Modification Site Localization Scoring Integrated into a Search Engine
2011; Elsevier BV; Volume: 10; Issue: 7 Linguagem: Inglês
10.1074/mcp.m111.008078
ISSN1535-9484
AutoresPeter R. Baker, Jonathan C. Trinidad, Robert J. Chalkley,
Tópico(s)Genomics and Phylogenetic Studies
ResumoLarge proteomic data sets identifying hundreds or thousands of modified peptides are becoming increasingly common in the literature. Several methods for assessing the reliability of peptide identifications both at the individual peptide or data set level have become established. However, tools for measuring the confidence of modification site assignments are sparse and are not often employed. A few tools for estimating phosphorylation site assignment reliabilities have been developed, but these are not integral to a search engine, so require a particular search engine output for a second step of processing. They may also require use of a particular fragmentation method and are mostly only applicable for phosphorylation analysis, rather than post-translational modifications analysis in general. In this study, we present the performance of site assignment scoring that is directly integrated into the search engine Protein Prospector, which allows site assignment reliability to be automatically reported for all modifications present in an identified peptide. It clearly indicates when a site assignment is ambiguous (and if so, between which residues), and reports an assignment score that can be translated into a reliability measure for individual site assignments. Large proteomic data sets identifying hundreds or thousands of modified peptides are becoming increasingly common in the literature. Several methods for assessing the reliability of peptide identifications both at the individual peptide or data set level have become established. However, tools for measuring the confidence of modification site assignments are sparse and are not often employed. A few tools for estimating phosphorylation site assignment reliabilities have been developed, but these are not integral to a search engine, so require a particular search engine output for a second step of processing. They may also require use of a particular fragmentation method and are mostly only applicable for phosphorylation analysis, rather than post-translational modifications analysis in general. In this study, we present the performance of site assignment scoring that is directly integrated into the search engine Protein Prospector, which allows site assignment reliability to be automatically reported for all modifications present in an identified peptide. It clearly indicates when a site assignment is ambiguous (and if so, between which residues), and reports an assignment score that can be translated into a reliability measure for individual site assignments. Proteomic research is increasingly moving from simply cataloging proteins to trying to understand which components are most important for regulation and function (1Zhao Y. Jensen O.N. Modification-specific proteomics: strategies for characterization of post-translational modifications using enrichment techniques.Proteomics. 2009; 9: 4632-4641Crossref PubMed Scopus (261) Google Scholar, 2Witze E.S. Old W.M. Resing K.A. Ahn N.G. Mapping protein post-translational modifications with mass spectrometry.Nat. Methods. 2007; 4: 798-806Crossref PubMed Scopus (590) Google Scholar). Protein activity can be controlled over the long term by changes in expression levels, but for rapid and precise changes, the cell employs a range of post-translational modifications (PTMs) 1The abbreviations used are:PTMpost-translational modificationCIDcollision induced dissociationFLRfalse localization rateSLIPSite Localization In PeptideQTOFquadrupole time-of-flight.. Mass spectrometry is the enabling tool for PTM characterization, as it is the only approach that can study thousands of modification sites in a single experiment (3Nilsson T. Mann M. Aebersold R. Yates 3rd, J.R. Bairoch A. Bergeron J.J. Mass spectrometry in high-throughput proteomics: ready for the big time.Nat. Methods. 2010; 7: 681-685Crossref PubMed Scopus (379) Google Scholar). Modern mass spectrometers are able to produce large amounts of data in relatively short periods of time, such that the bottleneck in most proteomic research is the data analysis (4Nesvizhskii A.I. Vitek O. Aebersold R. Analysis and validation of proteomic data generated by tandem mass spectrometry.Nat. Methods. 2007; 4: 787-797Crossref PubMed Scopus (507) Google Scholar). post-translational modification collision induced dissociation false localization rate Site Localization In Peptide quadrupole time-of-flight. There is a broad spectrum of mass spectrometry search engines that can be employed for data analysis (5Nesvizhskii A.I. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.J. Proteomics. 