectra, the average (5) Allen, T. E.; Herrgard, M. J.; Liu, M.; Qiu, Y.; Glasner, J. D.; Blattner, F. R.; Palsson, B. O. J. Bacteriol. 2003, 185, B

3 number of tandem mass spectra, the average number of identifications, and the average FDR from the five independent runs for both the LCQ XP Max and LTQ. For each µlc/ms/ms analysis, an average of ( 654 tandem mass spectra were obtained using the LTQ compared to 4087 ( 127 acquired on the LCQ XP Max. An average of 566 ( 7 protein IDs were made using the LTQ, a more than 5-fold increase over the 100 ( 4 identifications made with the LCQ XP Max (p <0.0001). This increase in IDs was accompanied by a slight increase in FDR (0.25% compared with 0.15% LTQ vs LCQ). Our data are consistent with previous studies that have shown similar increases in total number of protein identifications between these instruments. 6 The Venn diagram shown in Figure 1A shows the overlap between the unique protein identifications made from data acquired from the LTQ and the LCQ. A total of 787 unique proteins were identified on the LTQ from the combination of five independent runs, and a total of 167 unique proteins were identified by the LCQ. Of these, 165 proteins were identified by both instruments. Of the proteins identified by the LTQ, 22% were also identified by the LCQ, and 98.8% of proteins that were identified by the LCQ were also proteins identified by the LTQ. Of all the proteins identified by five combined runs on the LCQ, only two IDs were unique to the LCQ. Both of these protein identifications were identified from only a single peptide, reducing the confidence of the protein identification. 3,7 The mass spectra for the two peptides that are unique to the LCQ XP Max are shown in Figure 1B. Upon manual inspection, both of these protein identifications are obviously of poor quality, and neither exceeded the validation criteria reported by Link et al. 8 Because the chromatographic hardware was identical between the two instruments, differences observed between the LCQ and LTQ can be attributed specifically to the respective mass spectrometer. In contrast to these data, Mayya et al. 6 reported that the overlap of protein identifications between the two instruments is fairly low, with the LTQ only identifying 70% of the same proteins as the LCQ. Though Mayya and colleagues used stringent XCorr values, there is no attempt to assess the FDR between the two instruments. Mayya et al. also noticed a much better overlap in identifications when only multiple peptide-hit proteins were considered; only considering multipeptide hits also reduces the FDR (Figure 4). It is unclear what the FDR actually was for their analysis, so it is possible that many of the proteins identified by the LCQ and not by the LTQ, especially single-peptide identifications, were false discoveries. Comparison of Spectral Count and Protein Sequence Coverage Obtained between Instruments. The percent sequence coverage of identified proteins for each instrument is shown in Figure 2A (absolute number of identifications) and B (fractional number of total identifications). Analysis using the LTQ resulted in increased sequence coverage, with some proteins having up to 65% coverage, while no protein identified by the LCQ XP Max had greater than 55% coverage. Though the LTQ identified a larger number of proteins with greater sequence coverage, over a quarter of the proteins identified by both the (6) Mayya, V.; Rezaul, K.; Cong, Y. S.; Han, D. Mol. Cell. Proteomics 2005, 4, (7) Sadygov, R. G.; Liu, H.; Yates, J. R. Anal. Chem. 2004, 76, (8) Link, A. J.; Eng, J. K.; Schieltz, D. M.; Carmack, E.; Mize, G. J.; Morris, D. R.; Garvik, B. M.; Yates, J. R., III. Nat. Biotechnol. 1999, 17, 676. Figure 1. Overlap of proteins identified using the LTQ and LCQ XP Max. (A) Venn diagram showing the number of proteins that were identified only by each respective instrument and the number of proteins that were identified by both instruments. These data are the combined results from five analyses of the same sample on both instruments. (B) Two tandem mass spectra identifying proteins unique to the LCQ XP Max. The analysis of these two tandem mass spectra using a normalized version of SEQUEST resulted in normalized XCorr values that exceeded the cutoff of Based on the false discovery rate estimated from the decoy database, there are likely two proteins from the five LCQ analyses that are false discoveries. The false discoveries should be randomly distributed among proteins within the sequence database. Therefore, it is likely that the false discoveries are proteins unique to one of the two instruments because the chance of identifying random proteins from multiple analyses it is highly unlikely. LTQ and LCQ XP Max had less than 5% sequence coverage, and a majority of all proteins identified ( 60%) had less than 10% sequence coverage. This sampling is consistent with a previously C

