Identification and Phenotypic Characterization of Sphingomonas wittichii Strain RW1 by Peptide Mass Fingerprinting Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry

Mass spectrometry is a potentially attractive means of monitoring the survival and efficacy of bioaugmentation agents, such as the dioxin-mineralizing bacterium Sphingomonas wittichii strain RW1. The biotransformation activity of RW1 phenotypes is determined primarily by the presence and concentration of the dioxin dioxygenase, an enzyme initiating the degradation of both dibenzo-p-dioxin and dibenzofuran (DF). We explored the possibility of identifying and characterizing putative cultures of RW1 by peptide mass fingerprinting (PMF) targeting this characteristic phenotypic biomarker. The proteome from cells of RW1—grown on various media in the presence and absence of DF—was partially purified, tryptically digested, and analyzed using matrix-assisted laser desorption ionization-time of flight mass spectrometry. Mascot online database queries allowed statistically significant identification of RW1 in disrupted, digested cells (P < 0.01 to 0.05) and in digested whole-cell extracts (P < 0.00001 to 0.05) containing hundreds of proteins, as determined by two-dimensional gel electrophoresis. Up to 14 peptide ions of the alpha subunit of the dioxin dioxygenase (43% protein coverage) were detected in individual samples. A minimum of 10⁷ DF-grown cells was required to identify dioxin degradation-enabled phenotypes. The technique hinges on the detection of multiple characteristic peptides of a biomarker that can reveal at once the identity and phenotypic properties of the microbial host expressing the protein. The results demonstrate the power of PMF of minimally processed microbial cultures as a sensitive and specific technique for the positive identification and phenotypic characterization of certain microorganisms used in biotechnology and bioremediation.     

Systematic characterization of high mass accuracy influence on false discovery and probability scoring in peptide mass fingerprinting

Whereas the bearing of mass measurement error on protein identification is sometimes underestimated, uncertainty in observed peptide masses unavoidably translates to ambiguity in subsequent protein identifications. Although ongoing instrumental advances continue to make high accuracy mass spectrometry (MS) increasingly accessible, many proteomics experiments are still conducted with rather large mass error tolerances. In addition, the ranking schemes of most protein identification algorithms do not include a meaningful incorporation of mass measurement error. This article provides a critical evaluation of mass error tolerance as it pertains to false positive peptide and protein associations resulting from peptide mass fingerprint (PMF) database searching. High accuracy, high resolution PMFs of several model proteins were obtained using matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR-MS). Varying levels of mass accuracy were simulated by systematically modulating the mass error tolerance of the PMF query and monitoring the effect on figures of merit indicating the PMF quality. Importantly, the benefits of decreased mass error tolerance are not manifest in Mowse scores when operating at tolerances in the low parts-per-million range but become apparent with the consideration of additional metrics that are often overlooked. Furthermore, the outcomes of these experiments support the concept that false discovery is closely tied to mass measurement error in PMF analysis. Clear establishment of this relation demonstrates the need for mass error-aware protein identification routines and argues for a more prominent contribution of high accuracy mass measurement to proteomic science.     

Evaluating peptide mass fingerprinting-based protein identification

Identification of proteins by mass spectrometry (MS) is an essential step in proteomic studies and is typically accomplished by either peptide mass fingerprinting (PMF) or amino acid sequencing of the peptide. Although sequence information from MS/MS analysis can be used to validate PMF-based protein identification, it may not be practical when analyzing a large number of proteins and when high- throughput MS/MS instrumentation is not readily available. At present, a vast majority of proteomic studies employ PMF. However, there are huge disparities in criteria used to identify proteins using PMF. Therefore, to reduce incorrect protein identification using PMF, and also to increase confidence in PMF-based protein identification without accompanying MS/MS analysis, definitive guiding principles are essential. To this end, we propose a value-based scoring system that provides guidance on evaluating when PMF-based protein identification can be deemed sufficient without accompanying amino acid sequence data from MS/MS analysis.     

Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into account in the datamining process to analyze their influence on the peak intensity. Furthermore, this datamining approach can certainly be extended to the tandem mass spectrometry domain or other mass spectrometry derived methods.     