2010; 73: 2092-2123Crossref PubMed Scopus (369) Google Scholar), and from most of these programs a measure of reliability for individual peptide identifications is reported, commonly in the form of a probability or expectation value. These calculations determine how much better than random a particular assignment is. As a modified peptide with an incorrect modification site assignment is highly homologous to the correct answer, assignments to the correct peptide sequence but with incorrect site assignment will generally give a confident identification score. Hence, although these tools can be applied for analyzing peptides bearing chemical or biological modifications they will report some incorrect modification site assignments and no search engine currently reports a measure of reliability for the assignment of a site of modification within a peptide. To address this issue, a range of tools has been written to try to assess phosphorylation site assignments from search engine results (6Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2749) Google Scholar, 7Beausoleil S.A. Villen J. Gerber S.A. Rush J. Gygi S.P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization.Nat Biotechnol. 2006; 24: 1285-1292Crossref PubMed Scopus (1175) Google Scholar, 8Bailey C.M. Sweet S.M. Cunningham D.L. Zeller M. Heath J.K. Cooper H.J. SLoMo: automated site localization of modifications from ETD/ECD mass spectra.J. Proteome Res. 2009; 8: 1965-1971Crossref PubMed Scopus (81) Google Scholar, 9Ruttenberg B.E. Pisitkun T. Knepper M.A. Hoffert J.D. PhosphoScore: an open-source phosphorylation site assignment tool for MSn data.J. Proteome Res. 2008; 7: 3054-3059Crossref PubMed Scopus (79) Google Scholar, 10Savitski M.M. Lemeer S. Boesche M. Lang M. Mathieson T. Bantscheff M. Kuster B. Confident phosphorylation site localization using the Mascot Delta Score.Mol. Cell Proteomics. 2011; Abstract Full Text Full Text PDF Scopus (220) Google Scholar). Phosphorylation is an obvious modification on which to focus on developing tools, as in addition to it being arguably the most important biological regulatory modification, it is also a modification that can occur on a broad spectrum of amino acid residues, although it is most commonly found on serines, threonines, and tyrosines. Typical peptides produced from proteolytic digests of proteins will contain multiple potential sites of phosphorylation, so there is a clear need for software that can determine how reliably a particular assignment is pinpointed. Most of these tools take outputs from a particular search engine, then look for diagnostic fragment ions that would distinguish between potential sites of modification. Probably the best known of these is Ascore (7Beausoleil S.A. Villen J. Gerber S.A. Rush J. Gygi S.P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization.Nat Biotechnol. 2006; 24: 1285-1292Crossref PubMed Scopus (1175) Google Scholar), which calculates a probability for a site assignment by working out how many b or y ions could be observed to distinguish between potential sites, and what percentage of these were observed in the particular spectrum. However, this software was designed only for low mass accuracy ion trap collision induced dissociation (CID) data and requires an output from a particular version of the search engine Sequest (11Eng J.K. McCormack A.L. Yates J.R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.J. Am. Soc.. Mass Spectrm. 1994; 5: 976-989Crossref PubMed Scopus (5314) Google Scholar). Determining site assignment reliabilities directly from a search engine result has a number of advantages. It allows site assignment scoring for all data types that can be analyzed by the software. It also permits determining scores for all modifications; not just phosphorylation. Two groups have used results from the search engine Mascot (12Perkins D.N. Pappin D.J. Creasy D.M. Cottrell J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data.Electrophoresis. 1999; 20: 3551-3567Crossref PubMed Scopus (6661) Google Scholar) to report confidences for site assignments based on the difference in score between a particular site assignment and the next highest scoring modified version of the same peptide (10Savitski M.M. Lemeer S. Boesche M. Lang M. Mathieson T. Bantscheff M. Kuster B. Confident phosphorylation site localization using the Mascot Delta Score.Mol. Cell Proteomics. 2011; Abstract Full Text Full Text PDF Scopus (220) Google Scholar, 13Mischerikow N. Altelaar A.F. Navarro J.D. Mohammed S. Heck A.J. Comparative assessment of site assignments in CID and electron transfer dissociation spectra of phosphopeptides discloses limited relocation of phosphate groups.Mol. Cell Proteomics. 