4 Figure 2. Number and percent of total protein identifications resulting from data collected from the LTQ and LCQ XP Max. The data are categorized by percent sequence coverage (A, B), the number of spectra (C, D), and fluorescent intensities of the respective mrna from Allen et al. (E, F). Data represent mean ( SE, N ) 5. ***P <0.001, *P <0.01 significance between different instrument values in same category determined by t-test. (A) Average percent sequence coverage plotted as total proteins numbers identified or (B) percent of total proteins identified. (C) Average number of spectra plotted as total proteins numbers identified or (D) percent of total proteins identified. (E) Measured fluorescent intensity on a microarray chip for the corresponding mrna plotted for the total proteins identified or (F) percentage of total proteins identified. Proteins identified that did not have corresponding mrna measurements were between 1.8 and 3.9% for the LCQ proteins and 11.8 and 13.7% for the LTQ proteins. reported model for random sampling by data-dependent acquisition of MS/MS spectra. 9 The spectral count for identified proteins using each instrument is shown in Figure 2C and D. Consistent with a previous report, 10 the majority of proteins identified by both instruments were identified with less than five spectra. No protein identified using the LCQ had more than 20 spectra identifying it, while an average of 10 proteins identified for each of the LTQ runs had between 30 and 35 spectra identifying them. The increased number of spectra for the LTQ is most likely a result of the duty cycle of the LTQ. This increased number of spectra identifying each protein increases the confidence for the identification. 9 As can be seen in Figure 2D, the LTQ identifies a significantly smaller percentage of proteins with fewer than five spectra, when compared to the percentage of proteins identified by the LCQ with fewer than five spectra (p <0.001). The LTQ identified a significantly higher (9) Liu, H.; Sadygov, R. G.; Yates, J. R., III. Anal. Chem. 2004, 76, (10) Washburn, M. P.; Wolters, D.; Yates Iii, J. R. Nat. Biotechnol. 2001, 19, 242. percentage of proteins with between 5 and 15 spectra, when compared with the LCQ (p <0.001). Comparison of the Dynamic Range between the LTQ and LCQ XP Max. Codon adaptation index (CAI) has been used as an estimate of the identification of proteins in large-scale proteomics data sets. 10 However, there is only minimal if any correlation between CAI and mrna levels, 11 and thus, CAI is likely an even poorer estimate of protein abundance than it is of mrna. Therefore, although not ideal, we used publicly available microarray data as a measure of the mass spectrometer s ability to identify proteins from low-abundance transcripts. Figure 2E and F shows the number and percentage of protein identifications for each instrument and the corresponding log-normalized fluorescent intensity for the respective gene transcript. 5 These data demonstrate that the LTQ is capable of identifying proteins corresponding from the translation of low-abundance mrna with greater efficiency than the LCQ. A significantly higher percentage (44%) (11) Goetz, R. M.; Fuglsang, A. Biochem. Biophys. Res. Commun. 2005, 327, 4-7. D

5 of proteins identified by the LTQ correspond to mrnas that have low fluorescent intensity levels (below 1.92), compared with the proteins identified by the LCQ (15%). Whereas most of the protein identifications made by the LCQ (66%) corresponded to highabundant mrna, many of the protein identifications made by the LTQ correspond to genes of low abundance. Although a majority of the identifications with the LTQ are from proteins translated from high-abundance mrna, the fast duty cycle and high ion capacity of the linear ion trap has increased the dynamic range to improve the identification of low-abundance proteins in the presence of an excess of high-abundance proteins. Although a small percentage of proteins identified by the LCQ (3.1%) and LTQ (12.8%) did not have corresponding mrna fluorescence, these small percentages should not affect the overall trend shown in Figure 2E and F. Comparison and Evaluation of Mass Spectrometer Signalto-Noise Ratios. Although chemical noise is often the greatest noise source in the handling of any complex proteomics sample, instrument noise arising from ion statistics, the ion detection system, and fluctuations in the ion source can also be significant and represent the ultimate limiting factor in any analytical measurement. To accurately assess the signal-to-noise ratio on both the LTQ and LCQ without the confounding effect of chemical interferences, Glu-fibrinopeptide was infused continuously at a concentration of 10 fmol/µl and 1000 tandem mass spectra were acquired on both instruments for the doubly charged precursor. The mean intensity from these data should reflect an accurate measure of the signal and the standard deviation of the intensity reflects an accurate measure of the noise. 