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.     

牛血清白蛋白胰蛋白酶酶解产物的色谱-质谱联用分析及其三种数据库搜寻鉴定方法的比较

利用反相高效液相色谱 (RP HPLC)和电喷雾串联质谱 (ESI MS MS)联用技术直接对模式蛋白分子 (牛血清白蛋白 ,BSA)的胰蛋白酶酶解产物进行分离和测定 .获得的一系列BSA酶解片段的一级 (MS)和二级 (MS MS)质谱数据经分析软件处理后 ,分别在不同处理和不同参数条件下 ,用 3种不同的方法通过网上蛋白质数据库进行蛋白质搜寻鉴定 .结果显示 ,3种搜寻法都能正确地鉴定该蛋白质 ,其中以利用MS数据的肽质量指纹谱搜寻法 (PMF法 )较为快捷方便 ,但鉴定结果易受数据处理和数据库搜寻鉴定时参数设置等因素的影响 ;利用未解析MS MS数据 (rawMS MSdata)的搜寻法可在较宽的搜寻参数变化范围内获得明确的鉴定结果 ;而借助从头测序 (denovosequencing)结果的序列搜寻法 (sequencequery)则显示出更高的专一性 ,利用较少酶解片段数据就能得到稳定和明确的鉴定结果 ,搜寻参数变化的影响很小 .就酶解条件、数据处理和搜寻参数设置对蛋白质鉴定结果的影响展开详细的讨论 ,为蛋白质组学研究中的数据处理和库搜寻鉴定积累了可借鉴的资料     

Faster SEQUEST searching for peptide identification from tandem mass spectra

Computational analysis of mass spectra remains the bottleneck in many proteomics experiments. SEQUEST was one of the earliest software packages to identify peptides from mass spectra by searching a database of known peptides. Though still popular, SEQUEST performs slowly. Crux and TurboSEQUEST have successfully sped up SEQUEST by adding a precomputed index to the search, but the demand for ever-faster peptide identification software continues to grow. Tide, introduced here, is a software program that implements the SEQUEST algorithm for peptide identification and that achieves a dramatic speedup over Crux and SEQUEST. The optimization strategies detailed here employ a combination of algorithmic and software engineering techniques to achieve speeds up to 170 times faster than a recent version of SEQUEST that uses indexing. For example, on a single Xeon CPU, Tide searches 10,000 spectra against a tryptic database of 27,499 Caenorhabditis elegans proteins at a rate of 1550 spectra per second, which compares favorably with a rate of 8.8 spectra per second for a recent version of SEQUEST with index running on the same hardware.     

Confidence assessment for protein identification by using peptide‐mass fingerprinting data

Protein identification using Peptide Mass Fingerprinting (PMF) data remains an important yet only partially solved problem. Current computational methods may lead to false positive identification since the top hit from a database search may not be the target protein. In addition, the identification scores assigned singly by a scoring function (raw scores) are not normalized. Therefore, the ranking based on raw scores may be biased. To address the above issue, we have developed a statistical model to evaluate the confidence of the raw score and to improve the ranking of proteins for identification. The results show that the statistical model better ranks the correct protein than the raw scores. Our study provides a new method to enhance the accuracy of protein identification by using PMF data. We incorporated the method into our software package “Protein‐Decision” together with a user‐friendly graphical interface. A standalone version of Protein‐Decision is freely available at http://digbio.missouri.edu/ProteinDecision/ .     

ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data

With the recent quick expansion of DNA and protein sequence databases, intensive efforts are underway to interpret the linear genetic information of DNA in terms of function, structure, and control of biological processes. The systematic identification and quantification of expressed proteins has proven particularly powerful in this regard. Large-scale protein identification is usually achieved by automated liquid chromatography-tandem mass spectrometry of complex peptide mixtures and sequence database searching of the resulting spectra [Aebersold and Goodlett, Chem. Rev. 2001, 101, 269-295]. As generating large numbers of sequence-specific mass spectra (collision-induced dissociation/CID) spectra has become a routine operation, research has shifted from the generation of sequence database search results to their validation. Here we describe in detail a novel probabilistic model and score function that ranks the quality of the match between tandem mass spectral data and a peptide sequence in a database. We document the performance of the algorithm on a reference data set and in comparison with another sequence database search tool. The software is publicly available for use and evaluation at http://www.systemsbiology.org/research/software/proteomics/ProbID.     

Comparison of search engine contributions in protein mass fingerprinting for protein identification

Peptide mass fingerprinting (PMF) is a valuable method for rapid and high-throughput protein identification using the proteomics approach. Automated search engines, such as Ms-Fit, Mascot, ProFound, and Peptldent, have facilitated protein identification through PMF. The potential to obtain a true MS protein identification result depends on the choice of algorithm as well as experimental factors that influence the information content in MS data. When mass spectral data are incomplete and/or have low mass accuracy, the “number of matches” approach may be inadequate for a useful identification. Several studies have evaluated factors influencing the quality of mass spectrometry (MS) experiments. Missed cleavages, posttranslational modifications of peptides and contaminants (e.g., keratin) are important factors that can affect the results of MS analyses by influencing the identification process as well as the quality of the MS spectra. We compared search engines frequently used to identify proteins fromHomo sapiens andHalobacterium salinarum by evaluating factors, including data-based and mass tolerance to develop an improved search engine for PMF. This study may provide information to help develop a more effective algorithm for protein identification in each species through PMF.     

