Evaluation of parameters in peptide mass fingerprinting for protein identification by MALDI-TOF mass spectrometry

Protein identification by peptide mass fingerprinting, using the matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS), plays a major role in large proteome projects. In order to develop a simple and reliable method for protein identification by MALDI-TOF MS, we compared and evaluated the major steps in peptide mass fingerprinting. We found that the removal of excess enzyme from the in-gel digestion usually gave a few more peptide peaks, which were important for the identification of some proteins. Internal calibration always gave better results. However, for a large number of samples, two step calibrations (i.e. database search with peptide mass from external calibration, then the use of peptide masses from the search result as internal calibrants) were useful and convenient. From the evaluation and combination of steps that were already developed by others, we established a single overall procedure for peptide identification from a polyacrylamide gel.     

Calibration of mass spectrometric peptide mass fingerprint data without specific external or internal calibrants

Background  

Peptide Mass Fingerprinting (PMF) is a widely used mass spectrometry (MS) method of analysis of proteins and peptides. It relies on the comparison between experimentally determined and theoretical mass spectra. The PMF process requires calibration, usually performed with external or internal calibrants of known molecular masses.     

基于质谱和生物信息学分析的小菜蛾蛋白质鉴定

本研究以非模式昆虫小菜蛾Plutella xylostella为材料, 对比2, 3, 4龄幼虫的蛋白质组双向电泳图谱, 得到24个蛋白质差异点, 从中选取了编号为1111的差异表达蛋白质点进行质谱鉴定和生物信息学分析. 采用胶内酶解的多肽进行MALDI-TOF/TOF分析, 获得该点的肽质量指纹图谱（PMF）及串联质谱（MS/MS）图谱。将获得的PMF分别用MASCOT和ProFound等常用软件在NCBInr的Metazoa蛋白质数据库进行搜索, 匹配结果不理想. 进一步用PMF+MS/MS谱图搜索NCBInr的Metazoa蛋白质数据库, 以及小菜蛾EST数据库。 在NCBInr库中匹配结果为拟暗果蝇Drosophila pseudoobscura中的一种假定蛋白GA18218-PA, 而用EST库搜索的结果为家蚕Bombyx mori的ATP合酶的亚基。为验证搜索结果, 将该蛋白质点进行磺基异硫氰酸苯酯（SPITC）化学衍生后de novo测序, 最后确认该点可能为ATP合酶的一个亚基。最后着重讨论了蛋白质的质谱鉴定与生物信息学分析的联合使用, 希望据此选择出最适合于非模式昆虫蛋白质组学鉴定的方法。     

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.     

Protein identification from two-dimensional gel electrophoresis analysis of Klebsiella pneumoniae by combined use of mass spectrometry data and raw genome sequences

Separation of proteins by two-dimensional gel electrophoresis (2-DE) coupled with identification of proteins through peptide mass fingerprinting (PMF) by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is the widely used technique for proteomic analysis. This approach relies, however, on the presence of the proteins studied in public-accessible protein databases or the availability of annotated genome sequences of an organism. In this work, we investigated the reliability of using raw genome sequences for identifying proteins by PMF without the need of additional information such as amino acid sequences. The method is demonstrated for proteomic analysis of Klebsiella pneumoniae grown anaerobically on glycerol. For 197 spots excised from 2-DE gels and submitted for mass spectrometric analysis 164 spots were clearly identified as 122 individual proteins. 95% of the 164 spots can be successfully identified merely by using peptide mass fingerprints and a strain-specific protein database (ProtKpn) constructed from the raw genome sequences of K. pneumoniae. Cross-species protein searching in the public databases mainly resulted in the identification of 57% of the 66 high expressed protein spots in comparison to 97% by using the ProtKpn database. 10 dha regulon related proteins that are essential for the initial enzymatic steps of anaerobic glycerol metabolism were successfully identified using the ProtKpn database, whereas none of them could be identified by cross-species searching. In conclusion, the use of strain-specific protein database constructed from raw genome sequences makes it possible to reliably identify most of the proteins from 2-DE analysis simply through peptide mass fingerprinting.     

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.     

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.     

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.     

Prediction of missed cleavage sites in tryptic peptides aids protein identification in proteomics

Protein identification via peptide mass fingerprinting (PMF) remains a key component of high-throughput proteomics experiments in post-genomic science. Candidate protein identifications are made using bioinformatic tools from peptide peak lists obtained via mass spectrometry (MS). These algorithms rely on several search parameters, including the number of potential uncut peptide bonds matching the primary specificity of the hydrolytic enzyme used in the experiment. Typically, up to one of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using two distinct, nonredundant datasets of peptides identified via PMF and tandem MS, a simple predictive method based on information theory is presented which is able to identify experimentally defined missed cleavages with up to 90% accuracy from amino acid sequence alone. Using this simple protocol, we are able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines, using the PMF dataset as a test set. In addition, the improved approach is also demonstrated on an independent PMF data set of known proteins that also has corresponding high-quality tandem MS data, validating the protein identifications. This approach has wider applicability for proteomics database searching, and the program for predicting missed cleavages and masking Fasta-formatted protein sequence databases has been made available via http:// ispider.smith.man.ac uk/MissedCleave.     