12,13 To minimize the effect of ion source fluctuations as a major source of noise, 12 different fragment ions of different intensity were chosen to assess the effect of signal-to-noise ratios from the tandem mass spectra between the two mass spectrometers. Figure 3 shows a log-log plot of the experimentally measured mean signal versus signal-to-noise ratios for both instruments; Figure 3A shows the data for the LCQ XP Maxm and Figure 3B shows the data for the LTQ. The LTQ has a greater signal-tonoise ratio for each monitored fragment ion when compared to the LCQ. This improved signal-to-noise ratio ranged from 6.5 at the lowest intensity to 2.2 at the highest intensity. Noise from most mass spectrometer systems is composed of constant noise that is independent of signal, noise that is directly proportional to signal, and shot noise that is proportional to the square root of the number of ions measured. 12 The shot noise is defined by Poisson s statistics and represents the ultimate limiting factor of any ion current measurement system. Assuming that the mass spectrometry signal intensity is an accurate reflection of the number of ions measured, the shot noise limit can be estimated by the square root of the signal. The dashed line shows the theoretical best case signal-to-noise ratio as defined by Poisson s statistics, and the solid line with markers shows the experimentally obtained signal-to-noise ratio at different signal intensities. For the LCQ, the experimentally derived signal-to-noise data parallels the theoretically derived line; the difference in magnitude between the two lines is because the intensity is not an accurate reflection (12) Peterson, D. W., Hayes, J. M., Hercules, D. M., Hieftje, G. M., Evenson, M. A., Eds. Contemporary Topics in Analytical and Clinical Chemistry; Plenum Press: New York, (13) MacCoss, M. J.; Toth, M. J.; Matthews, D. E. Anal. Chem. 2001, 73, Figure 3. Log-log graph of signal-to-noise ratios versus signal. Signal or intensity is unfortunately expressed in different units between the two mass spectrometers. While the signal intensity on the LTQ is an approximate measure of counts (ions per second) the signal intensity on the LCQ XP Max has an additional factor that appears to be a function of the analog to digital converter. Noise was estimated on both instruments from the standard deviation of the signal. The dashed line shows the theoretical signal-to-noise ratio, and the dotted line shows the experimentally obtained ratio. (A) Signal-to-noise ratio vs signal for the LCQ XP Max. Measured signal-to-noise ratio on the LCQ XP Max parallels the predicted response based on Poisson s statistics. Offset between the two lines is likely a function of the arbitrary intensity units and not the mass spectrometer performance. (B) Signal-to-noise vs signal for the LTQ. At low intensities, the signalto-noise ratio is similar to that predicted by Poisson s statistics, whereas at higher intensities, the line diverges from the maximum signal-to-noise. The signal-to-noise ratio was higher for the LTQ than the LCQ XP Max. This signal-to-noise improvement ranged from 6.5 for the low-intensity fragment ion to 2.2 for the high-intensity fragment ion. of the number of ions measured but instead a measure of ions multiplied by a constant factor. 13 Nevertheless, this figure illustrates that because the measured noise is parallel to the limits predicted by Poisson s statistics, the LCQ is largely shot noise limited and constant/signal independent sources of noise are not a significant contributor to the total mass spectrometer noise measurement. In Figure 3B, the effect of signal on signal-to-noise ratios on the LTQ mass spectrometer is reported. The initial observation is that, unlike the LCQ XP Max, the LTQ has an intensity scale that is a better measure of ion counts. Additionally, at lower ion intensities, the signal-to-noise ratio parallels the shot noise limit, whereas at higher intensities, the signal-to-noise ratio begins to decrease and deviate from the maximum theoretical signal-to-noise limit. This deviation from the Poisson s noise prediction at higher ion counts indicates noise sources that are directly proportional to the increase in signal. This observation is common at higher ion counts 12 and is likely a result of the systematic noise becoming E

