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.     

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.     

Systematic evaluation of alternating CID and ETD fragmentation for phosphorylated peptides

CID has become a routine method for fragmentation of peptides in shotgun proteomics, whereas electron transfer dissociation (ETD) has been described as a preferred method for peptides carrying labile PTMs. Though both of these fragmentation techniques have their obvious advantages, they also have their own drawbacks. By combining data from CID and ETD fragmentation, some of these disadvantages can potentially be overcome because of the complementarity of fragment ions produced. To evaluate alternating CID and ETD fragmentation, we analyzed a complex mixture of phosphopeptides on an LTQ-Orbitrap mass spectrometer. When the CID and ETD-derived spectra were searched separately, we observed 2504, 491, 2584, and 3249 phosphopeptide-spectrum matches from CID alone, ETD alone, decision tree-based CID/ETD, and alternating CID and ETD, respectively. Combining CID and ETD spectra prior to database searching should, intuitively, be superior to either method alone. However, when spectra from the alternating CID and ETD method were merged prior to database searching, we observed a reduction in the number of phosphopeptide-spectrum matches. The poorer identification rates observed after merging CID and ETD spectra are a reflection of a lack of optimized search algorithms for carrying out such searches and perhaps inherent weaknesses of this approach. Thus, although alternating CID and ETD experiments for phosphopeptide identification are desirable for increasing the confidence of identifications, merging spectra prior to database search has to be carefully evaluated further in the context of the various algorithms before adopting it as a routine strategy.     

Clustering millions of tandem mass spectra

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.     

Assigning precursor–product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studies

Tandem mass spectrometry using precursor ion selection (MS/MS) is an invaluable tool for structural elucidation of small molecules. In non-targeted metabolite profiling studies, instrument duty cycle limitations and experimental costs have driven efforts towards alternate approaches. Recently, researchers have begun to explore methods for collecting indiscriminant MS/MS (idMS/MS) data in which the fragmentation process does not involve precursor ion isolation. While this approach has many advantages, importantly speed, sensitivity and coverage, confident assignment of precursor–product ion relationships is challenging, which has inhibited broad adoption of the technique. Here, we present an approach that uses open source software to improve the assignment of precursor–product relationships in idMS/MS data by appending a dataset-wide correlational analysis to existing tools. The utility of the approach was demonstrated using a dataset of standard compounds spiked into a malt-barley background, as well as unspiked human serum. The workflow was able to recreate idMS/MS spectra which are highly similar to standard MS/MS spectra of authentic standards, even in the presence of a complex matrix background. The application of this approach has the potential to generate high quality idMS/MS spectra for each detectable molecular feature, which will streamline the identification process for non-targeted metabolite profiling studies.     

Computational annotation of plant metabolomics profiles via a novel network-assisted approach

Mass spectrometry (MS) has become the analytical method of choice in plant metabolomics. Nevertheless, metabolite annotation remains a major challenge and implies the integration of structural searches in compound libraries with biological knowledge inferred from metabolite regulation studies. Here we propose a novel integrative approach to process and exploit the rich structural information contained in in-source fragmentation patterns of high-resolution LC–MS profiles. In this approach, a correlation matrix is first calculated from individual mass features extracted by xcms processing. Mass feature co-regulation patterns corresponding to metabolite in-source fragmentation are then detected and assembled into compound spectra using the R package CAMERA and processed for in silico fragment-based structure elucidation using MetFrag. We validate the performance of this approach for the rapid annotation of the twelve largest compound spectra, including four O-acyl sugars and six 17-hydroxygeranyllinalool diterpene glycosides in metabolic profiles of insect-attacked Nicotiana attenuata leaves. Additionally, we demonstrate the power of refining MetFrag metabolite annotations based on co-regulation patterns between known and unknown compounds in the correlation matrix and proposed structural annotations on two previously un-characterized O-acyl sugars. In summary, this novel approach facilitates compound annotation from in-source fragmentation patterns using correlation between intensities of mass features of one or several metabolites. Additionally, this analysis provides further support that insect herbivory activates major metabolic reconfigurations in N. attenuata leaves.     

Cleaning of raw peptide MS/MS spectra: improved protein identification following deconvolution of multiply charged peaks, isotope clusters, and removal of background noise

The dominant ions in MS/MS spectra of peptides, which have been fragmented by low-energy CID, are often b-, y-ions and their derivatives resulting from the cleavage of the peptide bonds. However, MS/MS spectra typically contain many more peaks. These can result not only from isotope variants and multiply charged replicates of the peptide fragmentation products but also from unknown fragmentation pathways, sample-specific or systematic chemical contaminations or from noise generated by the electronic detection system. The presence of this background complicates spectrum interpretation. Besides dramatically prolonged computation time, it can lead to incorrect protein identification, especially in the case of de novo sequencing algorithms. Here, we present an algorithm for detection and transformation of multiply charged peaks into singly charged monoisotopic peaks, removal of heavy isotope replicates, and random noise. A quantitative criterion for the recognition of some noninterpretable spectra has been derived as a byproduct. The approach is based on numerical spectral analysis and signal detection methods. The algorithm has been implemented in a stand-alone computer program called MS Cleaner that can be obtained from the authors upon request.     

