mMass as a Software Tool for the Annotation of Cyclic Peptide Tandem Mass Spectra

Natural or synthetic cyclic peptides often possess pronounced bioactivity. Their mass spectrometric characterization is difficult due to the predominant occurrence of non-proteinogenic monomers and the complex fragmentation patterns observed. Even though several software tools for cyclic peptide tandem mass spectra annotation have been published, these tools are still unable to annotate a majority of the signals observed in experimentally obtained mass spectra. They are thus not suitable for extensive mass spectrometric characterization of these compounds. This lack of advanced and user-friendly software tools has motivated us to extend the fragmentation module of a freely available open-source software, mMass (http://www.mmass.org), to allow for cyclic peptide tandem mass spectra annotation and interpretation. The resulting software has been tested on several cyanobacterial and other naturally occurring peptides. It has been found to be superior to other currently available tools concerning both usability and annotation extensiveness. Thus it is highly useful for accelerating the structure confirmation and elucidation of cyclic as well as linear peptides and depsipeptides.     

Peptide Identification from Mixture Tandem Mass Spectra

The success of high-throughput proteomics hinges on the ability of computational methods to identify peptides from tandem mass spectra (MS/MS). However, a common limitation of most peptide identification approaches is the nearly ubiquitous assumption that each MS/MS spectrum is generated from a single peptide. We propose a new computational approach for the identification of mixture spectra generated from more than one peptide. Capitalizing on the growing availability of large libraries of single-peptide spectra (spectral libraries), our quantitative approach is able to identify up to 98% of all mixture spectra from equally abundant peptides and automatically adjust to varying abundance ratios of up to 10:1. Furthermore, we show how theoretical bounds on spectral similarity avoid the need to compare each experimental spectrum against all possible combinations of candidate peptides (achieving speedups of over five orders of magnitude) and demonstrate that mixture-spectra can be identified in a matter of seconds against proteome-scale spectral libraries. Although our approach was developed for and is demonstrated on peptide spectra, we argue that the generality of the methods allows for their direct application to other types of spectral libraries and mixture spectra.The success of tandem MS (MS/MS¹) approaches to peptide identification is partly due to advances in computational techniques allowing for the reliable interpretation of MS/MS spectra. Mainstream computational techniques mainly fall into two categories: database search approaches that score each spectrum against peptides in a sequence database (1–4) or de novo techniques that directly reconstruct the peptide sequence from each spectrum (5–8). The combination of these methods with advances in high-throughput MS/MS have promoted the accelerated growth of spectral libraries, collections of peptide MS/MS spectra the identification of which were validated by accepted statistical methods (9, 10) and often also manually confirmed by mass spectrometry experts. The similar concept of spectral archives was also recently proposed to denote spectral libraries including “interesting” nonidentified spectra (11) (i.e. recurring spectra with good de novo reconstructions but no database match). The growing availability of these large collections of MS/MS spectra has reignited the development of alternative peptide identification approaches based on spectral matching (12–14) and alignment (15–17) algorithms.However, mainstream approaches were developed under the (often unstated) assumption that each MS/MS spectrum is generated from a single peptide. Although chromatographic procedures greatly contribute to making this a reasonable assumption, there are several situations where it is difficult or even impossible to separate pairs of peptides. Examples include certain permutations of the peptide sequence or post-translational modifications (see (18) for examples of co-eluting histone modification variants). In addition, innovative experimental setups have demonstrated the potential for increased throughput in peptide identification using mixture spectra; examples include data-independent acquisition (19) ion-mobility MS (20), and MS^E strategies (21).To alleviate the algorithmic bottleneck in such scenarios, we describe a computational approach, M-SPLIT (mixture-spectrum partitioning using library of identified tandem mass spectra), that is able to reliably and efficiently identify peptides from mixture spectra, which are generated from a pair of peptides. In brief, a mixture spectrum is modeled as linear combination of two single-peptide spectra, and peptide identification is done by searching against a spectral library. We show that efficient filtration and accurate branch-and-bound strategies can be used to avoid the huge computational cost of searching all possible pairs. Thus equipped, our approach is able to identify the correct matches by considering only a minuscule fraction of all possible matches. Beyond potentially enhancing the identification capabilities of current MS/MS acquisition setups, we argue that the availability of methods to reliably identify MS/MS spectra from mixtures of peptides could enable the collection of MS/MS data using accelerated chromatography setups to obtain the same or better peptide identification results in a fraction of the experimental time currently required for exhaustive peptide separation.     

电喷雾串联质谱图的叠合与多肽序列分析

利用离子阱电喷雾串联质谱仪,在选择性改变某些食品参数的条件下对模式分子Met-脑啡肽和自行固相化学合成的7肽及其修饰产物、10肽和20肽进行碎裂处理,从而获得一系列具有一定差异的串联质谱图。选择具有适当互补性的图谱进行叠合处理,得到具有连贯性“三联套”（triplet）及“二联套”（doublet）碎片离子峰的叠合串联质谱图,据此可以方便准确地角析出多肽的氨基酸序列。实验结果表明,这种方法在多肽的质谱法测定中具有一定的实用性。     

DtaRefinery, a Software Tool for Elimination of Systematic Errors from Parent Ion Mass Measurements in Tandem Mass Spectra Data Sets

Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and high throughput MS/MS fragmentation have become widely available in recent years, allowing for significantly better discrimination between true and false MS/MS peptide identifications by the application of a relatively narrow window for maximum allowable deviations of measured parent ion masses. To fully gain the advantage of highly accurate parent ion mass measurements, it is important to limit systematic mass measurement errors. Based on our previous studies of systematic biases in mass measurement errors, here, we have designed an algorithm and software tool that eliminates the systematic errors from the peptide ion masses in MS/MS data. We demonstrate that the elimination of the systematic mass measurement errors allows for the use of tighter criteria on the deviation of measured mass from theoretical monoisotopic peptide mass, resulting in a reduction of both false discovery and false negative rates of peptide identification. A software implementation of this algorithm called DtaRefinery reads a set of fragmentation spectra, searches for MS/MS peptide identifications using a FASTA file containing expected protein sequences, fits a regression model that can estimate systematic errors, and then corrects the parent ion mass entries by removing the estimated systematic error components. The output is a new file with fragmentation spectra with updated parent ion masses. The software is freely available.A key component in modern proteomics research is peptide identification through LC coupled to tandem MS where a selected parent or precursor ion from an MS scan undergoes fragmentation by collisionally activated/induced dissociation or any other methods (1). Identification of the putative peptides corresponding to the parent ions selected for fragmentation is performed by matching the observed to the theoretical MS/MS fragmentation patterns. The first step in the data analysis process is to create a set of input files representing the fragmentation spectra. For example, for the data sets from LTQ1 FT and LTQ Orbitrap instruments, software tools such as extract_msn (part of BioWorks software package, Thermo Electron, San Jose, CA) or DeconMSn (2) are often used for this step, creating files in “.dta” or other formats for the fragmentation spectra. These files contain the mass and charge of the parent ion and observed fragmentation pattern in the form of a list of m/z and intensity pairs. Once created, database search tools such as SEQUEST (3), X!Tandem (4), OMSSA (5), InsPect (6), MASCOT (7), Spectrum Mill (8), RAId_DbS (9), and others are used to analyze the .dta files to associate each MS/MS fragmentation pattern with a corresponding putative peptide sequence. Therefore, MS/MS fragmentation pattern information plays a primary role and ultimately can be used as essentially the only type of information for peptide identification in LC-MS/MS experiments (4, 10). However, in this case, the lack of constraint on parent ion mass measurement error (MME) results in a high rate of incorrect peptide identifications. Conversely, improved mass accuracy helps to achieve a better discrimination between true and false peptide identifications (8, 11).To fully utilize the high mass measurement accuracy of modern instruments, it is advantageous to eliminate systematic mass measurement errors. Eliminating the systematic MME component results in a more coherent distribution of anecdotal MME and helps to reduce the maximum allowable deviation (of the measured mass from the theoretical peptide mass) for true peptide identifications (12). Multiple sources of variation can cause systematic errors in mass measurements; for example, power supply voltage drift over time, space charge effects, differing ion compositions within the cell, ion intensity variation, and outdated calibration coefficients (for a review, see Ref. 13).The use of internal calibrants or standards co-injected with the sample into the mass spectrometer (14–18) can help reduce such systematic errors to a certain extent but may have some practical limitations. Internal calibrants well capture scan to scan variations and correct for time and/or total ion current (TIC)-dependent systematic errors, which are associated with the entire MS scan. Technically, it is also possible to correct for intensity-related dependence of MME, which is quite prominent on a certain type of instrument (13). However, it will require an increase in the number of calibrants to cover the entire dynamic range of the mass spectrometer. In addition, in certain cases, the calibration function (MME dependence on m/z parameter) shows evidence of non-linear behavior and may not be corrected by one or even a few calibrants (for example, see Fig. 7 in Ref. 13 and Fig. 5 in Ref. 19). Thus, there still potentially can be residual systematic biases even if internal calibration is applied.Open in a separate windowFig. 5.Scatter plots showing parent ion MME before (blue) and after (green) additive regression refinement for different parameters: scan number, m/z, log₁₀ of ion intensity, and total ion current of trapped ions.Open in a separate windowFig. 7.Mass error distribution histograms for all peptide to spectrum assignments with 2+ charge produced by SEQUEST searches for fully tryptic peptides within 136 LC-MS/MS data sets. No XCorr or ΔCn filtering criteria have been applied. The bin width is 0.5 ppm. The DtaRefinery tool has noticeably reduced the width of the MME distribution histogram and thus maximum allowable deviation of the parent ion mass for true peptide identifications from about 10 (left, blue) to about 3 (right, green) ppm.A number of alternative approaches based on knowledge about the sample content have been introduced recently (13, 20–25). Instead of spiked or co-injected calibrants, they make use of putative peptide identifications as internal calibrants. Initially, such recalibration approaches have been limited mostly to peptide identifications based on either high accuracy measurements of peptide masses alone (21) or in combination with LC retention times (13, 20). Recently, we and others have proposed that partial sample knowledge can also be utilized for recalibrating parent ion masses in MS/MS data sets obtained on hybrid instrumentation (12, 13, 23–26). In one implementation, described as “postexperiment monoisotopic mass filtering and refinement” (26), the parent ion masses in the .dta files were replaced with the mass of the ion averaged over all scans in which it was observed followed by a simple recalibration. That recalibration assumes one constant value of the systematic error for the entire data set, which can be estimated by zero centering the parent ion MME distribution. There is another implementation that resembles this concept (23, 25) that is based on using putative peptide identifications as calibrants from neighboring MS/MS scans, that is scans either immediately before or after the MS scan chosen for recalibration. We reviewed this approach earlier (13) and suggested that it has a potential benefit, although it does have a few potential limitations that need to be addressed, e.g. disregarding individual ion intensity information, use of only linear calibration functions, and lack of control of the m/z range covered by putative calibrants. Recently, another MS/MS data set recalibration tool has been developed (24) that incorporates the time component into recalibration equation. However, in this case, the authors assumed linear relationships of MME and time, which is rarely the case (13) and may serve only as a rough approximation. In line with our previous report (13), we recommended using a multidimensional non-parametric recalibration, an approach that is not limited by the disadvantages mentioned above.To derive practical benefits from our previous study on systematic MME behavior for the proteomics community, here, we developed an algorithm for eliminating systematic biases in the parent ion MME for the MS/MS data sets and implemented it into a software tool. This tool, DtaRefinery, is designed to work in tandem with either extract_msn or DeconMSn (Fig. 1). DtaRefinery first reads a set of fragmentation spectra from a concatenated .dta file (supplemental Fig. 1) produced by either extract_msn or DeconMSn. Next, it internally calls an MS/MS search engine to identify putative peptides based on matching MS/MS fragmentation patterns against an appropriate, user-specified FASTA file containing sequences of proteins expected to be present in the sample. Conceptually, it does not matter which MS/MS search engine is used, although we prefer X!Tandem as it is free, open source, and relatively lightweight. X!Tandem is included in the package: there is no need for installation of a search engine. All the database searching is done behind the scene, and the generated files with MS/MS search results exist only temporarily and are deleted after the parsing step. DtaRefinery then computes the parent ion MME based on observed masses and theoretical monoisotopic masses derived from peptide sequences. In the next step, it examines the parent ion MME of the peptides for dependences on scan number, m/z, log₁₀ of ion intensity, and TIC. If dependences are found, DtaRefinery trains a regression-based prediction model for the systematic components of the MME (Fig. 2). If an estimated prediction error of the regression model indicates an improvement of MME, then the model is applied to correct the observed parent ion masses within the entire MS/MS data set. This process is applied iteratively until no systematic MME dependences are detected for all of the considered explanatory variables (e.g. m/z, scan number, log₁₀ of ion intensity, and TIC). At this final point, a new concatenated .dta file is created with corrected parent ion masses. It also produces quality control images, allowing the researcher to visually explore the behavior of the MME and the log file with the records on all the processing steps and potential errors.Open in a separate windowFig. 1.Flowchart showing position of DtaRefinery in MS/MS data processing pipeline. The first step is extraction of MS/MS data from a binary file with DeconMSn or extract_msn. Next, the extracted MS/MS data are processed with DtaRefinery or alternatively can be directly used for searching the peptide identifications. The format of the refined data produced by DtaRefinery is the same as originally extracted by DeconMSn or extract_msn. Finally, the refined MS/MS data can be searched using the MS/MS search engine of choice. Note that DtaRefinery uses the X!Tandem MS/MS search engine. It is incorporated into the tool and independent of the search engine of choice used in the pipeline.Open in a separate windowFig. 2.Example showing correction of highly pronounced systematic parent ion MME along dimension of scan number parameter. The example is an actual LC-MS/MS analysis on an LTQ Orbitrap instrument that is out of calibration with significant sample overloading. Because of sample overloading, the automatic gain control system was not able to properly modulate the ion population within the Orbitrap cell, resulting in space charge effects causing noticeable systematic MME. However, after applying the DtaRefinery and subtracting the systematic MME components predicted by the regression models trained in the space of all four parameters (scan number, m/z, log₁₀ of ion intensity, and TIC), the mean of the MME distribution shifts from −16 ppm to approximately 0 ppm, and the standard deviation contracts from 4.3 to 0.8 ppm (data not shown). A, the individual parent ion MME plotted as a function of scan number (blue circles). B, smoothing the MME residuals with Tukey''s running median (yellow circles). C, fitting a spline function into smoothed data to have a continuous function for prediction of systematic MME (red line). D, corrected parent ion MME by subtracting the systematic MME predicted by the model trained using only the scan number parameter.     