Combination of a SAW-biosensor with MALDI mass spectrometric analysis

A S-sens K5 surface acoustic wave biosensor was coupled with mass spectrometry (SAW-MS) for the analysis of a protein complex consisting of human blood clotting cascade factor alpha-thrombin and human antithrombin III, a specific blood plasma inhibitor of thrombin. Specific binding of antithrombin III to thrombin was recorded as a function of time with a S-sens K5 biosensor. Two out of five elements of the sensor chip were used as references. To the remaining three elements coated with RNA anti-thrombin aptamers, thrombin and antithrombin III were bound consecutively. The biosensor measures mass changes on the chip surface showing that 20% of about 400fmol/cm2 thrombin formed a complex with the 1.7-times larger antithrombin III. Mass spectrometry (MS) was applied to identify the bound proteins. Sensor chips with aptamer-captured (1) thrombin and (2) thrombin-antithrombin III complex (TAT-complex) were digested with proteases on the sensor element and subsequently identified by peptide mass fingerprint (PMF) with matrix assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry. A significant identification of thrombin was achieved by measuring the entire digest with MALDI-ToF MS directly from the sensor chip surface. For the significant identification of both proteins in the TAT-complex, the proteolytic peptides had to be separated by nano-capillary-HPLC prior to MALDI-ToF MS. SAW-MS is applicable to protein interaction analysis as in functional proteomics and to miniaturized diagnostics.     

Improved mass spectrometric identification of gel-separated hydrophobic membrane proteins after sodium dodecyl sulfate removal by ion-pair extraction

Separation and identification of hydrophobic membrane proteins is a major challenge in proteomics. Identification of such sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE)-separated proteins by peptide mass fingerprinting (PMF) via matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) is frequently hampered by the insufficient amount of peptides being generated and their low signal intensity. Using the seven helical transmembrane-spanning proton pump bacteriorhodopsin as model protein, we demonstrate here that SDS removal from hydrophobic proteins by ion-pair extraction prior to in-gel tryptic proteolysis leads to a tenfold higher sensitivity in mass spectrometric identification via PMF, with respect to initial protein load on SDS-PAGE. Furthermore, parallel sequencing of the generated peptides by electrospray ionization-mass spectrometry (ESI-MS) and tandem mass spectrometry (MS/MS) was possible without further sample cleanup. We also show identification of other membrane proteins by this protocol, as proof of general applicability.     

Isolation and characterization of dibenzofuran-degrading bacteria

Two bacterial strains capable of utilizing dibenzofuran (DF) as a sole carbon source were isolated from soil samples of reclaimed land. The strains designated HL1 and HL7 were identified as Klebsiella sp. and Sphingomonas sp., respectively, on the basis of biochemical characteristics and the sequences of the 16S ribosomal DNA. Sphingomonas sp. strain HL7 degraded non-, mono- and also dichlorinated DF and dibenzo-p-dioxin (DD). Klebsiella sp. strain HL1 was able to degrade non- and monochlorinated DFs and DDs, but not dichlorinated ones. The metabolites formed from DF by strains HL1 and HL7 were similar to those by dioxin-degrading bacteria Sphingomonas sp. strain RW1 except for salicylic acid and catechol. Strain HL7 had a gene homologous to that encoding the dioxin dioxygenase alpha-subunit (dxnA1) gene of Sphingomonas sp. strain RW1. However, Southern hybridization analysis showed that the size of an EcoRV-digested genomic fragment involving the dioxin dioxygenase gene of strain HL7 was smaller than that of strain RW1, and that strain HL1 did not have the homologous gene. Strains HL1 and HL7 provided useful information regarding the dioxygenase genes.     

High-accuracy peptide mass fingerprinting using peak intensity data with machine learning

For MALDI-TOF mass spectrometry, we show that the intensity of a peptide-ion peak is directly correlated with its sequence, with the residues M, H, P, R, and L having the most substantial effect on ionization. We developed a machine learning approach that exploits this relationship to significantly improve peptide mass fingerprint (PMF) accuracy based on training data sets from both true-positive and false-positive PMF searches. The model's cross-validated accuracy in distinguishing real versus false-positive database search results is 91%, rivaling the accuracy of MS/MS-based protein identification.     

Feature-matching pattern-based support vector machines for robust peptide mass fingerprinting

Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF. We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available "gold standard" peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification. Several conclusions reached via this research make general contributions to MS-based protein identification. Firstly, inherent attributes showed comparable or even greater robustness than other explicit. As an inherent attribute of an experimental spectrum, peak intensity should receive considerable attention during protein identification. Secondly, alignment between intense experimental peaks and properly digested, unique or non-modified theoretical peptides is very likely to occur in positive peptide mass fingerprinting. Finally, normalization by several types of harmonic factors, including missed cleavages and mass modification, can make important contributions to the performance of the procedure.     