2010; 9: 2140-2148Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar). This information is not displayed directly in the search engine output, but software has been written to parse information out from links contained in the search engine results. In the study by Savitski et al., the authors created a range of fragmentation data from analyzing synthetic phosphopeptides where the modification sites were known, which allowed determination of a phosphorylation site false localization rate (FLR). In this manuscript we present Site Localization In Peptide (SLIP) scoring that is automatically calculated for all modifications identified in peptides identified by the search engine Batch-Tag in the Protein Prospector suite of tools (14Chalkley R.J. Baker P.R. Medzihradszky K.F. Lynn A.J. Burlingame A.L. In-depth analysis of tandem mass spectrometry data from disparate instrument types.Mol. Cell Proteomics. 2008; 7: 2386-2398Abstract Full Text Full Text PDF PubMed Scopus (140) Google Scholar). To characterize its performance, we compare it to the results using alternative tools AScore and Mascot Delta Score and we also test it using a larger phosphopeptide data set, determining FLRs associated with a given site score. The quadrupole fragmentation data was acquired using a quadrupole time-of-flight (QTOF) Micro (Waters, Milford, MA) mass spectrometer and the details describing the sample creation and data acquisition are described in the publication where the data set was created (10Savitski M.M. Lemeer S. Boesche M. Lang M. Mathieson T. Bantscheff M. Kuster B. Confident phosphorylation site localization using the Mascot Delta Score.Mol. Cell Proteomics. 2011; Abstract Full Text Full Text PDF Scopus (220) Google Scholar). Briefly, 180 phosphopeptides were synthesized bearing a mixture of phosphorylated serine, threonine, and tyrosine residues. The majority of them were singly phosphorylated, but some doubly phosphorylated peptides were also present. Each peptide was analyzed separately by liquid chromatography (LC)-tandem MS (MSMS), then all the data from the 180 peptides were combined for data analysis. The raw data and peak lists created from this data are freely available for download from Tranche (https://proteomecommons.org/tranche/). The phosphopeptide data for assessing ion trap fragmentation spectra was derived from a tryptic digestion of mouse synaptosomes. Mouse synaptosomes were resuspended in 1 ml buffer containing 50 mm ammonium bicarbonate, 6 m guanidine hydrochloride 6× Roche Phosphatase Inhibitor Cocktails I and II, and 6× PUGNAc inhibitor. The mixture was reduced with 2 mm Tris(2-carboxyethyl)phosphine hydrochloride and alkylated 4.2 mm iodoacetamide. The mixture was diluted to 1 m guanidine with ammonium bicarbonate and digested for 12 h at 37 °C with 1:50 (w/w) trypsin. Phosphorylated peptides were enriched over an analytical guard column packed with 5 μm titanium dioxide beads (GL Sciences, Tokyo, Japan). Peptides were loaded and washed in 20% acetonitrile/1% trifluoroacetic acid then eluted with saturated KH2PO4 followed by 5% phosphoric acid. High pH reverse phase chromatography was performed using an ÄKTA Purifier (GE Healthcare, Piscataway, NJ) equipped with a 1 × 100 mm Gemini 3μ C18 column (Phenomenex, Torrance, CA). Buffer A consisted of (20 mm ammonium formate, pH 10). Buffer B consisted of buffer A with 50% acetonitrile. The gradient went from 1% B 100% B over 6.5 mls. Twenty fractions were collected and dried down using a SpeedVac concentrator. Phosphopeptides were separated by low pH reverse phase chromatography using a NanoAcquity (Waters) interfaced to an LTQ-Orbitrap Velos (Thermo) mass spectrometer. Precursor masses were measured in the Orbitrap. All MS/MS data was acquired using CID in the linear ion trap and all fragment masses were measured using the linear ion trap. Data was searched using Protein Prospector version 5.8.0 (14Chalkley R.J. Baker P.R. Medzihradszky K.F. Lynn A.J. Burlingame A.L. In-depth analysis of tandem mass spectrometry data from disparate instrument types.Mol. Cell Proteomics. 2008; 7: 2386-2398Abstract Full Text Full Text PDF PubMed Scopus (140) Google Scholar). QTOF Micro data was searched against a concatenated database of SwissProt downloaded on August 10th 2010 and randomized versions of these entries. Only human entries were considered, meaning a total of 40574 entries were queried. Fully tryptic cleavage specificity was assumed, all cysteines were assumed to be carbamidomethylated and possible modifications considered included oxidation (M); Gln->pyro-Glu (peptide N-term); Met-loss, acetylation and the combination thereof (protein N-term); and phosphorylation (S, T, or Y). Precursor masses were considered with ± 200 ppm mass tolerance and fragments with ± 0.4 Da tolerance. The results are presented in supplemental Table S1. Peak lists for LTQ-Velos data were created using in-house software “PAVA.” LTQ-Velos data was searched against a list of 3794 rodent proteins and randomized versions thereof that are all found in UniprotKB downloaded on August 10th 