ADEPTS: advanced peptide de novo sequencing with a pair of tandem mass spectra

De novo sequencing is an important task in proteomics to identify novel peptide sequences. Traditionally, only one MS/MS spectrum is used for the sequencing of a peptide; however, the use of multiple spectra of the same peptide with different types of fragmentation has the potential to significantly increase the accuracy and practicality of de novo sequencing. Research into the use of multiple spectra is in a nascent stage. We propose a general framework to combine the two different types of MS/MS data. Experiments demonstrate that our method significantly improves the de novo sequencing of existing software.     

Algorithms for the de novo sequencing of peptides from tandem mass spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.     

Algorithms for the de novo sequencing of peptides from tandem mass spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.     

Searching sequence databases via de novo peptide sequencing by tandem mass spectrometry

There are many computer programs that can match tandem mass spectra of peptides to database-derived sequences; however, situations can arise where mass spectral data cannot be correlated with any database sequence. In such cases, sequences can be automatically deduced de novo, without recourse to sequence databases, and the resulting peptide sequences can be used to perform homologous nonexact searches of sequence databases. This article describes details on how to implement both a de novo sequencing program called “Lutefisk,” and a version of FASTA that has been modified to account for sequence ambiguities inherent in tandem mass spectrometry data.     

PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification

Many software tools have been developed for the automated identification of peptides from tandem mass spectra. The accuracy and sensitivity of the identification software via database search are critical for successful proteomics experiments. A new database search tool, PEAKS DB, has been developed by incorporating the de novo sequencing results into the database search. PEAKS DB achieves significantly improved accuracy and sensitivity over two other commonly used software packages. Additionally, a new result validation method, decoy fusion, has been introduced to solve the issue of overconfidence that exists in the conventional target decoy method for certain types of peptide identification software.     

A suboptimal algorithm for de novo peptide sequencing via tandem mass spectrometry.

Tandem mass spectrometry has emerged to be one of the most powerful high-throughput techniques for protein identification. Tandem mass spectrometry selects and fragments peptides of interest into N-terminal ions and C-terminal ions, and it measures the mass/charge ratios of these ions. The de novo peptide sequencing problem is to derive the peptide sequences from given tandem mass spectral data of k ion peaks without searching against protein databases. By transforming the spectral data into a matrix spectrum graph G = (V, E), where |V| = O(k(2)) and |E| = O(k(3)), we give the first polynomial time suboptimal algorithm that finds all the suboptimal solutions (peptides) in O(p|E|) time, where p is the number of solutions. The algorithm has been implemented and tested on experimental data. The program is available at http://hto-c.usc.edu:8000/msms/menu/denovo.htm.     

De novo peptide sequencing via tandem mass spectrometry.

Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics for identifying proteins. Because complete genome sequences are accumulating rapidly, the recent trend in interpretation of MS/MS spectra has been database search. However, de novo MS/MS spectral interpretation remains an open problem typically involving manual interpretation by expert mass spectrometrists. We have developed a new algorithm, SHERENGA, for de novo interpretation that automatically learns fragment ion types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The test data are used to construct optimal path scoring in the graph representations of MS/MS spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins and for validating the results of database search algorithms in fully automated, high-throughput peptide sequencing.     

A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry.

Tandem mass spectrometry fragments a large number of molecules of the same peptide sequence into charged molecules of prefix and suffix peptide subsequences and then measures mass/charge ratios of these ions. The de novo peptide sequencing problem is to reconstruct the peptide sequence from a given tandem mass spectral data of k ions. By implicitly transforming the spectral data into an NC-spectrum graph G (V, E) where /V/ = 2k + 2, we can solve this problem in O(/V//E/) time and O(/V/2) space using dynamic programming. For an ideal noise-free spectrum with only b- and y-ions, we improve the algorithm to O(/V/ + /E/) time and O(/V/) space. Our approach can be further used to discover a modified amino acid in O(/V//E/) time. The algorithms have been implemented and tested on experimental data.     