6 Figure 4. Effect of the minimum number of peptides required per protein (A) and the required peptide cleavage specificity (B) on the number of proteins and the observed false discovery rate. Results from the LCQ XP Max are shown using a broken line, and results from the LTQ are shown using a solid line. All data are expressed as mean ( SE. a larger fraction of the total noise. The absence of noise that is directly proportional to signal in the LCQ XP Max data is not because it does not exist, but because there are insufficient ions for it to become the predominant source of noise. Thus, although the LTQ has a higher signal-to-noise ratio for every monitored fragment ion, this improvement is more pronounced at low intensity than at high intensity. Effect of Database Searching Thresholds in Discriminating between True and False Discoveries Using Spectra from the LCQ XP Max and LTQ. A common approach for the analysis of database searching results is to apply a combination of different threshold criteria that a match must exceed using software like DTASelect 14 or Interact. 15 More recently, approaches have been developed that use machine learning techniques such as linear discriminant analysis 16 or support vector machines 17 that exploit the weighted combination of multiple criteria instead of requiring that every individual criterion be exceeded. Nevertheless, a basic assumption is that the weights for the combined criteria or the thresholds for each respective criterion will be transferable between data sets. Although we have shown that the LTQ provides increased peptide and protein identifications with respect to the LCQ XP Max, the common score thresholds used to discriminate between true and false discoveries from the SEQUEST database searching algorithm provide equal sensitivity and specificity from data acquired on the respective mass spectrometer. This suggests that it is reasonable and appropriate to use the same thresholds when evaluating data acquired between the LCQ XP Max and the LTQ. (14) Tabb, D. L.; McDonald, W. H.; Yates, J. R., III. J. Proteome Res. 2002, 1, 21. (15) Han, D. K.; Eng, J.; Zhou, H.; Aebersold, R. Nat. Biotechnol. 2001, 19, 946. (16) Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R. Anal. Chem. 2002, 74, (17) Anderson, D. C.; Li, W.; Payan, D. G.; Noble, W. S. J. Proteome Res. 2003, 2, 137. Figures 4 and 5 demonstrate the changes in the number of protein identifications and corresponding changes in FDR. For determining the effect of parameter value variation on the number of protein identifications and FDR, each parameter, with the exception of the parameter being varied, was set as follows: normalized cross-correlation cutoff for +1, +2, and +3 peptides of 0.25, DeltCN value of 0.08, include only fully tryptic peptides, a minimum of one peptide identification per locus, a minimum peptide length of 4, remove orthogonal identifications and remove ambiguous identifications. These filters could be obtained using the following DTASelect command line options: d y 2-p 1-o -Smn 4 -a false. Figure 4A shows the number of protein identifications and the respective false discovery rate for each instrument as a function of the number of peptides required per protein. As demonstrated previously, 3,16 the protein FDR drops dramatically as multiple peptides are required as a threshold cutoff for each identification. Interestingly, while the number of proteins identified is much higher for the LTQ than the LCQ XP Max, the FDR is indistinguishable between the two instruments for the same SEQUEST thresholds. Although all protein database searches were performed without any protein cleavage specificity, it is not uncommon to require peptides to have partial or full specificity during the filtering of the database search results. Because a spurious incorrect peptide identification should occur randomly throughout the sequences in the database, the FDR will be dramatically reduced with minimal reduction in true positive protein identifications. Figure 4B shows an accompanying drop in FDR from 64% for the LTQ and 74% for the LCQ when no enzyme specificity is required to 2% for both instruments as peptides without tryptic cleavage specificity. Although these data suggest that there is little specificity for database searches without filtering on tryptic cleavage status, a similar improvement in specificity can be F