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.     

Unmatched masses in peptide mass fingerprints caused by cross-contamination: an updated statistical result

Unmatched masses are often observed in the experimental peptide mass spectra when database searching is performed with the ProFound program. Comparison between theoretical and experimental mass spectra of standard proteins shows that contamination accounts for most of the unmatched masses. In this retrospective analysis, the top 100 most probable contaminating masses, as listed in order of their probability, are statistically filtered out from 118 different experimental peptide mass fingerprinting (PMF) maps. Most of the interfering masses originate from trypsin autolysis and human keratins. Subtraction of known contaminants from raw data and using cleaner masses for searching can enhance protein identification by PMF.     

Internal calibrants allow high accuracy peptide matching between MALDI imaging MS and LC-MS/MS

One of the important challenges for MALDI imaging mass spectrometry (MALDI-IMS) is the unambiguous identification of measured analytes. One way to do this is to match tryptic peptide MALDI-IMS m/z values with LC-MS/MS identified m/z values. Matching using current MALDI-TOF/TOF MS instruments is difficult due to the variability of in situ time-of-flight (TOF) m/z measurements. This variability is currently addressed using external calibration, which limits achievable mass accuracy for MALDI-IMS and makes it difficult to match these data to downstream LC-MS/MS results. To overcome this challenge, the work presented here details a method for internally calibrating data sets generated from tryptic peptide MALDI-IMS on formalin-fixed paraffin-embedded sections of ovarian cancer. By calibrating all spectra to internal peak features the m/z error for matches made between MALDI-IMS m/z values and LC-MS/MS identified peptide m/z values was significantly reduced. This improvement was confirmed by follow up matching of LC-MS/MS spectra to in situ MS/MS spectra from the same m/z peak features. The sum of the data presented here indicates that internal calibrants should be a standard component of tryptic peptide MALDI-IMS experiments.     

Identification of a type of human IgG-like protein in shrimp Penaeus vannamei by mass spectrometry

Recently, infectious diseases have delayed the growth of shrimp aquaculture. Interest has been focused on immune molecules and defense mechanisms to reduce these diseases in shrimp aquaculture. In invertebrates, various immunoglobulin superfamily (IgSF) molecules have been characterized in body tissues and fluids, which play a significant role in innate defense. In the current study, we found that a protein in shrimp serum, referred as an IgG-like protein, could be reacted with goat anti-human IgG, specifically. The IgG-like protein was purified from the serum of shrimp Penaeus vannamei by affinity chromatography using CNBr-activated sepharose 4B. The purified protein was subsequently analysed using one-dimensional sodium sulphate-polyacrylamide gel electrophoresis (1-DE), two-dimensional sodium sulphate-polyacrylamide gel electrophoresis (2-DE) and immunoblotting. Furthermore, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry and quadrupole time-of-flight (Q-TOF) tandem mass spectrometry methods were used for peptide mass fingerprint (PMF) and sequencing of the protein, respectively. Sequence information and PMF queried against the NCBI database confirmed the identity of the protein as hemocyanin. Conserved domain search showed that there was an Ig-like conserved domain of 252 amino acid residues in the C-terminus of arthropoda hemocyanins. In addition, four and one conserved regions were found between hemocyanin and human Ig heavy chain and Ig kappa chain, respectively. These results indicate that in addition to copper-binding domains hemocyanin has an Ig-like conserved domain, which would confer some new functions to multifunctional respiratory pigment of crustaceans.     

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.     

Parallel post-source decay for increasing protein identification confidence levels from 2-D gels

Peptide mass fingerprinting (PMF) has over the years become one of the most commonly used tools for high-throughput analysis and identification of proteins. This method is applicable when relatively simple samples have to be analysed and it is commonly used for analysing proteins previously separated by 2-DE. The most common type of instrument used for this approach is the MALDI-TOF that has proved to be particularly suitable for the PMF analysis because of its characteristics of speed, robustness, sensitivity and automation. We have used a MALDI-TOF equipped with a novel parallel PSD capability (MALDI micro MX), to perform the analysis of two sets of different biological samples isolated by 2-DE. By using a method that integrates the data obtained by PMF analysis with the PSD data obtained in the same experiment, we show that the new multiplexed PSD solution increases the protein identification rate compared to the normal PMF approach. We also investigated the use of a charge-directed fragmentation modification reagent to improve the identification rate and confidence levels.     

