Confident Peptide Identifications Research Articles

Improved Identification of Endogenous Peptides from Murine Nervous Tissue by Multiplexed Peptide Extraction Methods and Multiplexed Mass Spectrometric Analysis

In recent years, mass spectrometry (MS) based techniques have made their entrance in the analysis of endogenous peptides extracted from nervous tissue. In this study, we introduce a novel peptide extraction procedure using 8 M urea, next to the more established extraction method that uses acetic acid. The extracted peptide mixtures are analyzed by both high-resolution nanoLC MS/MS using collision induced dissociation (CID) on an LTQ-Orbitrap and nanoLC electron transfer induced dissociation (ETD) on a linear ion trap. The combined use of the two extraction methods significantly increased the yield of identified endogenous neuropeptides. The multiplexed use of high mass accuracy mass spectrometry and the ETD fragmentation technique further increased the yield and confidence of peptide identifications. Furthermore, reduction of disulfide bridges during sample preparation was essential in the identification of several endogenous peptides containing cysteine disulfide bonds. Through this study, we identified in total 142 peptides in extracts of the mouse pituitary tissue, whereby 43 uniquely in the urea extract and 11 uniquely in the acetic acid extract. A large number of detected endogenous peptides were reported previously, but we confidently identified 22 unreported peptides that possess characteristics of endogenous peptides and are thus interesting targets to be explored further.

Side Chain Chemistry Mediates Backbone Fragmentation in Hydrogen Deficient Peptide Radicals

A crown ether based, photolabile radical precursor which forms noncovalent complexes with peptides has been prepared. The peptide/precursor complexes can be electrosprayed, isolated in an ion trap, and then subjected to laser photolysis and collision induced dissociation to generate hydrogen deficient peptide radicals. It is demonstrated that these peptide radicals behave very differently from the hydrogen rich peptide radicals generated by electron capture methods. In fact, it is shown that side chain chemistry dictates both the occurrence and relative abundance of backbone fragments that are observed. Fragmentation at aromatic residues occurs preferentially over most other amino acids. The origin of this selectivity relates to the mechanism by which backbone dissociation is initiated. The first step is abstraction of a beta-hydrogen from the side chain, followed by beta-elimination to yield primarily a-type fragment ions. Calculations reveal that those side chains which can easily lose a beta-hydrogen correlate well with experimentally favored sites for backbone fragmentation. In addition, radical mediated side chain losses from the parent peptide are frequently observed. Eleven amino acids exhibit unique mass losses from side chains which positively identify that particular amino acid as part of the parent peptide. Therefore, side chain losses allow one to unambiguously narrow the possible sequences for a parent peptide, which when combined with predictable backbone fragmentation should lead to greatly increased confidence in peptide identification.

Identification of Marker Peptides in Digested Gelatins by High Performance Liquid Chromatography/Mass Spectrometry

Sequence analysis of bovine and porcine collagen type I indicates that some partial sequences are specific for bovine or porcine collagen type I, which can be used as marker peptides for gelatin differentiation. Bovine and porcine gelatins were digested with trypsin. The digest mixtures were analyzed with high performance liquid chromatography/mass spectrometry ( HPLC/MS ). Marker peptides were identified by the method of peptide sequence tag. The results indicated that the bovine and porcine. gelatins could be differentiated according to the sequences of marker peptides. Further analysis indicated that some prolines in the marker peptides were hydroxylated, which should be taken into account to increase the confidence in peptide identification. The relative abundance of marker peptides in gelatin of different molecular weight range was also investigated. The results indicate that identification of the marker peptides in the digested gelatins by HPLC/MS is an alternative method for differentiation of bovine and porcine gelatins.