Enhanced peptide mass fingerprinting through high mass accuracy: Exclusion of non-peptide signals based on residual mass

Peptide mass fingerprinting (PMF) is among the principle methods of contemporary proteomic analysis. While PMF is routinely practiced in many laboratories, the complexity of protein tryptic digests is such that PMF based on unrefined mass spectrometric peak lists is often inconclusive. A number of data processing strategies have thus been designed to improve the quality of PMF peak lists, and the development of increasingly elaborate tools for PMF data reduction remains an active area of research. In this report, a novel and direct means of PMF peak list enhancement is suggested. Since the monoisotopic mass of a peptide must fall within a predictable range of residual values, PMF peak lists can in principle be relieved of many non-peptide signals solely on the basis of accurately determined monoisotopic mass. The calculations involved are relatively simple, making implementation of this scheme computationally facile. When this procedure for peak list processing was used, the large number of unassigned masses typical of PMF peak lists was considerably attenuated. As a result, protein identifications could be made with greater confidence and improved discrimination as compared to PMF queries submitted with raw peak lists. Importantly, this scheme for removal of non-peptide masses was found to conserve peptides bearing various post-translational and artificial modifications. All PMF experiments discussed here were performed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), which provided the high mass resolution and high mass accuracy essential for this application. Previously reported equations relating the nominal peptide mass to the permissible range of fractional peptide masses were slightly modified for this application, and these adjustments have been illustrated in detail. The role of mass accuracy in application of this scheme has also been explored.     

The essence on mass spectrometry based microbial diagnostics

In recent years, matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry has become an important bioanalytical method to detect profiles of proteins and peptides derived from whole bacterial cells. This accurate molecular-phenotypic method can be easily applied to robustly detect bacteria on the genus, species and in some cases on the subspecies level. Standardised laboratory protocols for the preparation of abundant bacterial proteins and the development of tailored data analysis software, as well as high-quality databases of microbial reference mass spectra, made the procedure attractive to replace phenotypic or biochemical procedures for identification of bacteria and other microorganisms. Moreover, genotypic and functional mass spectrometry based methods to detect for example bacterial strains or antibiotic resistance may become useful in the coming years. In general, mass spectrometry is a powerful tool to facilitate routine microbial diagnostics.     

A strategy to improve peptide mass fingerprinting matches through the optimization of matrix-assisted laser desorption/ionization matrix selection and formulation

Peptide mass fingerprinting (PMF) is a powerful technique in which experimentally measured m/z values of peptides that result from a protein digest form the basis for a characteristic fingerprint of the intact protein. Due to its propensity to generate singly-charged ions, along with its relative insensitivity to salts and buffers, matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is the MS method of choice for PMF. The qualitative features of a MALDI-MS mass spectrum can be selectively tuned by varying the matrix and the solvent system used to prepare the matrix. The selective tuning of MALDI-MS mass spectra in order to optimize PMF results is addressed in this paper. Carbonic anhydrase, hemoglobin alpha- and beta-chain, and myoglobin were digested with trypsin, and the digest was analyzed with MALDI-MS. 2,5-Dihydroxybenzoic acid (2,5-DHB) and alpha-cyano-4-hydroxycinnamic acid were prepared, using five different solvent systems: (A) 99% acetone; (B) 50% acetonitrile (ACN), 0.1% trifluoroacetic acid (TFA); (C) 75% ACN, 0.1% TFA; (D) formic acid:H(2)O: 2-propanol (1:3:2); and (E) H(2)O:MeOH (2:1). Each protein was found to have a different optimum solvent system for PMF. Generally, better PMF results were obtained with 2,5-DHB. The best PMF results were obtained when all of the mass spectral data for a particular protein digest were convolved.     

Evaluation of algorithms for protein identification from sequence databases using mass spectrometry data

In this work, the commonly used algorithms for mass spectrometry based protein identification, Mascot, MS-Fit, ProFound and SEQUEST, were studied in respect to the selectivity and sensitivity of their searches. The influence of various search parameters were also investigated. Approximately 6600 searches were performed using different search engines with several search parameters to establish a statistical basis. The applied mass spectrometric data set was chosen from a current proteome study. The huge amount of data could only be handled with computational assistance. We present a software solution for fully automated triggering of several peptide mass fingerprinting (PMF) and peptide fragmentation fingerprinting (PFF) algorithms. The development of this high-throughput method made an intensive evaluation based on data acquired in a typical proteome project possible. Previous evaluations of PMF and PFF algorithms were mainly based on simulations.     

Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics

Liver cirrhosis is a worldwide health problem. Reliable, noninvasive methods for early detection of liver cirrhosis are not available. Using a three-step approach, we classified sera from rats with liver cirrhosis following different treatment insults. The approach consisted of: (i) protein profiling using surface-enhanced laser desorption/ionization (SELDI) technology; (ii) selection of a statistically significant serum biomarker set using machine learning algorithms; and (iii) identification of selected serum biomarkers by peptide sequencing. We generated serum protein profiles from three groups of rats: (i) normal (n=8), (ii) thioacetamide-induced liver cirrhosis (n=22), and (iii) bile duct ligation-induced liver fibrosis (n=5) using a weak cation exchanger surface. Profiling data were further analyzed by a recursive support vector machine algorithm to select a panel of statistically significant biomarkers for class prediction. Sensitivity and specificity of classification using the selected protein marker set were higher than 92%. A consistently down-regulated 3495 Da protein in cirrhosis samples was one of the selected significant biomarkers. This 3495 Da protein was purified on-chip and trypsin digested. Further structural characterization of this biomarkers candidate was done by using cross-platform matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) peptide mass fingerprinting (PMF) and matrix-assisted laser desorption/ionization time of flight/time of flight (MALDI-TOF/TOF) tandem mass spectrometry (MS/MS). Combined data from PMF and MS/MS spectra of two tryptic peptides suggested that this 3495 Da protein shared homology to a histidine-rich glycoprotein. These results demonstrated a novel approach to discovery of new biomarkers for early detection of liver cirrhosis and classification of liver diseases.     

Protein separation and characterization by np-RP-HPLC followed by intact MALDI-TOF mass spectrometry and peptide mass mapping analyses

Because of their complexity, the separation of intact proteins from complex mixtures is an important step to comparative proteomics and the identification and characterization of the proteins by mass spectrometry (MS). In the study reported, we evaluated the use of nonporous-reversed-phase (np-RP)-HPLC for intact protein separation prior to MS analyses. The separation system was characterized and compared to 1D-SDS-PAGE electrophoresis in terms of resolution and sensitivity. We demonstrate that np-RP-HPLC protein separation is highly reproducible and provides intact protein fractions which can be directly analyzed by MALDI-TOF-MS for intact molecular weight determination. An in-well digestion protocol was developed, allowing for rapid protein identification by peptide mass fingerprinting (PMF) and resulted in comparable or improved peptide recovery compared with in-gel digestion. The np-RP sensitivity of detection by UV absorbance at 214 nm for intact proteins was at the low ng level and the sensitivity of peptide analysis by MALDI-TOF-MS was in the 10-50 fmol level. A membrane protein fraction was characterized to demonstrate application of this methodology. Among the identified proteins, multiple forms of vimentin were observed. Overall, we demonstrate that np-RP-HPLC followed by MALDI-TOF-MS allows for rapid, sensitive, and reproducible protein fractionation and very specific protein characterization by integration of PMF analysis with MS intact molecular weight information.     

Peak bagging for peptide mass fingerprinting

MOTIVATION: Mass Spectrometry (MS)-based protein identification via peptide mass fingerprinting (PMF) is a key component in high-throughput proteome research. While PMF was the first commonly used protein identification method, provided higher throughput than the tandem MS-based method, its accuracy is lower than that of the tandem MS method. Thus, it is desirable to develop PMF-based algorithm with higher protein identification accuracy to facilitate proteome research. RESULTS: We propose a peak bagging method for single MS-based protein identification. It combines results from multiple PMF algorithms, where each PMF algorithm takes a random peak subset as input. Evaluation with a set of real MALDI-TOF MS spectra shows that the new peak bagging method provides consistent improvements over the single PMF algorithm.     

Protein identification and tracking in two-dimensional electrophoretic gels by minimal protein identifiers