Gapped spectral dictionaries and their applications for database searches of tandem mass spectra

Generating all plausible de novo interpretations of a peptide tandem mass (MS/MS) spectrum (Spectral Dictionary) and quickly matching them against the database represent a recently emerged alternative approach to peptide identification. However, the sizes of the Spectral Dictionaries quickly grow with the peptide length making their generation impractical for long peptides. We introduce Gapped Spectral Dictionaries (all plausible de novo interpretations with gaps) that can be easily generated for any peptide length thus addressing the limitation of the Spectral Dictionary approach. We show that Gapped Spectral Dictionaries are small thus opening a possibility of using them to speed-up MS/MS searches. Our MS-Gapped-Dictionary algorithm (based on Gapped Spectral Dictionaries) enables proteogenomics applications (such as searches in the six-frame translation of the human genome) that are prohibitively time consuming with existing approaches. MS-Gapped-Dictionary generates gapped peptides that occupy a niche between accurate but short peptide sequence tags and long but inaccurate full length peptide reconstructions. We show that, contrary to conventional wisdom, some high-quality spectra do not have good peptide sequence tags and introduce gapped tags that have advantages over the conventional peptide sequence tags in MS/MS database searches.     

Peptide identification by database search of mixture tandem mass spectra

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.     

Validation of endogenous peptide identifications using a database of tandem mass spectra

The SwePep database is designed for endogenous peptides and mass spectrometry. It contains information about the peptides such as mass, pl, precursor protein and potential post-translational modifications. Here, we have improved and extended the SwePep database with tandem mass spectra, by adding a locally curated version of the global proteome machine database (GPMDB). In peptidomic experiment practice, many peptide sequences contain multiple tandem mass spectra with different quality. The new tandem mass spectra database in SwePep enables validation of low quality spectra using high quality tandem mass spectra. The validation is performed by comparing the fragmentation patterns of the two spectra using algorithms for calculating the correlation coefficient between the spectra. The present study is the first step in developing a tandem spectrum database for endogenous peptides that can be used for spectrum-to-spectrum identifications instead of peptide identifications using traditional protein sequence database searches.     

Identification of phosphopeptides with unknown cleavage specificity by a de novo sequencing assisted database search strategy

In theory, proteases with broad cleavage specificity could be applied to digest protein samples to improve the phosphoproteomic analysis coverage. However, in practice this approach is seldom employed. This is because the identification of phosphopeptides without enzyme specificity by conventional database search strategy is extremely difficult due to the huge search space. In this study, we investigated the performance of a de novo sequencing assisted database search strategy for the identification of such phosphopeptides. Firstly, we compared the performance of conventional database search strategy and the de novo sequencing assisted database search strategy for the identification of peptides and phosphopeptides without stetting enzyme specificity. It was found that the identification sensitivity dropped significantly for the conventional one while it was only slightly decreased for the new approach. Then, this new search strategy was applied to identify phosphopeptides generated by Proteinase K digestion, which resulted in the identification of 717 phosphopeptides. Finally, this strategy was utilized for the identification of serum endogenous phosphopeptides, which were generated in vivo by different kinds of proteases and kinases, and the identification of 68 unique serum endogenous phosphopepitdes was successfully achieved.     

De novo analysis of peptide tandem mass spectra by spectral graph partitioning.

We report on a new de novo peptide sequencing algorithm that uses spectral graph partitioning. In this approach, relationships between m/z peaks are represented by attractive and repulsive springs, and the vibrational modes of the spring system are used to infer information about the peaks (such as "likely b-ion" or "likely y-ion"). We demonstrate the effectiveness of this approach by comparison with other de novo sequencers on test sets of ion-trap and QTOF spectra, including spectra of mixtures of peptides. On all datasets, we outperform the other sequencers. Along with spectral graph theory techniques, the new de novo sequencer EigenMS incorporates another improvement of independent interest: robust statistical methods for recalibration of time-of-flight mass measurements. Robust recalibration greatly outperforms simple least-squares recalibration, achieving about three times the accuracy for one QTOF dataset.     

Multiplex de novo sequencing of peptide antibiotics

Proliferation of drug-resistant diseases raises the challenge of searching for new, more efficient antibiotics. Currently, some of the most effective antibiotics (i.e., Vancomycin and Daptomycin) are cyclic peptides produced by non-ribosomal biosynthetic pathways. The isolation and sequencing of cyclic peptide antibiotics, unlike the same activity with linear peptides, is time-consuming and error-prone. The dominant technique for sequencing cyclic peptides is nuclear magnetic resonance (NMR)-based and requires large amounts (milligrams) of purified materials that, for most compounds, are not possible to obtain. Given these facts, there is a need for new tools to sequence cyclic non-ribosomal peptides (NRPs) using picograms of material. Since nearly all cyclic NRPs are produced along with related analogs, we develop a mass spectrometry approach for sequencing all related peptides at once (in contrast to the existing approach that analyzes individual peptides). Our results suggest that instead of attempting to isolate and NMR-sequence the most abundant compound, one should acquire spectra of many related compounds and sequence all of them simultaneously using tandem mass spectrometry. We illustrate applications of this approach by sequencing new variants of cyclic peptide antibiotics from Bacillus brevis, as well as sequencing a previously unknown family of cyclic NRPs produced by marine bacteria. Supplementary Material is available online at www.liebertonline.com/cmb.     

