Protein identification by syringe pump-driven reversed-phase LC-MS/MS

In typical mass spectrometry-based protein identification using peptide fragmentation fingerprinting, front-end separation plays a critical role in successful peptide sequencing. This separation step demands a great deal of time and usually is the rate-limiting step for the whole process. Here we provide an alternative separation method, based on a simple nanoflow delivery system, that is able to shorten the separation time considerably. This system consists of a 25-mul syringe connected to a manually packed reversed-phase mini-capillary column that can be directly coupled to an electrospray ionization tandem mass spectrometer. A syringe pump is then used to deliver the peptide mixtures at a nanoscale flow rate. We examined the efficiency and efficacy of this method by analyzing the tryptic peptides of bovine serum albumin and of 10 Escherichia coli proteins separated by two-dimensional gel electrophoresis (2DE). The results showed that identification of each protein could be achieved successfully within 25 min by using the disposable mini-capillary column. Moreover, all 2DE-separated E. coli proteins were identified at high confidence levels. Together, our data suggest that this method is a suitable option for mass spectrometry-based protein identification.     

The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments

Background  

Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.     

Automated protein identification by the combination of MALDI MS and MS/MS spectra from different instruments

The identification of proteins separated on two-dimensional gels is most commonly performed by trypsin digestion and subsequent matrix-assisted laser desorption ionization (MALDI) with time-of-flight (TOF). Recently, atmospheric pressure (AP) MALDI coupled to an ion trap (IT) has emerged as a convenient method to obtain tandem mass spectra (MS/MS) from samples on MALDI target plates. In the present work, we investigated the feasibility of using the two methodologies in line as a standard method for protein identification. In this setup, the high mass accuracy MALDI-TOF spectra are used to calibrate the peptide precursor masses in the lower mass accuracy AP-MALDI-IT MS/MS spectra. Several software tools were developed to automate the analysis process. Two sets of MALDI samples, consisting of 142 and 421 gel spots, respectively, were analyzed in a highly automated manner. In the first set, the protein identification rate increased from 61% for MALDI-TOF only to 85% for MALDI-TOF combined with AP-MALDI-IT. In the second data set the increase in protein identification rate was from 44% to 58%. AP-MALDI-IT MS/MS spectra were in general less effective than the MALDI-TOF spectra for protein identification, but the combination of the two methods clearly enhanced the confidence in protein identification.     

A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores

Shotgun tandem mass spectrometry-based peptide sequencing using programs such as SEQUEST allows high-throughput identification of peptides, which in turn allows the identification of corresponding proteins. We have applied a machine learning algorithm, called the support vector machine, to discriminate between correctly and incorrectly identified peptides using SEQUEST output. Each peptide was characterized by SEQUEST-calculated features such as delta Cn and Xcorr, measurements such as precursor ion current and mass, and additional calculated parameters such as the fraction of matched MS/MS peaks. The trained SVM classifier performed significantly better than previous cutoff-based methods at separating positive from negative peptides. Positive and negative peptides were more readily distinguished in training set data acquired on a QTOF, compared to an ion trap mass spectrometer. The use of 13 features, including four new parameters, significantly improved the separation between positive and negative peptides. Use of the support vector machine and these additional parameters resulted in a more accurate interpretation of peptide MS/MS spectra and is an important step toward automated interpretation of peptide tandem mass spectrometry data in proteomics.     

Quantitative proteome analysis using differential stable isotopic labeling and microbore LC-MALDI MS and MS/MS

We demonstrate an approach for global quantitative analysis of protein mixtures using differential stable isotopic labeling of the enzyme-digested peptides combined with microbore liquid chromatography (LC) matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS). Microbore LC provides higher sample loading, compared to capillary LC, which facilitates the quantification of low abundance proteins in protein mixtures. In this work, microbore LC is combined with MALDI MS via a heated droplet interface. The compatibilities of two global peptide labeling methods (i.e., esterification to carboxylic groups and dimethylation to amine groups of peptides) with this LC-MALDI technique are evaluated. Using a quadrupole-time-of-flight mass spectrometer, MALDI spectra of the peptides in individual sample spots are obtained to determine the abundance ratio among pairs of differential isotopically labeled peptides. MS/MS spectra are subsequently obtained from the peptide pairs showing significant abundance differences to determine the sequences of selected peptides for protein identification. The peptide sequences determined from MS/MS database search are confirmed by using the overlaid fragment ion spectra generated from a pair of differentially labeled peptides. The effectiveness of this microbore LC-MALDI approach is demonstrated in the quantification and identification of peptides from a mixture of standard proteins as well as E. coli whole cell extract of known relative concentrations. It is shown that this approach provides a facile and economical means of comparing relative protein abundances from two proteome samples.     