肽段的理论串联质谱图预测方法研究进展

基于串联质谱技术的蛋白质组学已经成为生命科学领域的重要工具,其中肽段的理论串联质谱图(通常也被称为二级谱图)预测问题在近年来广受关注.大量高质量质谱数据的积累和计算技术的发展为此问题的解决提供了有效途径.肽段的理论二级谱图预测的方法可以分为两大类,一类是基于物理模型的方法,即基于移动质子模型的方法,例如MassAnalyzer、MS-Simulator;另一类是基于机器学习的方法,包括集成学习相关算法和基于神经网络的方法,例如PeptideART、MS2PIP、MS2PBPI和p Deep等.本文对这两大类方法进行了整理和综述,并简要指出了目前理论谱图预测方法存在的一些不足,展望了未来的发展方向.     

Quality Assessment of Tandem Mass Spectra by Using a Weighted K-Means

Introduction  The tandem mass spectrometer is a powerful tool with which to generate peptide (tandem) mass spectrum data for the analysis of complex biological protein mixtures in genomic-related disease cell lines. However, the majority of experimental tandem mass spectra cannot be interpreted by any database search engines. One of the main reasons this happens is that majority of experimental spectra are of quality too poor to be interpretable. Interpreting these “un-interpretable” spectra is a waste of time. Therefore, it is worthwhile to determine the quality of mass spectra before any interpretation. Objectives  This paper proposes an approach to classifying tandem spectra into two groups: one with high quality and one with poor quality. Methods  The proposed approach has two steps. First, each spectrum is mapped to a feature vector which describes the quality of the spectrum. Then, a weighted K-means clustering method is applied in order to classify the tandem mass spectra. Results and Conclusion  Computational experiments illustrate that one cluster contains the majority of the high-quality spectra, while the other contains the majority of the poor-quality spectra. This result indicates that if we just search the spectra in the high-quality cluster, we can save the time for searching the majority of poor-quality spectra while losing a minimal amount of high-quality spectra. The software created for this work is available upon request.     

SweetSEQer,Simple de Novo Filtering and Annotation of Glycoconjugate Mass Spectra