Application of electron transfer dissociation (ETD) for the analysis of posttranslational modifications

Despite major advantages in the field of proteomics, the analysis of PTMs still poses a major challenge; thus far, preventing insights into the role and regulation of protein networks. Additionally, top-down sequencing of proteins is another powerful approach to reveal comprehensive information for biological function. A commonly used fragmentation technique in MS-based peptide sequencing is CID. As CID often fails in PTM-analysis and performs best on doubly-charged, short and middle-sized peptides, confident peptide identification may be hampered. A newly developed fragmentation technique, namely electron transfer dissociation (ETD), supports both, PTM- and top-down analysis, and generally results in more confident identification of long, highly charged or modified peptides. The following review presents the theoretical background of ETD and its technical implementation in mass analyzers. Furthermore, current improvements of ETD and approaches for the PTM-analysis and top-down sequencing are introduced. Alternating both fragmentation techniques, ETD and CID, increases the amount of information derived from peptide fragmentation, thereby enhancing both, peptide sequence coverage and the confidence of peptide and protein identification.

Characterization of Strategies for Obtaining Confident Identifications in Bottom-Up Proteomics Measurements Using Hybrid FTMS Instruments

Hybrid FTMS instruments, such as the LTQ-FT and LTQ-Orbitrap, are capable of generating high duty cycle linear ion trap MS/MS data along with high resolution information without compromising the overall throughput of measurements. Combined with online LC separations, these instruments provide powerful capabilities for proteomics research. In the present work, we explore three alternative strategies for high throughput proteomics measurements using hybrid FTMS instruments. Our accurate mass and time tag (AMT tag) strategy enables identification of thousands of peptides in a single LC-FTMS analysis by comparing accurate molecular mass and LC elution time information from the analysis to a reference database. An alternative strategy considered here, termed accurate precursor mass filter (APMF), employs linear ion trap (low resolution) MS/MS identifications generated by an appropriate search engine, such as SEQUEST, refined with high resolution precursor ion data obtained from FTMS mass spectra. The APMF results can be additionally filtered using the LC elution time information from the AMT tag database, which constitutes a precursor mass and time filter (PMTF), the third approach implemented in this study. Both the APMF and the PMTF approaches are evaluated for coverage and confidence of peptide identifications and contrasted with the AMT tag strategy. The commonly used decoy database method and an alternative method based on mass accuracy histograms were used to reliably quantify identification confidence, revealing that both methods yielded similar results. Comparison of the AMT, APMF and PMTF approaches indicates that the AMT tag approach is preferential for studies desiring a highest achievable number of identified peptides. In contrast, the APMF approach does not require an AMT tag database and provides a moderate level of peptide coverage combined with acceptable confidence values of approximately 99%. The PMTF approach yielded a significantly better peptide identification confidence, >99.9%, that essentially excluded any false peptide identifications. Since AMT tag databases that exclude incorrect identifications are desirable, this study points to the value of a multipass APMF approach to generate AMT tag databases, which are then validated using the PMTF approach. The resulting compact, high quality databases can then be used for subsequent high-throughput, high peptide coverage AMT tag studies.

Oscore: a combined score to reduce false negative rates for peptide identification in tandem mass spectrometry analysis

Tandem mass spectrometry (MS/MS) has been widely used in proteomics studies. Multiple algorithms have been developed for assessing matches between MS/MS spectra and peptide sequences in databases. However, it is still a challenge to reduce false negative rates without compromising the high confidence of peptide identification. In this study, we developed the score, Oscore, by logistic regression using SEQUEST and AMASS variables to identify fully tryptic peptides. Since these variables showed complicated association with each other, combining them together rather than applying them to a threshold model improved the classification of correct and incorrect peptide identifications. Oscore achieved both a lower false negative rate and a lower false positive rate than PeptideProphet on datasets from 18 known protein mixtures and several proteome-scale samples of different complexity, database size and separation methods. By a three-way comparison among Oscore, PeptideProphet and another logistic regression model which made use of PeptideProphet's variables, the main contributor for the improvement made by Oscore is discussed.