Clustering of MS spectra for improved protein identification rate and screening for protein variants and modifications by MALDI-MS/MS

It is an established fact that allelic variation and post-translational modifications create different variants of proteins, which are observed as isoelectric and size subspecies in two-dimensional gel based proteomics. Here we explore the stromal proteome of spinach and Arabidopsis chloroplast and show that clustering of mass spectra is a useful tool for investigating such variants and detecting modified peptides with amino acid substitutions or post-translational modifications. This study employs data mining by hierarchical clustering of MALDI-MS spectra, using the web version of the SPECLUST program (http://bioinfo.thep.lu.se/speclust.html). The tool can also be used to remove peaks of contaminating proteins and to improve protein identification, especially for species without a fully sequenced genome. Mutually exclusive peptide peaks within a cluster provide a good starting point for MS/MS investigation of modified peptides, here exemplified by the identification of an A to E substitution that accounts for the isoelectric heterogeneity in protein isoforms.     

Identification and quantification of differentially expressed proteins in E-cadherin deficient SCC9 cells and SCC9 transfectants expressing E-cadherin by dimethyl isotope labeling, LC-MALDI MS and MS/MS

A strategy based on isotope labeling of peptides and liquid chromatography matrix-assisted laser desorption ionization mass spectrometry (LC-MALDI MS) has been employed to accurately quantify and confidently identify differentially expressed proteins between an E-cadherin-deficient human carcinoma cell line (SCC9) and its transfectants expressing E-cadherin (SCC9-E). Proteins extracted from each cell line were tryptically digested and the resultant peptides were labeled individually with either d(0)- or d(2)-formaldehyde. The labeled peptides were combined and the peptide mixture was separated and fractionated by a strong cation exchange (SCX) column. Peptides from each SCX fraction were further separated by a microbore reversed-phase (RP) LC column. The effluents were then directly spotted onto a MALDI target using a heated droplet LC-MALDI interface. After mixing with a MALDI matrix, individual sample spots were analyzed by MALDI quadrupole time-of-flight MS, using an initial MS scan to quantify the dimethyl labeled peptide pairs. MS/MS analysis was then carried out on the peptide pairs having relative peak intensity changes of greater than 2-fold. The MS/MS spectra were subjected to database searching for protein identification. The search results were further confirmed by comparing the MS/MS spectra of the peptide pairs. Using this strategy, we detected and compared relative peak intensity changes of 5480 peptide pairs. Among them, 320 peptide pairs showed changes of greater than 2-fold. MS/MS analysis of these changing pairs led to the identification of 49 differentially expressed proteins between the parental SCC9 cells and SCC9-E transfectants. These proteins were determined to be involved in different pathways regulating cytoskeletal organization, cell adhesion, epithelial polarity, and cell proliferation. The changes in protein expression were consistent with increased cell-cell and cell-matrix adhesion and decreased proliferation in SCC9-E cells, in line with E-cadherin tumor suppressor activity. Finally, the accuracy of the MS quantification and subcellular localization for 6 differentially expressed proteins were validated by immunoblotting and immunofluorescence assays.     

A data-mining scheme for identifying peptide structural motifs responsible for different MS/MS fragmentation intensity patterns

Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28 330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms.     

Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies

Database-searching programs generally identify only a fraction of the spectra acquired in a standard LC/MS/MS study of digested proteins. Subtle variations in database-searching algorithms for assigning peptides to MS/MS spectra have been known to provide different identification results. To leverage this variation, a probabilistic framework is developed for combining the results of multiple search engines. The scores for each search engine are first independently converted into peptide probabilities. These probabilities can then be readily combined across search engines using Bayesian rules and the expectation maximization learning algorithm. A significant gain in the number of peptides identified with high confidence with each additional search engine is demonstrated using several data sets of increasing complexity, from a control protein mixture to a human plasma sample, searched using SEQUEST, Mascot, and X! Tandem database-searching programs. The increased rate of peptide assignments also translates into a substantially larger number of protein identifications in LC/MS/MS studies compared to a typical analysis using a single database-search tool.     