2010. Fully tryptic cleavage specificity was assumed and all cysteines were considered to be carbamidomethylated. A precursor mass tolerance of 15 ppm and fragment mass tolerance of 0.6 Da were permitted. Two searches were performed. In each case the same variable modifications were considered as for the QTOF data set described above. However, in one search phosphorylation of proline was also considered; in the other phosphorylation of glutamic acid was considered. Batch-Tag identifies both phosphorylated fragment ions and those for which phosphoric acid has been eliminated. Therefore, to fully simulate other potential sites of phosphorylation the code was adapted to consider phosphate loss from glutamates and prolines that were assigned as phosphorylated. Results were filtered to a 0.1% spectrum false discovery rate (FDR) according to target-decoy database searching. SLIP scores are determined by comparing probability and expectation values for the same peptide with different site assignments. As a default, Protein Prospector was only saving the top five matches to each spectrum, and in some cases the match to the same peptide with the next best modification site assignment was not in these results (e.g. if the other potential modification site is at the other end of the peptide). Hence, the manner in which the results were saved was altered so that the score for the next best site assignment is always stored for all of the top five peptide matches to a particular spectrum. The SLIP score is derived by comparing the probability or expectation value for each peptide to the next best match of the same peptide but with different site assignment (the difference between probabilities and expectation values will be identical as the expectation value is the probability multiplied by a constant (number of precursors in the database within the precursor mass tolerance)). This difference is converted into a simple integer score by converting into Log10 scale and then multiplying by −10. This transformation is analogous to that used by the Mascot search engine for converting its probability scores into Mascot scores (12Perkins D.N. Pappin D.J. Creasy D.M. Cottrell J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data.Electrophoresis. 1999; 20: 3551-3567Crossref PubMed Scopus (6661) Google Scholar). Under this conversion, a SLIP score of 10 means an order of magnitude difference in probability scores for the different site assignments whereas a SLIP score of 5 corresponds to about a sevenfold higher confidence for a peptide assignment. A previous study compared the performance of the Mascot Delta Score to the alternative phosphorylation site scoring software AScore (10Savitski M.M. Lemeer S. Boesche M. Lang M. Mathieson T. Bantscheff M. Kuster B. Confident phosphorylation site localization using the Mascot Delta Score.Mol. Cell Proteomics. 2011; Abstract Full Text Full Text PDF Scopus (220) Google Scholar). Generously, they made the raw data produced for this comparison freely available. Hence, this allows new tools for phosphorylation site assignment to be benchmarked against these results. The data set given the most focus in the previous study was acquired on a QTOF Micro mass spectrometer, so we decided to employ Batch-Tag for analyzing this same data set, then assessed the SLIP scoring integrated into the Search Compare output to determine site assignment reliability. The results are summarized in the first column of Table I and the full results are supplemental Table S1. Search Compare reported 2334 correct peptide identifications, containing 2437 phosphorylation site assignments. Of these, 164 peptides contained only one possible site of modification, so site assignment scoring was not relevant. A further 220 sites were reported as completely ambiguous; i.e. multiple sites achieved exactly the same score. In the remaining 2053 cases, one possible site assignment scored better than the others, so a SLIP score was reported. Of these, 130 of the site assignments were incorrect, corresponding to a 6.3% FLR. The previously reported result for Ascore when analyzing this data set was 1584 IDs with 138 incorrect (9% FLR) and 1840 with 201 incorrect (11% FLR) for Mascot Delta Score.Table IComparison of different peak list filtering approaches. Three different peak list filtering approaches are compared for their ability to identify phosphopeptides and pinpoint modification sites20 + 204 per 1005 per 100Spectra233423782282Phosphosites243724882397Correct192419101883Incorrect130136161Ambiguous220255211One Site164187142FLR (%)6.36.67.9Ambiguous (%)9.010.28.8 Open table in a new tab Results were broken down by SLIP score to try to determine a reliability estimate for a given SLIP score. Fig. 1 presents score histograms for correct and incorrect site assignments and the corresponding plots for Ascore and Mascot Delta Score are