Enhancing Peptide Identification Confidence by Combining Search Methods

Confident peptide identification is one of the most important components in mass-spectrometry-based proteomics. We propose a method to properly combine the results from different database search methods to enhance the accuracy of peptide identifications. The database search methods included in our analysis are SEQUEST (v27 rev12), ProbID (v1.0), InsPecT (v20060505), Mascot (v2.1), X! Tandem (v2007.07.01.2), OMSSA (v2.0) and RAId_DbS. Using two data sets, one collected in profile mode and one collected in centroid mode, we tested the search performance of all 21 combinations of two search methods as well as all 35 possible combinations of three search methods. The results obtained from our study suggest that properly combining search methods does improve retrieval accuracy. In addition to performance results, we also describe the theoretical framework which in principle allows one to combine many independent scoring methods including de novo sequencing and spectral library searches. The correlations among different methods are also investigated in terms of common true positives, common false positives, and a global analysis. We find that the average correlation strength, between any pairwise combination of the seven methods studied, is usually smaller than the associated standard error. This indicates only weak correlation may be present among different methods and validates our approach in combining the search results. The usefulness of our approach is further confirmed by showing that the average cumulative number of false positive peptides agrees reasonably well with the combined E-value. The data related to this study are freely available upon request.

Monte Carlo Simulation-Based Algorithms for Analysis of Shotgun Proteomic Data

Two new statistical models based on Monte Carlo Simulation (MCS) have been developed to score peptide matches in shotgun proteomic data and incorporated in a database search program, MassMatrix (www.massmatrix.net). The first model evaluates peptide matches based on the total abundance of matched peaks in the experimental spectra. The second model evaluates amino acid residue tags within MS/MS spectra. The two models provide complementary scores for peptide matches that result in higher confidence in peptide identification when significant scores are returned from both models. The MCS-based models use a variance reduction technique that improves estimation precision. Due to the high computational expense of MCS-based models, peptide matches were prefiltered by other statistical models before further evaluation by the MCS-based models. Receiver operating characteristic analysis of the data sets confirmed that MCS-based models improved the overall performance of the MassMatrix search software, especially for low-mass accuracy data sets.

Proteome-wide identification of proteins and their modifications with decreased ambiguities and improved false discovery rates using unique sequence tags.

Identifying proteins and their modification states and with known levels of confidence remains as a significant challenge for proteomics. Random or decoy peptide databases are increasingly being used to estimate the false discovery rate (FDR), e.g., from liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses of tryptic digests. We show that this approach can significantly underestimate the FDR and describe an approach for more confident protein identifications that uses unique partial sequences derived from a combination of database searching and amino acid residue sequencing using high-accuracy MS/MS data. Applied to a Saccharomyces cerevisiae tryptic digest, the approach provided 3 132 confident peptide identifications ( approximately 5% modified in some fashion), covering 575 proteins with an estimated zero FDR. The conventional approach provided 3 359 peptide identifications and 656 proteins with 0.3% FDR based upon a decoy database analysis. However, the present approach revealed approximately 5% of the 3 359 identifications to be incorrect and many more as potentially ambiguous (e.g., due to not considering certain amino acid substitutions and modifications). In addition, 677 peptides and 39 proteins were identified that had been missed by conventional analysis, including nontryptic peptides, peptides with a variety of expected/unexpected chemical modifications, known/unknown post-translational modifications, single nucleotide polymorphisms or gene encoding errors, and multiple modifications of individual peptides.

Investigating MS2/MS3 Matching Statistics: A Model For Coupling Consecutive Stage Mass Spectrometry Data For Increased Peptide Identification Confidence

Improvements in ion trap instrumentation have made n-dimensional mass spectrometry more practical. The overall goal of the study was to describe a model for making use of MS(2) and MS(3) information in mass spectrometry experiments. We present a statistical model for adjusting peptide identification probabilities based on the combined information obtained by coupling peptide assignments of consecutive MS(2) and MS(3) spectra. Using two data sets, a mixture of known proteins and a complex phosphopeptide-enriched sample, we demonstrate an increase in discriminating power of the adjusted probabilities compared with models using MS(2) or MS(3) data only. This work also addresses the overall value of generating MS(3) data as compared with an MS(2)-only approach with a focus on the analysis of phosphopeptide data.