Mediator-lipidomics: databases and search algorithms for PUFA-derived mediators

Lipid mediators (LMs) derived from PUFAs play important roles in health and disease. Databases and search algorithms are crucial, but currently unavailable, for accurate and prompt analysis of LMs via liquid chromatography-ultraviolet-tandem mass spectrometry (LC-UV-MS/MS). A novel algorithm and databases, cognoscitive-contrast-angle algorithm and databases (COCAD), were developed for the identification of LMs based on the integration of standard MS/MS spectra with chromatograms and UV spectra. Segment naming and empirical fragmentation rules were introduced to determine MS/MS ion identities, along with ion intensities used by COCAD in matching the unknown to those of authentic standards. The structures of potential LMs without synthetic and/or authentic products as standards were identified by developing theoretical databases and algorithms based on virtual LC-UV-MS/MS spectra and chromatograms. The performance of these databases and algorithms was tested by identifying LMs in murine tissues. These results indicate that COCAD has many advantages for profiling and identification of LMs compared with the conventional dot-product algorithm.     

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.     

Database-independent, database-dependent, and extended interpretation of peptide mass spectra in VEMS V2.0

The Virtual Expert Mass Spectrometrist (VEMS) program package was developed for flexible, automated, and manual de novo tandem mass spectrometry (MS/MS) protein sequencing, and includes accessory programs for matrix-assisted laser desorption/ionization-mass spectrometry (MS) interpretation, and generation of protein and peptide databases. VEMS V2.0 has been developed into a fast tool for combining database-independent and -dependent protein assignments in an extended analysis of MS/MS-peptide data. MS or MS/MS data can be directly recalibrated after the first search by fitting the data to the best search result using polynomial equations. The score function is an improvement of known scoring algorithms and can be adapted for any MS instrument type. In addition, VEMS offers a novel statistical model for evaluating the significance of the protein assignment. The novel features are illustrated by the analysis of the fragmentation spectra obtained by liquid chromatrography-MS/MS analysis of peptides from an anionic peroxidase enriched protein fraction from potato root tissue. The extended analysis mode resulted in the additional assignment of spectra for nine modified tryptic peptides and nine miscleaved peptides, in addition to the 45 spectra from regular tryptic peptides. Of the nine modified peptides, three were glycosylated.     

用于串联质谱鉴定多肽的计量方法

目前已有多种对串联质谱与数据库中多肽的理论质谱的一致性进行评估的高通量计量算法用于鸟枪法蛋白质组学 (shotgunproteomics)研究。然而这些方法操作时存在大量错误的多肽鉴定。这里提出一种新的串联质谱识别多肽序列的计量算法。该算法综合考虑了串联质谱中不同离子出现的概率、多肽的酶切位点数、理论离子与实验离子的匹配程度和匹配模式。对大容量的串联质谱数据集的测试表明 ,根据算法开发的软件PepSearch比目前最常用的软件SEQUEST有更好的鉴定准确性。PepSearch可从http : compbio.sibsnet.org projects pepsearch下载。     

HMMatch: peptide identification by spectral matching of tandem mass spectra using hidden Markov models.

Peptide identification by tandem mass spectrometry is the dominant proteomics workflow for protein characterization in complex samples. The peptide fragmentation spectra generated by these workflows exhibit characteristic fragmentation patterns that can be used to identify the peptide. In other fields, where the compounds of interest do not have the convenient linear structure of peptides, fragmentation spectra are identified by comparing new spectra with libraries of identified spectra, an approach called spectral matching. In contrast to sequence-based tandem mass spectrometry search engines used for peptides, spectral matching can make use of the intensities of fragment peaks in library spectra to assess the quality of a match. We evaluate a hidden Markov model approach (HMMatch) to spectral matching, in which many examples of a peptide's fragmentation spectrum are summarized in a generative probabilistic model that captures the consensus and variation of each peak's intensity. We demonstrate that HMMatch has good specificity and superior sensitivity, compared to sequence database search engines such as X!Tandem. HMMatch achieves good results from relatively few training spectra, is fast to train, and can evaluate many spectra per second. A statistical significance model permits HMMatch scores to be compared with each other, and with other peptide identification tools, on a unified scale. HMMatch shows a similar degree of concordance with X!Tandem, Mascot, and NIST's MS Search, as they do with each other, suggesting that each tool can assign peptides to spectra that the others miss. Finally, we show that it is possible to extrapolate HMMatch models beyond a single peptide's training spectra to the spectra of related peptides, expanding the application of spectral matching techniques beyond the set of peptides previously observed.     