The past 15 years have seen significant progress in LC-MS/MS peptide sequencing, including the advent of successful de novo and database search methods; however, analysis of glycopeptide and, more generally, glycoconjugate spectra remains a much more open problem, and much annotation is still performed manually. This is partly because glycans, unlike peptides, need not be linear chains and are instead described by trees. In this study, we introduce SweetSEQer, an extremely simple open source tool for identifying potential glycopeptide MS/MS spectra. We evaluate SweetSEQer on manually curated glycoconjugate spectra and on negative controls, and we demonstrate high quality filtering that can be easily improved for specific applications. We also demonstrate a high overlap between peaks annotated by experts and peaks annotated by SweetSEQer, as well as demonstrate inferred glycan graphs consistent with canonical glycan tree motifs. This study presents a novel tool for annotating spectra and producing glycan graphs from LC-MS/MS spectra. The tool is evaluated and shown to perform similarly to an expert on manually curated data.Protein glycosylation is a common modification, affecting ∼50% of all expressed proteins (1). Glycosylation affects critical biological functions, including cell-cell recognition, circulating half-life, substrate binding, immunogenicity, and others (2). Regrettably, determining the exact role glycosylation plays in different biological contexts is slowed by a dearth of analytical methods and of appropriate software. Such software is crucial for performing and aiding experts in data analysis complex glycosylation.Glycopeptides are highly heterogeneous in regard to glycan composition, glycan structure, and linkage stereochemistry in addition to the tens of thousands of possible peptides. The analysis of protein glycosylation is often segmented into three distinct types of mass spectrometry experiments, which together help to resolve this complexity. The first analyzes enzymatically or chemically released glycans (which may or may not be chemically modified), and the second determines glycosylation sites after release of glycans from peptides (the resulting mass spectra allow detection of glycosylation sites and the glycans on those sites simultaneously). The third determines the glycosylation sites and the glycans on those sites simultaneously, by MS of intact glycopeptides. Frequently, researchers will perform all three types of analysis, with the first two types providing information about possible combinations of glycan structures and peptides that could be found in the third experiment. Using this MS1 information, the problem is reduced to matching masses observed with a combinatorial pool of all possible glycans and all possible glycosylated peptides within a sample; however, this combinatorial approach alone is insufficient (3), and tandem mass spectrometry can provide copious additional information to help resolve the glycopeptide content from complex samples.The similar problem of inferring peptide sequences from MS/MS spectra has received considerably more attention. Peptide inference is more constrained than glycan inference, because the chain of MS/MS peaks corresponds to a linear peptide sequence; given an MS/MS spectrum, the linear peptide sequence can be inferred through brute force or dynamic programming via de novo methods (4–6) as described in Ref. 7. Additionally, the possible search space of peptides can be dramatically lowered by using database searching (8–21) as described in Ref. 7, which compares the MS/MS spectrum to the predicted spectra from only those peptides resulting from a protein database or translated open reading frames (ORFs) of a genomic database.The possible search space of glycans is larger than the search space of peptides because, in contrast to linear peptide chains, glycans may form branching trees. Identifying glycans using database search methodologies is impractical, as it is impractical to define the database when the detailed activities of the set of glycosyltransferases are not defined. Generating an overly large database would artificially inflate the set of incompletely characterized spectra, and too small of a search space would lead to inaccurate results. Furthermore, as glycosylation is not a template-driven process, no clear choice for a database matching approach is available, and de novo sequencing is therefore a more appropriate approach.As a result, few desirable software options are available for the high throughput analysis of tandem mass spectrometry data from intact glycopeptides (as noted in a recent review (22)). In fact, manual annotation of spectra is still commonplace, despite being slow and despite the potential for disagreement between different experts. Some available software requires user-defined lists of glycan and/or peptide masses as input, which is suboptimal from a sample consumption and throughput perspective (23, 24). These lists must typically be generated by parallel experiments or simply hypothesized a priori, meaning omissions in either list may affect the results. Furthermore, some software does not work on batched input files, meaning each spectrum must be analyzed separately (23, 25–28). Moreover, there is an even greater lack of open source software for glycoproteomics, so modifying the existing software for the researchers individual applications is not easily achieved. The one open source tool that we know of (GlypID) is applicable only to the analysis of glycopeptide spectra acquired from a very specialized workflow, which requires MS1, CID, and higher-energy C-trap type dissociation (HCD) spectra (29). With that approach, oxonium ions from HCD spectra are necessary to predict the glycan class; potential peptide lists are queried by precursor m/z values (requiring accurate a priori knowledge of all modifications), and possible theoretical “N-linked” precursor m/z values are used to select candidate spectra (using templates, unlike de novo characterization). As a result, the tool is specialized and limited to analysis of “N-linked” glycopeptide spectra from very specific experimental setups.Free, open-source glycoproteomic software capable of batch analysis of general tandem mass spectrometry spectra of glycoconjugates is sorely needed. In this work, we present SweetSEQer, a tool for de novo analysis of tandem mass spectra of glycoconjugates (the most general class of spectra containing fragmentation involving sugars). Furthermore, because SweetSEQer is so general and simple, and because it does not require specific experimental setup, it is widely applicable to the analysis of general glycoconjugate spectra (e.g. it is already applicable to “O-linked” glycopeptide and glycoconjugate spectra). Moreover, because it is an open source and does not use external software, it not only eschews solving problems like MS1 deisotoping, it can also be easily customized and even used to augment and complement existing tools like GlypID (and, because we do not use a “copyleft” software license, our algorithm and code can even be added to non-open source and proprietary variants).SweetSEQer''s performance was tested on a validated, manually annotated set of glycoconjugate identifications from a urinary glycoproteomics study. Specificity was demonstrated by showing a low identification rate on negative control spectra from Escherichia coli. Annotated structures are shown to be consistent by a human expert by demonstrating a high overlap in identified glycan fragment ions, as well as a consistency between SweetSEQer''s predicted glycan graph and glycan chains produced by an expert. Our simple object-oriented python implementation is freely available (Apache 2.0 license) on line.     

Combinatorial Approach for Large-scale Identification of Linked Peptides from Tandem Mass Spectrometry Spectra