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

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

Shotgun protein sequencing: assembly of peptide tandem mass spectra from mixtures of modified proteins

Despite significant advances in the identification of known proteins, the analysis of unknown proteins by MS/MS still remains a challenging open problem. Although Klaus Biemann recognized the potential of MS/MS for sequencing of unknown proteins in the 1980s, low throughput Edman degradation followed by cloning still remains the main method to sequence unknown proteins. The automated interpretation of MS/MS spectra has been limited by a focus on individual spectra and has not capitalized on the information contained in spectra of overlapping peptides. Indeed the powerful shotgun DNA sequencing strategies have not been extended to automated protein sequencing. We demonstrate, for the first time, the feasibility of automated shotgun protein sequencing of protein mixtures by utilizing MS/MS spectra of overlapping and possibly modified peptides generated via multiple proteases of different specificities. We validate this approach by generating highly accurate de novo reconstructions of multiple regions of various proteins in western diamondback rattlesnake venom. We further argue that shotgun protein sequencing has the potential to overcome the limitations of current protein sequencing approaches and thus catalyze the otherwise impractical applications of proteomics methodologies in studies of unknown proteins.     

Patchwork peptide sequencing: extraction of sequence information from accurate mass data of peptide tandem mass spectra recorded at high resolution

The accurate mass values of all immonium, y(1), y(2), a(2), and b(2) ions of tryptic peptides composed of the 20 standard amino acids were calculated. The differences between adjacent masses in this data set are greater than 10 mDa for more than 80% of the values. Using this mass list, the majority of low mass ions in quadrupole-time of flight tandem mass spectra of peptides from tryptic digests and from an elastase digest could be assigned. Besides the a(2)/b(2) ions, which carry residues 1-2 from the N-terminus, a variety of internal dipeptide b ions were regularly observed. In case internal proline was present, corresponding dipeptide b ions carrying proline at the N-terminal position occurred. By assigning the dipeptide b ions on the basis of their accurate mass, bidirectional or unidirectional sequence information was obtained, which is localized to the peptide N-terminus (a(2)/b(2) ions) or not localized (internal b ions). Identification of the y(1) and y(2) ions by their accurate mass provides unidirectional sequence information localized to the peptide C-terminus. It is shown that this patchwork-type sequence information extractable from accurate mass data of low-mass ions is highly efficient for protein identification.     

De novo peptide sequencing and identification with precision mass spectrometry

The recent proliferation of novel mass spectrometers such as Fourier transform, QTOF, and OrbiTrap marks a transition into the era of precision mass spectrometry, providing a 2 orders of magnitude boost to the mass resolution, as compared to low-precision ion-trap detectors. We investigate peptide de novo sequencing by precision mass spectrometry and explore some of the differences when compared to analysis of low-precision data. We demonstrate how the dramatically improved performance of de novo sequencing with precision mass spectrometry paves the way for novel approaches to peptide identification that are based on direct sequence lookups, rather than comparisons of spectra to a database. With the direct sequence lookup, it is not only possible to search a database very efficiently, but also to use the database in novel ways, such as searching for products of alternative splicing or products of fusion proteins in cancer. Our de novo sequencing software is available for download at http://peptide.ucsd.edu/.     

AUDENS: a tool for automated peptide de novo sequencing

We present AUDENS, a new platform-independent open source tool for automated de novo sequencing of peptides from MS/MS data. We implemented a dynamic programming algorithm and combined it with a flexible preprocessing module which is designed to distinguish between signal and other peaks. By applying a user-defined set of heuristics, AUDENS screens through the spectrum and assigns high relevance values to putative signal peaks. The algorithm constructs a sequence path through the MS/MS spectrum using the peak relevances to score each suggested sequence path, i.e., the corresponding amino acid sequence. At present, we consider AUDENS a prototype that unfolds its biggest potential if used in parallel with other de novo sequencing tools. AUDENS is available open source and can be downloaded with further documentation at http://www.ti.inf.ethz.ch/pw/software/audens/ .