Modes of Inference for Evaluating the Confidence of Peptide Identifications

Several modes of inference are currently used in practice to evaluate the confidence of putative peptide identifications resulting from database scoring algorithms such as Mascot, SEQUEST, or X!Tandem. The approaches include parametric methods, such as classic PeptideProphet, and distribution free methods, such as methods based on reverse or decoy databases. Because of its parametric nature, classic PeptideProphet, although highly robust, was not highly flexible and was difficult to apply to new search algorithms or classification scores. While commonly applied, the decoy approach has not yet been fully formalized and standardized. And, although they are distribution-free, they like other approaches are not free of assumptions. Recent manuscripts by Kall et al., Choi and Nesvizhskii, and Choi et al. help advance these methods, specifically by formalizing an alternative formulation of decoy databases approaches and extending the PeptideProphet methods to make explicit use of decoy databases, respectively. Taken together with standardized decoy database methods, and expectation scores computed by search engines like Tandem, there exist at least four different modes of inference used to assign confidence levels to individual peptides or groups of peptides. We overview and compare the assumptions of each of these approaches and summarize some interpretation issues. We also discuss some suggestions, which may make the use of decoy databases more computationally efficient in practice.

Semi-supervised learning for peptide identification from shotgun proteomics datasets

Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.

Capillary scale monolithic trap column for desalting and preconcentration of peptides and proteins in one- and two-dimensional separations

Monolithic columns based on poly-(styrene-divinylbenzene) (PS-DVB) were utilized both for preconcentration (in 10 mm × 0.20 mm I.D. format) and analytical separation (in 60 mm × 0.20 and 0.10 mm I.D. format) of peptides and proteins in column switching micro-scale high-performance liquid chromatography. A special holder for short monolithic preconcentration columns was designed and pressure durability tests approved long-term stability up to 400 bar. An 11–20% decrease in the average peak widths of nine peptides was obtained upon combining a preconcentration column with an analytical column as compared with a setup using an analytical column only. Trapping efficiency, especially for small and hydrophilic peptides, was optimized by using 0.10% heptafluorobutyric acid instead of 0.050% trifluoroacetic acid as solvent additive during sample loading. Using a 10 mm × 0.20 mm I.D. preconcentration column, loadabilities between 0.5 and 1.6 μg were determined by frontal analysis of proteins and bioactive peptides, respectively. A 100-fold concentration followed by direct on-line intact mass determination is demonstrated for diluted (3 μmol L −1) protein solutions. The applicability of the monolithic preconcentration column for multidimensional chromatography was tested by off-line two-dimensional separation, combining strong cation-exchange chromatography and ion-pair reversed-phase chromatography. Peptide identification data from digested protein mixtures demonstrated reproducibilities of 46–75% in triplicate analyses, and confident peptide identifications of low abundant peptides even in the presence of a 650-fold molar excess of high abundant peptides.

The Mycobacterial Thioredoxin Peroxidase Can Act as a One-cysteine Peroxiredoxin

Thioredoxin peroxidase (TPx) has been reported to dominate the defense against H(2)O(2), other hydroperoxides, and peroxynitrite at the expense of thioredoxin (Trx) B and C in Mycobacterium tuberculosis (Mt). By homology, the enzyme has been classified as an atypical 2-C-peroxiredoxin (Prx), with Cys(60) as the "peroxidatic" cysteine (C(P)) forming a complex catalytic center with Cys(93) as the "resolving" cysteine (C(R)). Site-directed mutagenesis confirms Cys(60) to be C(P) and Cys(80) to be catalytically irrelevant. Replacing Cys(93) with serine leads to fast inactivation as seen by conventional activity determination, which is associated with oxidation of Cys(60) to a sulfinic acid derivative. However, in comparative stopped-flow analysis, WT-MtTPx and MtTPx C93S reduce peroxynitrite and react with TrxB and -C similarly fast. Reduction of pre-oxidized WT-MtTPx and MtTPx C93S by MtTrxB is demonstrated by monitoring the redox-dependent tryptophan fluorescence of MtTrxB. Furthermore, MtTPx C93S remains stable for 10 min at a morpholinosydnonimine hydrochloride-generated low flux of peroxynitrite and excess MtTrxB in a dihydrorhodamine oxidation model. Liquid chromatography-tandem mass spectrometry analysis revealed disulfide bridges between Cys(60) and Cys(93) and between Cys(60) and Cys(80) in oxidized WT-MtTPx. Reaction of pre-oxidized WT-MtTPx and MtTPx C93S with MtTrxB C34S or MtTrxC C40S yielded dead-end intermediates in which the Trx mutants are preferentially linked via disulfide bonds to Cys(60) and never to Cys(93) of the TPx. It is concluded that neither Cys(80) nor Cys(93) is required for the catalytic cycle of the peroxidase. Instead, MtTPx can react as a 1-C-Prx with Cys(60) being the site of attack for both the oxidizing and the reducing substrate. The role of Cys(93) is likely to conserve the oxidation equivalents of the sulfenic acid state of C(P) as a disulfide bond to prevent overoxidation of Cys(60) under a restricted supply of reducing substrate.