The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein–protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides.The study of protein–protein interactions is crucial to understanding how cellular systems function because proteins act in concert through a highly organized set of interactions. Most cellular processes are carried out by large macromolecular assemblies and regulated through complex cascades of transient protein–protein interactions (1). In the past several years numerous high-throughput studies have pioneered the systematic characterization of protein–protein interactions in model organisms (2–4). Such studies mainly utilize two techniques: the yeast two-hybrid system, which aims at identifying binary interactions (5), and affinity purification combined with tandem mass spectrometry analysis for the identification of multi-protein assemblies (6–8). Together these led to a rapid expansion of known protein–protein interactions in human and other model organisms. Patche and Aloy recently estimated that there are more than one million interactions catalogued to date (9).But despite rapid progress, most current techniques allow one to determine only whether proteins interact, which is only the first step toward understanding how proteins interact. A more complete picture comes from characterizing the three-dimensional structures of protein complexes, which provide mechanistic insights that govern how interactions occur and the high specificity observed inside the cell. Traditionally the gold-standard methods used to solve protein structures are x-ray crystallography and NMR, and there have been several efforts similar to structural genomics (10) aiming to comprehensively solve the structures of protein complexes (11, 12). Although there has been accelerated growth of structures for protein monomers in the Protein Data Bank in recent years (11), the growth of structures for protein complexes has remained relatively small (9). Many factors, including their large size, transient nature, and dynamics of interactions, have prevented many complexes from being solved via traditional approaches in structural biology. Thus, the development of complementary analytical techniques with which to probe the structure of large protein complexes continues to evolve (13–18).Recent developments have advanced the analysis of protein structures and interaction by combining cross-linking and tandem mass spectrometry (17, 19–24). The basic idea behind this technique is to capture and identify pairs of amino acid residues that are spatially close to each other. When these linked pairs of residues are from the same protein (intraprotein cross-links), they provide distance constraints that help one infer the possible conformations of protein structures. Conversely, when pairs of residues come from different proteins (interprotein cross-links), they provide information about how proteins interact with one another. Although cross-linking strategies date back almost a decade (25, 26), difficulty in analyzing the complex MS/MS spectrum generated from linked peptides made this approach challenging, and therefore it was not widely used. With recent advances in mass spectrometry instrumentation, there has been renewed interest in employing this strategy to determine protein structures and identify protein–protein interactions. However, most studies thus far have been focused on purified protein complexes. With today''s mass spectrometers being capable of analyzing tens of thousands of spectra in a single experiment, it is now potentially feasible to extend this approach to the analysis of complex biological samples. Researchers have tried to realize this goal using both experimental and computational approaches. Indeed, a plethora of chemical cross-linking reagents are now available for stabilizing these complexes, and some are designed to allow for easier peptide identification when employed in concert with MS analysis (20, 27, 28). There have also been several recent efforts to develop computational methods for the automatic identification of linked peptides from MS/MS spectra (29–36). However, because of the lack of large annotated training data, most approaches to date either borrow fragmentation models learned from unlinked, linear peptides or learn the fragmentation statistics from training data of limited size (30, 37), which might not generalize well across different samples. In some cases it is possible to generate relatively large training data, but it is often very labor intensive and involves hundreds of separate LC-MS/MS runs (36). Here, employing disulfide-bridged peptides as an example, we propose a novel method that uses a combinatorial peptide library to (a) efficiently generate a large mass spectral reference dataset for linked peptides and (b) use these data to automatically train our new algorithm, MXDB, which can efficiently and accurately identify linked peptides from MS/MS spectra.     

An Unsupervised, Model-Free, Machine-Learning Combiner for Peptide Identifications from Tandem Mass Spectra

As the speed of mass spectrometers, sophistication of sample fractionation, and complexity of experimental designs increase, the volume of tandem mass spectra requiring reliable automated analysis continues to grow. Software tools that quickly, effectively, and robustly determine the peptide associated with each spectrum with high confidence are sorely needed. Currently available tools that postprocess the output of sequence-database search engines use three techniques to distinguish the correct peptide identifications from the incorrect: statistical significance re-estimation, supervised machine learning scoring and prediction, and combining or merging of search engine results. We present a unifying framework that encompasses each of these techniques in a single model-free machine-learning framework that can be trained in an unsupervised manner. The predictor is trained on the fly for each new set of search results without user intervention, making it robust for different instruments, search engines, and search engine parameters. We demonstrate the performance of the technique using mixtures of known proteins and by using shuffled databases to estimate false discovery rates, from data acquired on three different instruments with two different ionization technologies. We show that this approach outperforms machine-learning techniques applied to a single search engine’s output, and demonstrate that combining search engine results provides additional benefit. We show that the performance of the commercial Mascot tool can be bested by the machine-learning combination of two open-source tools X!Tandem and OMSSA, but that the use of all three search engines boosts performance further still. The Peptide identification Arbiter by Machine Learning (PepArML) unsupervised, model-free, combining framework can be easily extended to support an arbitrary number of additional searches, search engines, or specialized peptide–spectrum match metrics for each spectrum data set. PepArML is open-source and is available from . Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users.     

Deconvolution and Database Search of Complex Tandem Mass Spectra of Intact Proteins: A COMBINATORIAL APPROACH*