Effect of iTRAQ Labeling on the Relative Abundance of Peptide Fragment Ions Produced by MALDI-MS/MS

The identification of proteins in proteomics experiments is usually based on mass information derived from tandem mass spectrometry data. To improve the performance of the identification algorithms, additional information available in the fragment peak intensity patterns has been shown to be useful. In this study, we consider the effect of iTRAQ labeling on the fragment peak intensity patterns of singly charged peptides from MALDI tandem MS data. The presence of an iTRAQ-modified basic group on the N-terminus leads to a more pronounced set of b-ion peaks and distinct changes in the abundance of specific peptide types. We performed a simple intensity prediction by using a decision-tree machine learning approach and were able to show that the relative ion abundance in a spectrum can be correctly predicted and distinguished from closely related sequences. This information will be useful for the development of improved method-specific intensity-based protein identification algorithms.     

MS2‐Deisotoper: A Tool for Deisotoping High‐Resolution MS/MS Spectra in Normal and Heavy Isotope‐Labelled Samples

High‐resolution MS/MS spectra of peptides can be deisotoped to identify monoisotopic masses of peptide fragments. The use of such masses should improve protein identification rates. However, deisotoping is not universally used and its benefits have not been fully explored. Here, MS2‐Deisotoper, a tool for use prior to database search, is used to identify monoisotopic peaks in centroided MS/MS spectra. MS2‐Deisotoper works by comparing the mass and relative intensity of each peptide fragment peak to every other peak of greater mass, and by applying a set of rules concerning mass and intensity differences. After comprehensive parameter optimization, it is shown that MS2‐Deisotoper can improve the number of peptide spectrum matches (PSMs) identified by up to 8.2% and proteins by up to 2.8%. It is effective with SILAC and non‐SILAC MS/MS data. The identification of unique peptide sequences is also improved, increasing the number of human proteoforms by 3.7%. Detailed investigation of results shows that deisotoping increases Mascot ion scores, improves FDR estimation for PSMs, and leads to greater protein sequence coverage. At a peptide level, it is found that the efficacy of deisotoping is affected by peptide mass and charge. MS2‐Deisotoper can be used via a user interface or as a command‐line tool.     

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.     

Protein identification using MS/MS data

The subject of this tutorial is protein identification and characterisation by database searching of MS/MS Data. Peptide Mass Fingerprinting is excluded because it is covered in a separate tutorial. Practical aspects of database searching are emphasised, such as choice of sequence database, effect of mass tolerance, and how to identify post-translational modifications. The relationship between sensitivity and specificity is discussed, as is the challenge of using peptide match information to infer which proteins were present in the sample. Since these tutorials are introductory in nature, most references are to reviews, rather than primary research papers. Some familiarity with mass spectrometry and protein chemistry is assumed. There is an accompanying slide presentation, including speaker notes, and a collection of web-based, practical exercises, designed to reinforce key points. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 6).     

SwedCAD, a database of annotated high-mass accuracy MS/MS spectra of tryptic peptides

A database of high-mass accuracy tryptic peptides has been created. The database contains 15 897 unique, annotated MS/MS spectra. It is possible to search for peptides according to their mass, number of missed cleavages, and sequence motifs. All of the data contained in the database is downloadable, and each spectrum can be visualized. An example is presented of how the database can be used for studying peptide fragmentation. Fragmentation of different types of missed cleaved peptides has been studied, and the results can be used to improve identification of these types of peptides.     

pepgrep: A Tool for Peptide MS/MS Pattern Matching

Typically, detection of protein sequences in collision-induced dissociation (CID) tandem MS (MS2) dataset is performed by mapping identified peptide ions back to protein sequence by using the protein database search (PDS) engine. Finding a particular peptide sequence of interest in CID MS2 records very often requires manual evaluation of the spectrum, regardless of whether the peptide-associated MS2 scan is identified by PDS algorithm or not. We have developed a compact cross-platform database-free command-line utility, pepgrep, which helps to find an MS2 fingerprint for a selected peptide sequence by pattern-matching of modelled MS2 data using Peptide-to-MS2 scoring algorithm. pepgrep can incorporate dozens of mass offsets corresponding to a variety of post-translational modifications (PTMs) into the algorithm. Decoy peptide sequences are used with the tested peptide sequence to reduce false-positive results. The engine is capable of screening an MS2 data file at a high rate when using a cluster computing environment. The matched MS2 spectrum can be displayed by using built-in graphical application programming interface (API) or optionally recorded to file. Using this algorithm, we were able to find extra peptide sequences in studied CID spectra that were missed by PDS identification. Also we found pepgrep especially useful for examining a CID of small fractions of peptides resulting from, for example, affinity purification techniques. The peptide sequences in such samples are less likely to be positively identified by using routine protein-centric algorithm implemented in PDS. The software is freely available at http://bsproteomics.essex.ac.uk:8080/data/download/pepgrep-1.4.tgz.     