Optimization and Use of Peptide Mass Measurement Accuracy in Shotgun Proteomics

Mass spectrometers that provide high mass accuracy such as FT-ICR instruments are increasingly used in proteomic studies. Although the importance of accurately determined molecular masses for the identification of biomolecules is generally accepted, its role in the analysis of shotgun proteomic data has not been thoroughly studied. To gain insight into this role, we used a hybrid linear quadrupole ion trap/FT-ICR (LTQ FT) mass spectrometer for LC-MS/MS analysis of a highly complex peptide mixture derived from a fraction of the yeast proteome. We applied three data-dependent MS/MS acquisition methods. The FT-ICR part of the hybrid mass spectrometer was either not exploited, used only for survey MS scans, or also used for acquiring selected ion monitoring scans to optimize mass accuracy. MS/MS data were assigned with the SEQUEST algorithm, and peptide identifications were validated by estimating the number of incorrect assignments using the composite target/decoy database search strategy. We developed a simple mass calibration strategy exploiting polydimethylcyclosiloxane background ions as calibrant ions. This strategy allowed us to substantially improve mass accuracy without reducing the number of MS/MS spectra acquired in an LC-MS/MS run. The benefits of high mass accuracy were greatest for assigning MS/MS spectra with low signal-to-noise ratios and for assigning phosphopeptides. Confident peptide identification rates from these data sets could be doubled by the use of mass accuracy information. It was also shown that improving mass accuracy at a cost to the MS/MS acquisition rate substantially lowered the sensitivity of LC-MS/MS analyses. The use of FT-ICR selected ion monitoring scans to maximize mass accuracy reduced the number of protein identifications by 40%.

Different Immunoaffinity Fractionation Strategies to Characterize the Human Plasma Proteome

Plasma proteins may often serve as indicators of disease and are a rich source for biomarker discovery. However, the intrinsic large dynamic range of plasma proteins makes the analysis very challenging because a large number of low abundance proteins are often masked by a few high abundance proteins. The use of prefractionation methods, such as depletion of higher abundance proteins before protein profiling, can assist in the discovery and detection of less abundant proteins that may ultimately prove to be informative biomarkers. But there are few studies on comprehensive investigation of the proteins both in the fractions depleted and remainder. In the present study, two different immunoaffinity fractionation columns for the top-6 or the top-12 proteins in plasma were investigated and both the proteins in column-bound and flow-through fractions were subsequently analyzed. A two-dimensional peptide separation strategy, utilizing chromatographic separation techniques, combined with tandem mass spectrometry (MS/MS) was employed for proteomic analysis of the four fractions. Using the established HUPO PPP criteria, a total of 2401 unique plasma proteins were identified. The Multiple Affinity Removal System yielded 921 and 725 unique proteins from the flow-through and bound fractions, respectively, whereas the Seppro MIXED 12 column yielded identification of 897 and 730 unique proteins from the flow-through and bound fractions, respectively. When more stringent criteria, based on searching against the reversed database, were implemented, 529 unique proteins were identified from the four fractions with the confidence in peptide identification increased from 73.6% to 99%. To determine whether the presence of nontarget proteins in the immunoaffinity-bound fraction could be attributed to their interaction with high abundance proteins, co-immunoprecipitation analysis with an antibody to human plasma albumin was performed, which resulted in an identification of 40 unique proteins from the coimmunoprecipitate with the more stringent criteria. This study illustrated that combining the column-bound and flow-through fractions from immunoaffinity separation affords more extensive profiling of the protein content of human plasma. The presence of nontarget proteins in the column-bound fractions may be induced by their binding to the higher abundance proteins targeted by the immunoaffinity column.

Quality classification of tandem mass spectrometry data

Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.