also presented. In Fig. 1D a plot of the FLR against score is shown. This plot is quite noisy, because of the fact that there are only 130 incorrect results (and only 32 with a score of three or greater), but it suggests that a SLIP score of six corresponds to a local FLR of about 5%; i.e. 5% of results with a SLIP score of exactly six are incorrect. When raw data is converted into a peak list for analysis by a database search engine there is typically no peak detection step during the process; i.e. the peak list contains a mixture of real and noise peaks. Hence, most search engines employ a filtering step to try to maximize the number of real peaks while minimizing the number of noise peaks considered. Batch-Tag in Protein Prospector normally splits the observed mass range into two, then uses the 20 most intense peaks in each half of the mass range (a total of forty peaks if there are at least 20 peaks in each half of the mass range) for database searching (15Chalkley R.J. Baker P.R. Huang L. Hansen K.C. Allen N.P. Rexach M. Burlingame A.L. Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight mass spectrometer: II. New developments in Protein Prospector allow for reliable and comprehensive automatic analysis of large datasets.Mol. Cell Proteomics. 2005; 4: 1194-1204Abstract Full Text Full Text PDF PubMed Scopus (144) Google Scholar). Ascore uses a more extensive binning approach where it considers a fixed number of peaks per 100 m/z, and varies this value to try to optimize site assignment discrimination (7Beausoleil S.A. Villen J. Gerber S.A. Rush J. Gygi S.P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization.Nat Biotechnol. 2006; 24: 1285-1292Crossref PubMed Scopus (1175) Google Scholar). We decided to investigate the effect of using a 100 m/z binning approach, comparing it to the standard Batch-Tag 20 + 20 approach, assessing it both for peptide identification and site assignment reliability. Values from three to nine peaks per 100 m/z were investigated. The results for 20 + 20, four per 100 and five per 100 are presented in Table I. Using four peaks per 100 identified marginally more spectra than the other two analyses, but had the highest number of ambiguous modification site assignments. Considering five peaks per 100 identified the fewest number of both spectra and sites and made the most mistakes, partly because it reported fewer sites as being ambiguous. The 20 + 20 results correctly identified the most sites and gave the most reliable site assignments. Determining the accuracy of site assignments can be difficult, as it requires knowledge of the correct answer, which is practically never the case when analyzing large, biologically derived data sets. The results presented so far were created from synthetic peptides with known sites of modification, but there were only 130 incorrect results (using the 20 + 20 peak filtering approach) from which to measure the FLR. Therefore, to create a larger data set for testing, and also to test a different fragmentation data type, we employed an alternative strategy for assessing site assignment scoring. We temporarily changed the settings in Batch-Tag such that it would consider phosphorylation of proline and glutamic acid residues. We also allowed it to consider loss of phosphoric acid from these amino acids. Hence, we were able to perform searches considering phosphorylation of two amino acids that if assigned as the site of modification, would always be incorrect. We analyzed a large phosphopeptide data set acquired using ion trap CID fragmentation on an LTQ-Orbitrap Velos mass spectrometer. Two searches were performed: in the first we considered phosphorylation of S, T, Y, and P; in the second search we considered phosphorylation of S, T, Y, and E. In each search, over 90,000 spectra were identified at an estimated 0.1% false discovery rate according to searching against a concatenated normal/random database (16Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2726) Google Scholar). Of these spectra, roughly 60,000 were of phosphopeptides. For testing site assignment scoring we wanted spectra where the correct site assignment is known. Hence, we restricted our subsequent analysis to those spectra matched to phosphopeptides where there was only one serine, threonine, or tyrosine in the peptide sequence. For the “phosphoproline” search results there were 5433 of these peptide identifications, and for the “phosphoglutamate” results there were 5415. Of these, 361 and 245 respectively reported phosphorylations on the relevant decoy amino acid, corresponding to 6.6% and 4.5% global FLRs. Fig. 2 plots false localization rates against SLIP score for the two different search results. As can be seen, very similar results were produced for the two decoy amino acid searches, and as in the QTOF Micro data analysis results, a SLIP score of six corresponded to a 