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.     

Verification of automated peptide identifications from proteomic tandem mass spectra

Shotgun proteomics yields tandem mass spectra of peptides that can be identified by database search algorithms. When only a few observed peptides suggest the presence of a protein, establishing the accuracy of the peptide identifications is necessary for accepting or rejecting the protein identification. In this protocol, we describe the properties of peptide identifications that can differentiate legitimately identified peptides from spurious ones. The chemistry of fragmentation, as embodied in the 'mobile proton' and 'pathways in competition' models, informs the process of confirming or rejecting each spectral match. Examples of ion-trap and tandem time-of-flight (TOF/TOF) mass spectra illustrate these principles of fragmentation.     

SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database

Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances in tandem mass spectrometry has made it the method of choice for high-throughput identification of proteins. Unfortunately, the software for unambiguously identifying peptide sequences has not kept pace with the recent hardware improvements in mass spectrometry instruments. Critical for reliable high-throughput protein identification, scoring functions evaluate the quality of a match between experimental spectra and a database peptide. Current scoring function technology relies heavily on ad-hoc parameterization and manual curation by experienced mass spectrometrists. In this work, we propose a two-stage stochastic model for the observed MS/MS spectrum, given a peptide. Our model explicitly incorporates fragment ion probabilities, noisy spectra, and instrument measurement error. We describe how to compute this probability based score efficiently, using a dynamic programming technique. A prototype implementation demonstrates the effectiveness of the model.     

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.     

Hardware acceleration of processing of mass spectrometric data for proteomics

MOTIVATION: High-resolution mass spectrometers generate large data files that are complex, noisy and require extensive processing to extract the optimal data from raw spectra. This processing is readily achieved in software and is often embedded in manufacturers' instrument control and data processing environments. However, the speed of this data processing is such that it is usually performed off-line, post data acquisition. We have been exploring strategies that would allow real-time advanced processing of mass spectrometric data, making use of the reconfigurable computing paradigm, which exploits the flexibility and versatility of Field Programmable Gate Arrays (FPGAs). This approach has emerged as a powerful solution for speeding up time-critical algorithms. We describe here a reconfigurable computing solution for processing raw mass spectrometric data generated by MALDI-ToF instruments. The hardware-implemented algorithms for de-noising, baseline correction, peak identification and deisotoping, running on a Xilinx Virtex 2 FPGA at 180 MHz, generate a mass fingerprint over 100 times faster than an equivalent algorithm written in C, running on a Dual 3 GHz Xeon workstation.     

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.     

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.     

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。     

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.     

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.     

A suffix tree approach to the interpretation of tandem mass spectra: applications to peptides of non-specific digestion and post-translational modifications

MOTIVATION: Tandem mass spectrometry combined with sequence database searching is one of the most powerful tools for protein identification. As thousands of spectra are generated by a mass spectrometer in one hour, the speed of database searching is critical, especially when searching against a large sequence database, or when the peptide is generated by some unknown or non-specific enzyme, even or when the target peptides have post-translational modifications (PTM). In practice, about 70-90% of the spectra have no match in the database. Many believe that a significant portion of them are due to peptides of non-specific digestions by unknown enzymes or amino acid modifications. In another case, scientists may choose to use some non-specific enzymes such as pepsin or thermolysin for proteolysis in proteomic study, in that not all proteins are amenable to be digested by some site-specific enzymes, and furthermore many digested peptides may not fall within the rang of molecular weight suitable for mass spectrometry analysis. Interpreting mass spectra of these kinds will cost a lot of computational time of database search engines. OVERVIEW: The present study was designed to speed up the database searching process for both cases. More specifically speaking, we employed an approach combining suffix tree data structure and spectrum graph. The suffix tree is used to preprocess the protein sequence database, while the spectrum graph is used to preprocess the tandem mass spectrum. We then search the suffix tree against the spectrum graph for candidate peptides. We design an efficient algorithm to compute a matching threshold with some statistical significance level, e.g. p = 0.01, for each spectrum, and use it to select candidate peptides. Then we rank these peptides using a SEQUEST-like scoring function. The algorithms were implemented and tested on experimental data. For post-translational modifications, we allow arbitrary number of any modification to a protein. AVAILABILITY: The executable program and other supplementary materials are available online at: http://hto-c.usc.edu:8000/msms/suffix/.     

Automatic quality assessment of peptide tandem mass spectra

MOTIVATION: A powerful proteomics methodology couples high-performance liquid chromatography (HPLC) with tandem mass spectrometry and database-search software, such as SEQUEST. Such a set-up, however, produces a large number of spectra, many of which are of too poor quality to be useful. Hence a filter that eliminates poor spectra before the database search can significantly improve throughput and robustness. Moreover, spectra judged to be of high quality, but that cannot be identified by database search, are prime candidates for still more computationally intensive methods, such as de novo sequencing or wider database searches including post-translational modifications. RESULTS: We report on two different approaches to assessing spectral quality prior to identification: binary classification, which predicts whether or not SEQUEST will be able to make an identification, and statistical regression, which predicts a more universal quality metric involving the number of b- and y-ion peaks. The best of our binary classifiers can eliminate over 75% of the unidentifiable spectra while losing only 10% of the identifiable spectra. Statistical regression can pick out spectra of modified peptides that can be identified by a de novo program but not by SEQUEST. In a section of independent interest, we discuss intensity normalization of mass spectra.