Top-down proteomics studies intact proteins, enabling new opportunities for analyzing post-translational modifications. Because tandem mass spectra of intact proteins are very complex, spectral deconvolution (grouping peaks into isotopomer envelopes) is a key initial stage for their interpretation. In such spectra, isotopomer envelopes of different protein fragments span overlapping regions on the m/z axis and even share spectral peaks. This raises both pattern recognition and combinatorial challenges for spectral deconvolution. We present MS-Deconv, a combinatorial algorithm for spectral deconvolution. The algorithm first generates a large set of candidate isotopomer envelopes for a spectrum, then represents the spectrum as a graph, and finally selects its highest scoring subset of envelopes as a heaviest path in the graph. In contrast with other approaches, the algorithm scores sets of envelopes rather than individual envelopes. We demonstrate that MS-Deconv improves on Thrash and Xtract in the number of correctly recovered monoisotopic masses and speed. We applied MS-Deconv to a large set of top-down spectra from Yersinia rohdei (with a still unsequenced genome) and further matched them against the protein database of related and sequenced bacterium Yersinia enterocolitica. MS-Deconv is available at http://proteomics.ucsd.edu/Software.html.Top-down proteomics is a mass spectrometry-based approach for identification of proteins and their post-translational modifications (PTMs)1 (1–14). Unlike the “bottom-up” approach where proteins are first digested into peptides and then a peptide mixture is analyzed by mass spectrometry, the top-down approach analyzes intact proteins. Thus, it has advantages in detecting and localizing PTMs as well as identifying multiple protein species (e.g. proteolytically processed protein species). Despite its advantages, top-down proteomics presents many challenges. These include requirement of high sample quantity, sophisticated instrumentation, protein separation, and robust computational analysis tools. For this reason, top-down proteomics has rarely been used for analyzing complex mixtures (12–18), and it is typically used to study single purified proteins. However, this situation is quickly changing with recent top-down studies of complex protein mixtures (14, 19).Because of the existence of natural isotopes, fragment ions of the same chemical formula and charge state are usually represented by a collection of spectral peaks in tandem mass spectra called an isotopomer envelope. The monoisotopic mass of a chemical formula is the sum of the masses of the atoms using the principal (most abundant) isotope for each element. Spectral deconvolution focuses on grouping spectral peaks into isotopomer envelopes. By doing so, the charge state and monoisotopic mass of each envelope are effectively determined. A complex multi-isotopic peak list in the m/z space is translated into a simple monoisotopic mass list that is easier to analyze.Given the monoisotopic mass and charge state of a fragment ion, its theoretical isotopic distribution can be predicted by assuming the fragment ion has an average elemental composition with respect to its mass (20) or using its precise elemental composition if the protein is known. Exploiting this, many deconvolution methods use theoretical isotopic distributions to detect and evaluate candidate isotopomer envelopes, which is the envelope detection problem (Fig. 1). To evaluate the fit of a candidate envelope to its theoretical isotopic distribution, many metrics have been proposed (20–32).Open in a separate windowFig. 1.Envelope detection. a, a theoretical isotopic distribution is predicted with the monoisotopic mass and charge state of a fragment ion. b, an observed envelope is detected by mapping peaks in the theoretical distribution to the spectrum. c, match between the theoretical isotopic distribution and the observed envelope. d, the theoretical isotopic distribution is scaled (the intensities of the peaks are multiplied by a constant) to have the best fit with the intensities of peaks in the observed envelope. Finally, a score for the observed envelope can be computed by comparing it with the intensity-scaled theoretical isotopic distribution.The candidate envelopes often overlap and share peaks, leading to a combinatorial problem of selecting the list of envelopes that best explains the spectrum (Fig. 2). In contrast to the well studied envelope detection problem, the envelope selection problem remains poorly explored. Most deconvolution algorithms follow a simple greedy approach to selecting the set of envelopes where the highest scoring envelopes are iteratively selected and removed from the spectrum. Although this approach often generates reasonable sets of envelopes for simple spectra, its performance deteriorates in cases of complex spectra.Open in a separate windowFig. 2.Envelope selection problem. Overlapping envelopes lead to a difficult combinatorial problem of selecting an optimal set of envelopes. We illustrate two cases where a deconvolution method that follows a greedy envelope selection outputs the envelope E₂, whereas the optimal solution consists of the envelopes E₁ and E₃. Example a illustrates the case where envelopes do not share peaks, and example b illustrates the case where envelopes share a spectral peak (E₁ and E₃).In particular, the greedy approach performs well when the envelopes are distributed sparsely along the m/z axis. Large proteins have many fragments that appear in multiple charge states. The high number of envelopes/peaks and the small m/z spread of the fragments with high charge states result in narrow m/z regions with high peak density. In these peak-dense regions, envelopes may overlap and share peaks, and the greedy approach and even manual interpretation often fail to find the optimal combination of envelopes (supplemental Fig. 1).Several methods have been proposed to explore the envelope selection problem. McIlwain et al. (33) presented a dynamic programming algorithm for selecting a set of envelopes such that the m/z ranges of the envelopes do not overlap. This non-overlapping condition becomes too restrictive for complex spectra of intact proteins. Samuelsson et al. (34) proposed a method that follows a non-negative sparse regression scheme. Du and Angeletti (35) and Renard et al. (36) addressed the envelope selection problem as a statistical problem of variable selection and used LASSO to solve it.Here, we present MS-Deconv, a combinatorial algorithm for spectral deconvolution. MS-Deconv (i) generates a large set of candidate envelopes, (ii) constructs an envelope graph encoding all envelopes and relationships between them, and (iii) finds a heaviest path in the envelope graph. Although the envelope graph of a complex spectrum is large (exceeding a million nodes in some cases), the heaviest path algorithm can efficiently find an optimal set of envelopes. MS-Deconv explicitly scores combinations of candidate envelopes rather than individual envelopes as in previous approaches.We tested MS-Deconv on a data set of top-down spectra from known proteins and evaluated the monoisotopic masses recovered by MS-Deconv. A mass was classified as a true positive if it was matched to the monoisotopic mass of a theoretical fragment ion of the protein within a specific parts per million (ppm) tolerance. We compared the performance of MS-Deconv with the widely used Thrash (20) and Xtract (37) and demonstrated that, with a few exceptions, MS-Deconv recovers more true positive masses. For example, for the collisionally activated dissociation (CAD) spectrum of bacteriorhodopsin (BR) with charge 10, the percentage of true positive masses among the top 150 masses is above 70% for MS-Deconv and less than 50% for Thrash. Additionally, MS-Deconv is ∼33 times faster than Thrash and 4 times faster than Xtract. Furthermore, MS-Deconv implements some user-friendly features: (i) outputs the set of peptide sequence tags, (ii) provides protein and spectral annotations, and (iii) allows one to inspect the recovered envelopes. We also tested MS-Deconv on a large LC-MS/MS data set from Yersinia rohdei (with a still unsequenced genome) (19). Y. rohdei is a non-pathogenic bacterium that is often used as a simulant for the potential bioterrorism agent Yersinia pestis, the causative agent of plague. We applied MS-Deconv to extract monoisotopic mass lists from top-down spectra and compared the mass lists with those reported by Thrash. We used ProSightPC (38) and the spectral alignment algorithm (39) to identify related proteins from a protein database of Yersinia enterocolitica (with a closely related and sequenced genome). The results demonstrated that MS-Deconv reported more matched fragments than Thrash for most proteins. Additionally, using spectral alignment, we identified eight proteins in Y. rohdei that were not reported in the ProSightPC-based searches (19) of the Y. enterocolitica protein database.     