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.     

Matrix-assisted laser desorption/ionization-tandem mass spectrometry with high resolution and sensitivity for identification and characterization of proteins

Although peptide mass fingerprinting is currently the method of choice to identify proteins, the number of proteins available in databases is increasing constantly, and hence, the advantage of having sequence data on a selected peptide, in order to increase the effectiveness of database searching, is more crucial. Until recently, the ability to identify proteins based on the peptide sequence was essentially limited to the use of electrospray ionization tandem mass spectrometry (MS) methods. The recent development of new instruments with matrix-assisted laser desorption/ionization (MALDI) sources and true tandem mass spectrometry (MS/MS) capabilities creates the capacity to obtain high quality tandem mass spectra of peptides. In this work, using the new high resolution tandem time of flight MALDI-(TOF/TOF) mass spectrometer from Applied Biosystems, examples of successful identification and characterization of bovine heart proteins (SWISS-PROT entries: P02192, Q9XSC6, P13620) separated by two-dimensional electrophoresis and blotted onto polyvinylidene difluoride membrane are described. Tryptic protein digests were analyzed by MALDI-TOF to identify peptide masses afterward used for MS/MS. Subsequent high energy MALDI-TOF/TOF collision-induced dissociation spectra were recorded on selected ions. All data, both MS and MS/MS, were recorded on the same instrument. Tandem mass spectra were submitted to database searching using MS-Tag or were manually de novo sequenced. An interesting modification of a tryptophan residue, a "double oxidation", came to light during these analyses.     

Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling

Reliable statistical validation of peptide and protein identifications is a top priority in large-scale mass spectrometry based proteomics. PeptideProphet is one of the computational tools commonly used for assessing the statistical confidence in peptide assignments to tandem mass spectra obtained using database search programs such as SEQUEST, MASCOT, or X! TANDEM. We present two flexible methods, the variable component mixture model and the semiparametric mixture model, that remove the restrictive parametric assumptions in the mixture modeling approach of PeptideProphet. Using a control protein mixture data set generated on an linear ion trap Fourier transform (LTQ-FT) mass spectrometer, we demonstrate that both methods improve parametric models in terms of the accuracy of probability estimates and the power to detect correct identifications controlling the false discovery rate to the same degree. The statistical approaches presented here require that the data set contain a sufficient number of decoy (known to be incorrect) peptide identifications, which can be obtained using the target-decoy database search strategy.     

High-performance hardware implementation of a parallel database search engine for real-time peptide mass fingerprinting

Motivation: Peptide mass fingerprinting (PMF) is a method for protein identification in which a protein is fragmented by a defined cleavage protocol (usually proteolysis with trypsin), and the masses of these products constitute a 'fingerprint' that can be searched against theoretical fingerprints of all known proteins. In the first stage of PMF, the raw mass spectrometric data are processed to generate a peptide mass list. In the second stage this protein fingerprint is used to search a database of known proteins for the best protein match. Although current software solutions can typically deliver a match in a relatively short time, a system that can find a match in real time could change the way in which PMF is deployed and presented. In a paper published earlier we presented a hardware design of a raw mass spectra processor that, when implemented in Field Programmable Gate Array (FPGA) hardware, achieves almost 170-fold speed gain relative to a conventional software implementation running on a dual processor server. In this article we present a complementary hardware realization of a parallel database search engine that, when running on a Xilinx Virtex 2 FPGA at 100 MHz, delivers 1800-fold speed-up compared with an equivalent C software routine, running on a 3.06 GHz Xeon workstation. The inherent scalability of the design means that processing speed can be multiplied by deploying the design on multiple FPGAs. The database search processor and the mass spectra processor, running on a reconfigurable computing platform, provide a complete real-time PMF protein identification solution.     

tsORFdb: Theoretical Small Open Reading Frames (ORFs) database and massProphet: Peptide Mass Fingerprinting (PMF) tool for unknown small functional ORFs

Peptide mass fingerprinting (PMF) has become one of the most widely used methods for rapid identification of proteins in proteomics research. Many peaks, however, remain unassigned after PMF analysis, partly because of post-translational modification and the limited scope of protein sequences. Almost all PMF tools employ only known or predicted protein sequences and do not include open reading frames (ORFs) in the genome, which eliminates the chance of finding novel functional peptides. Unlike most tools that search protein sequences from known coding sequences, the tool we developed uses a database for theoretical small ORFs (tsORFs) and a PMF application using a tsORFs database (tsORFdb). The tsORFdb is a database for ORFeome that encompasses all potential tsORFs derived from whole genome sequences as well as the predicted ones. The massProphet system tries to extend the search scope to include the ORFeome using the tsORFdb. The tsORFdb and massProphet should be useful for proteomics research to give information about unknown small ORFs as well as predicted and registered proteins.