Improved peptide charge state assignment

We present a new approach capable of assigning charge states to peptides based on both their intact mass spectrum and their fragmentation mass spectrum. More specifically, our approach aims at fully exploiting available information to improve correct charge assignment rate. This is achieved by using information provided by the fragmentation spectrum extensively. For low-resolution spectra, charge assignment based on fragmentation mass spectrum is better than charge assignment based on intact peptide signal only. We introduce two methods that allow to integrate information contributing to successful peptide charge state assignment. We demonstrate the performance of our algorithms on large ion trap data sets. The application of these algorithms to large-scale proteomics projects can save significant computation time and have a positive impact on identification false positive rates.     

General framework for developing and evaluating database scoring algorithms using the TANDEM search engine

MOTIVATION: Tandem mass spectrometry (MS/MS) identifies protein sequences using database search engines, at the core of which is a score that measures the similarity between peptide MS/MS spectra and a protein sequence database. The TANDEM application was developed as a freely available database search engine for the proteomics research community. To extend TANDEM as a platform for further research on developing improved database scoring methods, we modified the software to allow users to redefine the scoring function and replace the native TANDEM scoring function while leaving the remaining core application intact. Redefinition is performed at run time so multiple scoring functions are available to be selected and applied from a single search engine binary. We introduce the implementation of the pluggable scoring algorithm and also provide implementations of two TANDEM compatible scoring functions, one previously described scoring function compatible with PeptideProphet and one very simple scoring function that quantitative researchers may use to begin their development. This extension builds on the open-source TANDEM project and will facilitate research into and dissemination of novel algorithms for matching MS/MS spectra to peptide sequences. The pluggable scoring schema is also compatible with related search applications P3 and Hunter, which are part of the X! suite of database matching algorithms. The pluggable scores and the X! suite of applications are all written in C++. AVAILABILITY: Source code for the scoring functions is available from http://proteomics.fhcrc.org     

Intensity-based protein identification by machine learning from a library of tandem mass spectra

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomics owing in part to robust spectral interpretation algorithms. Widely used algorithms do not fully exploit the intensity patterns present in mass spectra. Here, we demonstrate that intensity pattern modeling improves peptide and protein identification from MS/MS spectra. We modeled fragment ion intensities using a machine-learning approach that estimates the likelihood of observed intensities given peptide and fragment attributes. From 1,000,000 spectra, we chose 27,000 with high-quality, nonredundant matches as training data. Using the same 27,000 spectra, intensity was similarly modeled with mismatched peptides. We used these two probabilistic models to compute the relative likelihood of an observed spectrum given that a candidate peptide is matched or mismatched. We used a 'decoy' proteome approach to estimate incorrect match frequency, and demonstrated that an intensity-based method reduces peptide identification error by 50-96% without any loss in sensitivity.     

Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra

Tandem mass spectrometry (MS/MS) experiments yield multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra they generate. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about peptide spectra that are common across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with new ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ～1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives rather than be analyzed as disparate datasets, as is mostly the case today.     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.     

The spectral networks paradigm in high throughput mass spectrometry

High-throughput proteomics is made possible by a combination of modern mass spectrometry instruments capable of generating many millions of tandem mass (MS(2)) spectra on a daily basis and the increasingly sophisticated associated software for their automated identification. Despite the growing accumulation of collections of identified spectra and the regular generation of MS(2) data from related peptides, the mainstream approach for peptide identification is still the nearly two decades old approach of matching one MS(2) spectrum at a time against a database of protein sequences. Moreover, database search tools overwhelmingly continue to require that users guess in advance a small set of 4-6 post-translational modifications that may be present in their data in order to avoid incurring substantial false positive and negative rates. The spectral networks paradigm for analysis of MS(2) spectra differs from the mainstream database search paradigm in three fundamental ways. First, spectral networks are based on matching spectra against other spectra instead of against protein sequences. Second, spectral networks find spectra from related peptides even before considering their possible identifications. Third, spectral networks determine consensus identifications from sets of spectra from related peptides instead of separately attempting to identify one spectrum at a time. Even though spectral networks algorithms are still in their infancy, they have already delivered the longest and most accurate de novo sequences to date, revealed a new route for the discovery of unexpected post-translational modifications and highly-modified peptides, enabled automated sequencing of cyclic non-ribosomal peptides with unknown amino acids and are now defining a novel approach for mapping the entire molecular output of biological systems that is suitable for analysis with tandem mass spectrometry. Here we review the current state of spectral networks algorithms and discuss possible future directions for automated interpretation of spectra from any class of molecules.