Quantifying the Labeling and the Levels of Plant Cell Wall Precursors Using Ion Chromatography Tandem Mass Spectrometry

The biosynthesis of cell wall polymers involves enormous fluxes through central metabolism that are not fully delineated and whose regulation is poorly understood. We have established and validated a liquid chromatography tandem mass spectrometry method using multiple reaction monitoring mode to separate and quantify the levels of plant cell wall precursors. Target analytes were identified by their parent/daughter ions and retention times. The method allows the quantification of precursors at low picomole quantities with linear responses up to the nanomole quantity range. When applying the technique to Arabidopsis (Arabidopsis thaliana) T87 cell cultures, 16 hexose-phosphates (hexose-Ps) and nucleotide-sugars (NDP-sugars) involved in cell wall biosynthesis were separately quantified. Using hexose-P and NDP-sugar standards, we have shown that hot water extraction allows good recovery of the target metabolites (over 86%). This method is applicable to quantifying the levels of hexose-Ps and NDP-sugars in different plant tissues, such as Arabidopsis T87 cells in culture and fenugreek (Trigonella foenum-graecum) endosperm tissue, showing higher levels of galacto-mannan precursors in fenugreek endosperm. In Arabidopsis cells incubated with [U-¹³C_Fru]sucrose, the method was used to track the labeling pattern in cell wall precursors. As the fragmentation of hexose-Ps and NDP-sugars results in high yields of [PO₃]⁻/or [H₂PO₄]⁻ ions, mass isotopomers can be quantified directly from the intensity of selected tandem mass spectrometry transitions. The ability to directly measure ¹³C labeling in cell wall precursors makes possible metabolic flux analysis of cell wall biosynthesis based on dynamic labeling experiments.Plant cell walls are the most abundant renewable resources (Pauly and Keegstra, 2008a). Much of the current biotechnological research on plant cell wall synthesis involves manipulating these biosynthetic processes to obtain higher concentrations of starches or oil, which show much promise in biofuel production, or to alter cell wall composition for easier breakdown. A detailed knowledge of these processes is essential to understanding and utilizing plant cell wall materials as well as for progress in understanding plant growth and structural development (Pauly and Keegstra, 2008b). However, research into cell wall biosynthesis has been hindered by our limited understanding of the metabolic processes that produce cell walls and particularly their regulation. Progress in this area is limited by the difficulty of differentiating among the compounds involved and of analyzing the fluxes through the biochemical network of wall biosynthesis. Many of the metabolic steps involve isomeric sugars, including hexose-Ps and nucleotide-sugars (NDP-sugars) that serve as direct precursors to plant cell wall biosynthesis. Separate quantification of these sugars has been difficult to achieve.Much of the current research on identifying and differentiating among different metabolic pathways involves the use of chromatography and mass spectrometry (MS; Wolfender et al., 2009). It has been found that liquid chromatography (LC), when linked to a triple quadrupole mass spectrometer (tandem MS [MS/MS]), can be a powerful tool to detect and specifically quantify several classes of metabolic compounds (Allwood and Goodacre, 2010). After initial compound separation by LC, analytes are directed to a triple quadrupole mass spectrometer (MS/MS), where the initial two quadrupoles separate the compounds for detection in the third quadrupole, first by selection of particular mass-to-charge (m/z) ratios of the ionized parent compounds in the first quadrupole, then by fragmentation of the compounds in the second quadrupole (Arrivault et al., 2009). This coupling method of LC-MS/MS to identify compounds has been used with several metabolites involved in plant primary metabolism recently (Cruz et al., 2008; Arrivault et al., 2009).Several studies have reported the separation of phosphorylated metabolites using LC-MS/MS. We are particularly interested in analyzing such compounds because plant cell wall biosynthesis involves a range of phosphorylated precursors, mainly NDP-sugars and hexose-Ps (Feingold and Barber, 1990; Fry 2000, 2004; Seifert, 2004; Somerville et al., 2004; Sharples and Fry, 2007). For example, Huck et al. (2003) and Luo et al. (2007) were able to separate, respectively, six and 28 intracellular metabolites involved in glycolysis, the oxidative pentose-P pathway, and the tricarboxylic acid cycle, some of which are phosphorylated. Bajad et al. (2006) were able to separate a large number of water-soluble cellular metabolites by hydrophilic interaction chromatography, but this method does not appear to separate isomers. Anion exchange chromatography was shown to be effective at separating phosphorylated intermediates involved in glycolysis (van Dam et al., 2002) and in the Calvin cycle (Cruz et al., 2008; Arrivault et al., 2009), especially when coupled with the high specificity and sensitivity of triple quadrupole MS. Moreover, anion-exchange chromatography coupled with MS/MS can be used to determine the mass isotopomer distribution of labeled compounds and Kiefer et al. (2007) were able to quantify isotope abundances in six phosphorylated metabolites in Escherichia coli.However, some of the methods that achieved good separation of four NDP-sugars did not allow quantification by MS/MS because of the eluents used (Räbinä et al., 2001), and none of the methods using coupled LC-MS/MS developed to date separates all or nearly all of the hexose-Ps and NDP-sugars known to be involved in plant cell wall biosynthesis (Turnock and Ferguson, 2007). This presents a special challenge given the fact that many of these sugar compounds are diastereoisomers and ionize similarly in traditional LC-MS/MS methods. Current methods of separating hexose-Ps and NDP-sugars also involve multiple steps of chromatographic and enzymatic separation. In a notable recent study, Sharples and Fry (2007) separated many of the compounds involved in plant cell wall biosynthesis, including hexose-Ps and NDP-sugars, and used radioactive [U-¹⁴C]Fru and [1-³H]Gal as substrates to determine their relative contributions to different cell wall components. The method used in that study involved high-voltage paper electrophoresis separation followed by mild acid hydrolysis and/or phosphatase digestion of different fractions to release neutral hexoses that were then separated by a second paper chromatography procedure. At the cost of considerable effort, this approach allowed eight compounds to be separated. However, neither this nor many of the other approaches used to date appear to have yielded absolute metabolite levels or specific activities in labeling. Metabolic flux analysis requires quantifying these compounds, their fractional and preferably also positional labeling, and the ability to analyze many time point samples. These requirements necessitated the development of a method that can be performed in medium to high throughput and achieves compound separation and quantitation, such as LC-MS/MS, and that also yields detailed labeling information.In this study we have developed and validated a robust and sensitive LC-MS/MS method that successfully allows us to separate and quantify the levels and isotopic labeling of plant cell wall precursors. Using plant tissues from fenugreek (Trigonella foenum-graecum) endosperms and Arabidopsis (Arabidopsis thaliana) cell cultures, 12 hexose-Ps and NDP-sugars known to be involved in plant cell wall biosynthesis were separated and quantified. The direct analysis of intracellular cell wall precursors and their isotopic labeling significantly expands the set of tools for assessing the dynamics and regulation of cell wall biosynthesis, including the potential for dynamic metabolic flux analysis.     