Protein identification by matrix-assisted laser desorption/ionization mass-spectrometry peptide mass fingerprinting (MALDI-MS PMF) represents a cornerstone of proteomics. However, it often fails to identify low-molecular-mass proteins, protein fragments, and protein mixtures reliably. To overcome these limitations, PMF can be complemented by tandem mass spectrometry and other search strategies for unambiguous protein identification. The present study explores the advantages of using a MALDI-MS-based approach, designated minimal protein identifier (MPI) approach, for protein identification. This is illustrated for culture supernatant (CSN) proteins of Mycobacterium tuberculosis H37Rv after separation by two-dimensional gel electrophoresis (2-DE). The MPI approach takes into consideration that proteins yield characteristic peptides upon proteolytic cleavage. In this study, peptide mixtures derived from tryptic protein cleavage were analyzed by MALDI-MS and the resulting spectra were compared with template spectra of previously identified counterparts. The MPI approach allowed protein identification by few protein-specific signature peptide masses and revealed truncated variants of mycobacterial elongation factor EF-Tu, previously not identified by PMF. Furthermore, the MPI approach can be employed to track proteins in 2-DE gels, as demonstrated for the 14 kDa antigen, the 10 kDa chaperone, and the conserved hypothetical protein Rv0569 of M. tuberculosis H37Rv. Furthermore, it is shown that the power of the MPI approach strongly depends on distinct factors, most notably on the complexity of the proteome analyzed and accuracy of the mass spectrometer used for peptide mass determination.     

A second [2Fe-2S] ferredoxin from Sphingomonas sp. Strain RW1 can function as an electron donor for the dioxin dioxygenase

The first step in the degradation of dibenzofuran and dibenzo-p-dioxin by Sphingomonas sp. strain RW1 is carried out by dioxin dioxygenase (DxnA1A2), a ring-dihydroxylating enzyme. An open reading frame (fdx3) that could potentially specify a new ferredoxin has been identified downstream of dxnA1A2, a two-cistron gene (J. Armengaud, B. Happe, and K. N. Timmis, J. Bacteriol. 180:3954-3966, 1998). In the present study, we report a biochemical analysis of Fdx3 produced in Escherichia coli. This third ferredoxin thus far identified in Sphingomonas sp. strain RW1 contained a putidaredoxin-type [2Fe-2S] cluster which was characterized by UV-visible absorption spectrophotometry and electron paramagnetic resonance spectroscopy. The midpoint redox potential of this ferredoxin (E'(0) = -247 +/- 10 mV versus normal hydrogen electrode at pH 8.0) is similar to that exhibited by Fdx1 (-245 mV), a homologous ferredoxin previously characterized in Sphingomonas sp. strain RW1. In in vitro assays, Fdx3 can be reduced by RedA2 (a reductase similar to class I cytochrome P-450 reductases), previously isolated from Sphingomonas sp. strain RW1. RedA2 exhibits a K(m) value of 3.2 +/- 0.3 microM for Fdx3. In vivo coexpression of fdx3 and redA2 with dxnA1A2 confirmed that Fdx3 can serve as an electron donor for the dioxin dioxygenase.     

Evaluating Peptide Mass Fingerprinting-based Protein Identification

Identification of proteins by mass spectrometry (MS) is an essential step in pro- teomic studies and is typically accomplished by either peptide mass fingerprinting (PMF) or amino acid sequencing of the peptide. Although sequence information from MS/MS analysis can be used to validate PMF-based protein identification, it may not be practical when analyzing a large number of proteins and when high- throughput MS/MS instrumentation is not readily available. At present, a vast majority of proteomic studies employ PMF. However, there are huge disparities in criteria used to identify proteins using PMF. Therefore, to reduce incorrect protein identification using PMF, and also to increase confidence in PMF-based protein identification without accompanying MS/MS analysis, definitive guiding principles are essential. To this end, we propose a value-based scoring system that provides guidance on evaluating when PMF-based protein identification can be deemed sufficient without accompanying amino acid sequence data from MS/MS analysis.     

Selection of the peptide mass tolerance value for protein identification with peptide mass fingerprinting

Peptide mass fingerprinting (PMF) is widely used for protein identification while studying proteome via time-of-flight mass spectrometer or via 1D or 2D electrophoresis. Peptide mass tolerance indicating the fit of theoretical peptide mass to an experimental one signifcantly influences protein identification. The role of peptide mass tolerance could be estimated by counting the number of correctly identified proteins for the reference set of mass spectra. The reference set of 400 Ultraflex (Bruker Daltonics, Germany) protein mass spectra was obtained for liver microsomes slices hydrolyzed via 1D gel electrophoresis. Using a Mascot server for protein identification, the peptide mass tolerance value varied within 0.02–0.40 Da with a step of 0.01 Da. The number of identified proteins changed up to 10 times depending on the tolerance. The maximal number of identified proteins was reported for the tolerance value of 0.15 Da (120 ppm) known to be 1.5–2-fold higher than the recommended values for such a type of mass spectrometer. The software program PMFScan was developed to obtain the dependence between the number of identified proteins and the tolerance values.