Added Value for Tandem Mass Spectrometry Shotgun Proteomics Data Validation through Isoelectric Focusing of Peptides

A very popular approach in proteomics is the so-called "shotgun LC-MS/MS" strategy. In its mostly used form, a total protein digest is separated by ion exchange fractionation in the first dimension followed by off- or on-line RP LC-MS/MS. We replaced the first dimension by isoelectric focusing in the liquid phase using the Off-Gel device producing 15 fractions. As peptides are separated by their isoelectric point in the first dimension and hydrophobicity in the second, those experimentally derived parameters (pI and R(T)) can be used for the validation of potentially identified peptides. We applied this strategy to a cellular extract of Drosophila Kc167 cells and identified peptides with two different database search engines, namely PHENYX and SEQUEST, with PeptideProphet validation of the SEQUEST results. PHENYX returned 7582 potential peptide identifications and SEQUEST 7629. The SEQUEST results were reduced to 2006 identifications by validation with PeptideProphet. Validation of the PeptideProphet, SEQUEST and PHENYX results by pI and R(T) parameters confirmed 1837 PeptideProphet identifications while in the remainder of the SEQUEST results another 1130 peptides were found to be likely hits. The validation on PHENYX resulted in the fixation of a solid p-value threshold of <1 x 10(-04) that sets by itself the correct identification confidence to >95%, and a final count of 2034 highly confident peptide identifications was achieved after pI and R(T) validation. Although the PeptideProphet and PHENYX datasets have a very high confidence the overlap of common identifications was only at 79.4%, to be explained by the fact that data interpretation was done searching different protein databases with two search engines of different algorithms. The approach used in this study allowed for an automated and improved data validation process for shotgun proteomics projects producing MS/MS peptide identification results of very high confidence.

Rapid enrichment of bioactive milk proteins and iterative, consolidated protein identification by multidimensional protein identification technology

Direct injection of complex protein mixtures, e.g. those derived from crude biological fluids, is often incompatible with conventional LC supports, because of column clogging and rapid deterioration of chromatographic performance. In this paper, we report the use of restricted access media to rapidly enrich and fractionate human breast milk. This resin, combining size exclusion and anion exchange functionalities, yielded a fraction enriched in soluble CD14 and showing specific sCD14-dependant activity. This fraction was split into five aliquots, which were individually characterized using multidimensional protein identification technology. Reproducibility of the results was addressed by analysing and comparing five datasets using different protein identification tools available within the Sequest software. Furthermore, a comparison of three major releases of the Ensembl human protein database was performed to examine the effect of database updates on our results. We report here the benefit of repeated analysis of aliquots of the same fraction: first to increase the confidence in peptide identification by repeated confirmation in several aliquots; and second to assess experimental reproducibility. We demonstrate furthermore the effect of database modifications on the results and the importance of constantly re-analysing data with new releases to keep them consistent and up to date with the latest protein identities and predictions available.

Prediction of Peptide Retention at Different HPLC Conditions from Multiple Linear Regression Models

To quantitatively characterize the structure of a peptide and to predict its gradient retention time at given HPLC conditions three structural descriptors are used: (i) logarithm of the sum of retention times of the amino acids composing the peptide, log SumAA, (ii) logarithm of the van der Waals volume of the peptide, log VDW(Vol), (iii) and the logarithm of the peptide's calculated n-octanol-water partition coefficient, clog P. The log SumAA descriptor is obtained from empirical data for 20 natural amino acids, determined in a given HPLC system. The two other descriptors are calculated from the peptides' structural formulas using molecular modeling methods. The quantitative structure-retention relationships (QSRR), build by multiple linear regression, describe HPLC retention of peptide on a given chromatographic system on which the retention of the 20 amino acids was predetermined. A structurally diversified series of 98 peptides was employed. The predicted gradient retention times on several chromatographic systems were in good agreement with the experimental data. The QSRR equations, derived for a given system operated at variable gradient times and temperatures allowed for the prediction of peptide retention in that system. Matching the experimental HPLC retention to the theoretically predicted for a presumed peptide could facilitate original protein identification in proteomics. In conjunction with MS data, prediction of the retention time for a given peptide might be used to improve the confidence of peptide identifications and to increase the number of correctly identified peptides.