Expert System for Computer-assisted Annotation of MS/MS Spectra

An important step in mass spectrometry (MS)-based proteomics is the identification of peptides by their fragment spectra. Regardless of the identification score achieved, almost all tandem-MS (MS/MS) spectra contain remaining peaks that are not assigned by the search engine. These peaks may be explainable by human experts but the scale of modern proteomics experiments makes this impractical. In computer science, Expert Systems are a mature technology to implement a list of rules generated by interviews with practitioners. We here develop such an Expert System, making use of literature knowledge as well as a large body of high mass accuracy and pure fragmentation spectra. Interestingly, we find that even with high mass accuracy data, rule sets can quickly become too complex, leading to over-annotation. Therefore we establish a rigorous false discovery rate, calculated by random insertion of peaks from a large collection of other MS/MS spectra, and use it to develop an optimized knowledge base. This rule set correctly annotates almost all peaks of medium or high abundance. For high resolution HCD data, median intensity coverage of fragment peaks in MS/MS spectra increases from 58% by search engine annotation alone to 86%. The resulting annotation performance surpasses a human expert, especially on complex spectra such as those of larger phosphorylated peptides. Our system is also applicable to high resolution collision-induced dissociation data. It is available both as a part of MaxQuant and via a webserver that only requires an MS/MS spectrum and the corresponding peptides sequence, and which outputs publication quality, annotated MS/MS spectra (www.biochem.mpg.de/mann/tools/). It provides expert knowledge to beginners in the field of MS-based proteomics and helps advanced users to focus on unusual and possibly novel types of fragment ions.In MS-based proteomics, peptides are matched to peptide sequences in databases using search engines (1–3). Statistical criteria are established for accepted versus rejected peptide spectra matches based on the search engine score, and usually a 99% certainty is required for reported peptides. The search engines typically only take sequence specific backbone fragmentation into account (i.e. a, b, and y ions) and some of their neutral losses. However, tandem mass spectra—especially of larger peptides—can be quite complex and contain a number of medium or even high abundance peptide fragments that are not annotated by the search engine result. This can result in uncertainty for the user—especially if only relatively few peaks are annotated—because it may reflect an incorrect identification. However, the most common cause of unlabeled peaks is that another peptide was present in the precursor selection window and was cofragmented. This has variously been termed “chimeric spectra” (4–6), or the problem of low precursor ion fraction (PIF)¹ (7). Such spectra may still be identifiable with high confidence. The Andromeda search engine in MaxQuant, for instance, attempts to identify a second peptide in such cases (8, 9). However, even “pure” spectra (those with a high PIF) often still contain many unassigned peaks. These can be caused by different fragment types, such as internal ions, single or combined neutral losses as well as immonium and other ion types in the low mass region. A mass spectrometric expert can assign many or all of these peaks, based on expert knowledge of fragmentation and manual calculation of fragment masses, resulting in a higher degree of confidence for the identification. However, there are more and more practitioners of proteomics without in depth training or experience in annotating MS/MS spectra and such annotation would in any case be prohibitive for hundreds of thousands of spectra. Furthermore, even human experts may wrongly annotate a given peak—especially with low mass accuracy tandem mass spectra—or fail to consider every possibility that could have resulted in this fragment mass.Given the desirability of annotating fragment peaks to the highest degree possible, we turned to “Expert Systems,” a well-established technology in computer science. Expert Systems achieved prominence in the 1970s and 1980s and were meant to solve complex problems by reasoning about knowledge (10, 11). Interestingly, one of the first examples was developed by Nobel Prize winner Joshua Lederberg more than 40 years ago, and dealt with the interpretation of mass spectrometric data. The program''s name was Heuristic DENTRAL (12), and it was capable of interpreting the mass spectra of aliphatic ethers and their fragments. The hypotheses produced by the program described molecular structures that are plausible explanations of the data. To infer these explanations from the data, the program incorporated a theory of chemical stability that provided limiting constraints as well as heuristic rules.In general, the aim of an Expert System is to encode knowledge extracted from professionals in the field in question. This then powers a rule-based system that can be applied broadly and in an automated manner. A rule-based Expert System represents the information obtained from human specialists in the form of IF-THEN rules. These are used to perform operations on input data to reach appropriate conclusion. A generic Expert System is essentially a computer program that provides a framework for performing a large number of inferences in a predictable way, using forward or backward chains, backtracking, and other mechanisms (13). Therefore, in contrast to statistics based learning, the “expert program” does not know what it knows through the raw volume of facts in the computer''s memory. Instead, like a human expert, it relies on a reasoning-like process of applying an empirically derived set of rules to the data.Here we implemented an Expert System for the interpretation for high mass accuracy tandem mass spectrometry data of peptides. It was developed in an iterative manner together with human experts on peptide fragmentation, using the published literature on fragmentation pathways as well as large data sets of higher-energy collisional dissociation (HCD) (14) and collision-induced dissociation (CID) based peptide identifications. Our goal was to achieve an annotation performance similar or better than experienced mass spectrometrists (15), thus making comprehensively annotated peptide spectra available in large scale proteomics.     

串联质谱在多肽测序中的应用

采用纳升喷雾(Nano)技术和碰撞诱导解离(CID collision inducd dissociation)方法,在电喷雾-四极杆-飞行时间质谱(ESI-Q-TOF electrspectrometry ionization-quadrupole-time of flight)上,对两种序列部分未知的天然多肽进行从头测序(de novo sequence),结果证明质谱的de novo sequence可以方便有效的解决传统的Edman降解法测序中常见的实际问题,如末位残基的丢失,赖氨酸和亮氨酸难鉴定等,此方法的建立是对Edman降解测序法很好的补充．