7 Figure 5. Comparison of normalized XCorr (A), DeltaCN (B), and fraction of the predicted fragment ions (C) on the ability to discriminate between true and false protein identifications using LTQ (solid lines) and LCQ XP Max (broken lines) tandem mass spectra. All data are expressed as mean ( SE. obtained without tryptic specificity by requiring multiple peptides per loci as demonstrated in Figure 4A. The most widely used criterion for filtering SEQUEST results is the resulting XCorr score. Figure 5A shows the effect of increasing normalized XCorr on the number of protein identifications and the respective protein FDR. An increase in the normalized XCorr results in a decrease in FDR on both instruments. As expected, the increasing the threshold XCorr results in a greater decrease in FDR than protein identifications. For both the LTQ and LCQ XP Max, there is no difference in the database search result sensitivity (true discovery rate) when the FDR is >2%. For really high specificity database search results (FDR <2%), the LTQ has superior search sensitivity over the LCQ XP Max. This difference in sensitivity at very low FDRs is probably reflective of the increase in the overall tandem mass spectrum quality between the LTQ and the LCQ. Figure 5B shows the effect of DeltaCN on the sensitivity and specificity of protein identifications between the two instruments. The DeltaCN is the difference between the normalized crosscorrelation score between the first and second ranked sequence. Variation of this parameter reduces the number of protein identifications the least compared to variation of the other SEQUEST score thresholds. Furthermore, raising the threshold DeltaCN has a greater effect on reducing the FDR than the normalized XCorr. The magnitude of the DeltaCN has been shown to correspond to the likelihood that a given database search result is correct and has also been shown to be the most predictive feature to discriminate between true and false discoveries. 17 Though a DeltCN value of 0.1 is generally accepted, 3,18 an increase in DeltCN can decrease the FDR without substantially decreasing the number of protein identifications. We observed no statistical difference in the ability to differentiate between the true and false discoveries using either the LTQ or the LCQ XP Max. Another output from the SEQUEST database search that can be used to discriminate between the true and false protein discoveries is the fraction of predicted B- and Y-ions from the peptide sequence that were observed in the experimentally obtained tandem mass spectrum. Figure 5C shows the effect of increasing the required fraction of observed ions on the number of identified proteins and the FDR. Using this threshold, there is a dramatic reduction in FDR for both instruments in going between 0.2 and 0.4. However, at higher thresholds for the fraction of the predicted ions observed, the LCQ XP Max spectra had a greater sensitivity in maintaining the true protein identifications than the LTQ spectra at the very low FDRs (<0.2%). CONCLUSION The ThermoElectron LTQ outperforms the LCQ XP Max for qualitative protein identification from complex peptide mixtures. The LTQ identified more than 5-fold more proteins than the LCQ from replicate analyses of the same sample. The LTQ also had higher sequence coverage for identified proteins, with some proteins having 65% sequence coverage, while the LCQ XP Max had at most 55% sequence coverage. The LTQ identified a higher (18) Yates, J. R.; Eng, J. K.; McCormack, A. L. Anal. Chem. 1995, 67, G

8 percentage of proteins with more than five spectra and identified more proteins translated from low-abundance transcripts than the LCQ. One consequence to consider is that the increase in number of spectra obtained from the LTQ results in much larger files and significantly increased computational overhead. This overhead can obviously be minimized on the LTQ by increasing the number of microscans averaged per spectrumsthis will result in fewer, higher-quality spectra. However, for some laboratories, cost may be a deciding factor and the LCQ is still a robust instrument offering excellent performance for simpler mixtures at a fraction of the cost. This paper has demonstrated that the increased trapping efficiency, ion capacity, and faster scan rate of the LTQ results in more protein identifications, better identification of lowabundance proteins, and higher confidence protein identifications when compared with a LCQ XP Max. ACKNOWLEDGMENT The E. coli strain (OP50) used in these experiments was provided by James Thomas at the University of Washington. C.C.W. especially thanks an anonymous reviewer for his/her insightful suggestions regarding manuscript content. Financial support for this work was provided by National Institutes of Health Grants K22-AI (C.C.W.) and P41-RR (M.J.M.). Received for review August 17, Accepted December 14, AC051486A H PAGE EST: 7.2