PRIMA: peptide robust identification from MS/MS spectra

In proteomics, tandem mass spectrometry is the key technology for peptide sequencing. However, partially due to the deficiency of peptide identification software, a large portion of the tandem mass spectra are discarded in almost all proteomics centers because they are not interpretable. The problem is more acute with the lower quality data from low end but more popular devices such as the ion trap instruments. In order to deal with the noisy and low quality data, this paper develops a systematic machine learning approach to construct a robust linear scoring function, whose coefficients are determined by a linear programming. A prototype, PRIMA, was implemented. When tested with large benchmarks of varying qualities, PRIMA consistently has higher accuracy than commonly used software MASCOT, SEQUEST and X! Tandem.     

A reproducibility-based evaluation procedure for quantifying the differences between MS/MS peak intensity normalization methods

The identification of peptides and proteins from fragmentation mass spectra is a very common approach in the field of proteomics. Contemporary high-throughput peptide identification pipelines can quickly produce large quantities of MS/MS data that contain valuable knowledge about the actual physicochemical processes involved in the peptide fragmentation process, which can be extracted through extensive data mining studies. As these studies attempt to exploit the intensity information contained in the MS/MS spectra, a critical step required for a meaningful comparison of this information between MS/MS spectra is peak intensity normalization. We here describe a procedure for quantifying the efficiency of different published normalization methods in terms of the quartile coefficient of dispersion (qcod) statistic. The quartile coefficient of dispersion is applied to measure the dispersion of the peak intensities between redundant MS/MS spectra, allowing the quantification of the differences in computed peak intensity reproducibility between the different normalization methods. We demonstrate that our results are independent of the data set used in the evaluation procedure, allowing us to provide generic guidance on the choice of normalization method to apply in a certain MS/MS pipeline application.     

An improved method of sample preparation on AnchorChip targets for MALDI-MS and MS/MS and its application in the liver proteome project

An improved method for sample preparation for MALDI-MS and MS/MS using AnchorChip targets is presented. The method, termed the SMW method (sample, matrix wash), results in better sensitivity for peptide mass fingerprinting as well as for sequencing by MS/MS than previously published methods. The method allows up-concentration and desalting directly on the mass spectrometric target and should be amenable for automation. A draw back caused by extensive oxidation of methionine and tryptophan in the SMW method can be alleviated by the addition of n-octyl glucopyranoside and DTT to the sample solution. The method was validated for protein identification from a 2-DE based liver proteome study. The SMW method resulted in identification of many more proteins and in most cases with a better score than the previously published methods.     

Peptide identification by tandem mass spectrometry with alternate fragmentation modes

The high-throughput nature of proteomics mass spectrometry is enabled by a productive combination of data acquisition protocols and the computational tools used to interpret the resulting spectra. One of the key components in mainstream protocols is the generation of tandem mass (MS/MS) spectra by peptide fragmentation using collision induced dissociation, the approach currently used in the large majority of proteomics experiments to routinely identify hundreds to thousands of proteins from single mass spectrometry runs. Complementary to these, alternative peptide fragmentation methods such as electron capture/transfer dissociation and higher-energy collision dissociation have consistently achieved significant improvements in the identification of certain classes of peptides, proteins, and post-translational modifications. Recognizing these advantages, mass spectrometry instruments now conveniently support fine-tuned methods that automatically alternate between peptide fragmentation modes for either different types of peptides or for acquisition of multiple MS/MS spectra from each peptide. But although these developments have the potential to substantially improve peptide identification, their routine application requires corresponding adjustments to the software tools and procedures used for automated downstream processing. This review discusses the computational implications of alternative and alternate modes of MS/MS peptide fragmentation and addresses some practical aspects of using such protocols for identification of peptides and post-translational modifications.