5% local FLR. To test the performance of the SLIP scoring for a third type of fragmentation and also for a different regulatory PTM we re-analyzed data used in a previous publication studying O-GlcNAc modified peptides in mouse brain (17Chalkley R.J. Thalhammer A. Schoepfer R. Burlingame A.L. Identification of protein O-GlcNAcylation sites using electron transfer dissociation mass spectrometry on native peptides.Proc. Natl. Acad. Sci. U. S. A. 2009; 106: 8894-8899Crossref PubMed Scopus (192) Google Scholar). In this study 58 sites of O-GlcNAc modification were identified from roughly eighty modified peptide spectra. Clearly, with this limited number of modified peptide spectra it is not possible to accurately calculate a FLR for a particular SLIP score. However, it is possible to examine spectra with assignments of a particular SLIP score and make general comments about the amount of evidence supporting the site assignment. Fig. 3 compares site assignments in a peptide from Ankyrin G that contains two O-GlcNAc (HexNAc) modifications. One modification is assigned to the seventh residue in the peptide (middle threonine) with a SLIP score of nine; the second to the tenth residue (final serine) with a SLIP score of three. The next best assignment alternative to residue 10 is residue nine. Assignments that are unique to serine 9 are shown in blue, whereas assignments unique to serine 10 are in green. A peak at m/z 1024.97 matches a c-19 ion containing no modification, which supports the assignment of serine 10 as the modification site. Conversely, the peak at 1228.18 would correspond to a modified c-19 ion. However, this peak can also be explained as a z+19 ion from either site assignment interpretation. Hence, the assignment of serine 10 explains more peaks, but there is some level of ambiguity, which is consistent with the assignment only getting a SLIP score of three. The assignment of modification to threonine 7 is supported by the mass difference between z+15 and z+16 ions corresponding to a modified threonine. Hence, this interpretation explains an extra z+1 ion in comparison to site assignments on neighboring residues. z+1 ions are very common in ETD spectra of doubly charged precursors (18Chalkley R.J. Medzihradszky K.F. Lynn A.J. Baker P.R. Burlingame A.L. Statistical analysis of Peptide electron transfer dissociation fragmentation mass spectrometry.Anal. Chem. 2010; 82: 579-584Crossref PubMed Scopus (51) Google Scholar) and Protein Prospector scoring takes this into consideration (19Baker P.R. Medzihradszky K.F. Chalkley R.J. Improving software performance for peptide ETD data analysis by implementation of charge-state and sequence-dependent scoring.Mol. Cell Proteomics. 2010; Abstract Full Text Full Text PDF Scopus (44) Google Scholar). Therefore, this extra peak assignment gives a SLIP score of 9. A second example is shown in supplemental Fig. S1 for a spectrum with a SLIP score of five. In this example there is some evidence to suggest that the spectrum may be a mixture of two different site assignments, but the mass difference between z+19 and z+110 strongly suggests modification of the second most N-terminal serine in the sequence. Mass spectrometry instrumentation has improved dramatically in the last 15 years, such that it can now produce a vast amount of high quality data. This puts tremendous pressure on database search engines to be able to analyze these large data sets and produce reliable, unsupervised peptide and protein identification summaries. Initially, there was a period in which results of uncertain reliability were being produced and published. However, through pressure from the community and proteomic journals (20Bradshaw R.A. Burlingame A.L. Carr S. Aebersold R. Reporting protein identification data: the next generation of guidelines.Mol. Cell Proteomics. 2006; 5: 787-788Abstract Full Text Full Text PDF PubMed Scopus (198) Google Scholar), software has now “caught up” such that reliability metrics are now associated with most published peptide sequence identifications. PTM analysis is progressing through a similar cycle where the ability to reliably identify modified peptides is ahead of the software's ability to measure the confidence in the modification site assignment (21Bradshaw R.A. Medzihradszky K.F. Chalkley R.J. Protein PTMs: post-translational modifications or pesky trouble makers?.J. Mass Spectrom. 2010; 45: 1095-1097Crossref PubMed Scopus (10) Google Scholar). However, again spearheaded by pressure from journals, programmers are busily developing tools to report reliabilities for site modifications reported. Most of these are stand-alone tools that re-analyze spectra for site assignment reliability using peptide identification results from a particular search engine as the reference. However, there are now attempts to use the search engine results directly to report
Referência(s)