MSNovo: A Dynamic Programming Algorithm for de Novo Peptide Sequencing via Tandem Mass Spectrometry

Making MS Omics Data ML-Ready: SpeCollate Protocols

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

Machine learning-assisted structure annotation of natural products based on MS and NMR data

Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.

Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review

Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.

The Current State-of-the-Art Identification of Unknown Proteins Using Mass Spectrometry Exemplified on De Novo Sequencing of a Venom Protease from Bothrops moojeni

(1) Background: The amino acid sequence elucidation of peptides from the gas phase fragmentation mass spectra, de novo sequencing, is a valuable method for the identification of unknown proteins complementary to Edman sequencing. It is increasingly used in shot-gun mass spectrometry (MS)-based proteomics experiments. We review the current state-of-the-art and use the identification of an unknown snake venom protein targeting the human tissue factor (TF) as an example to describe the analysis process based on manual spectrum interrogation. (2) Methods: The immobilized TF was incubated with a crude B. moojeni venom solution. The potential binding partners were eluted and further purified by gel electrophoresis. Edman degradation was performed to elucidate the N-terminus of the 31 kDa protein of interest. High-resolution MS with collision-induced dissociation was employed to generate peptide fragmentation spectra. Sequence tags were deduced and used for searches in the NCBI and Uniprot databases. Protein matches from the snake species were further validated by target MS/MS. (3) Results: Sequence tag D [K/Q] D [I/L] VDD [K/Q] led to a snake venom serine protease (SVSP) from lancehead B. jararaca (P81824). With target MS/MS, 24% of the SVSP sequence were confirmed; an additional 41% were tentatively assigned by data-independent MS. Edman sequencing provided information for 10 N-terminal amino acid residues, also confirming the match to SVSP. (4) Conclusions: The identification of unknown proteins continues to be a challenge despite major advances in MS instrumentation and bioinformatic tools. The main requirement is the generation of meaningful, high-quality MS peptide fragmentation spectra. These are used to elucidate sufficiently long sequence tags, which can subsequently be submitted to searches in protein databases. This basic method does not require extensive bioinformatics because peptide MS/MS spectra, especially of doubly-charged ions, can be analysed manually. We demonstrated the procedure with the elucidation of SVSP. While de novo sequencing quickly indicates the correct protein group, the validation of the entire protein sequence of amino acid-by-amino acid will take time. Reasons are the need to properly assign isobaric amino acid residues and modifications. With the ongoing efforts in genomics and transcriptomics and the availability of ever more data in public databases, the need for de novo MS sequencing will decrease. Still, not every animal and plant species will be sequenced, so the combination of MS and Edman sequencing will continue to be of importance for the identification of unknown proteins.

The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms

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Most correct de novo peptides have ⩽1 missing fragmentation cleavages.

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DeepNovo outperforms Novor for peptide accuracy for both data types.

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Novor excels at amino acid recall when many fragmentation cleavages are missing.

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Deep learning allows DeepNovo to predict amino acids without adjacent peaks.

Proteomics aims to characterise system-wide protein expression and typically relies on mass-spectrometry and peptide fragmentation, followed by a database search for protein identification. It has wide ranging applications from clinical to environmental settings and virtually impacts on every area of biology. In that context, de novo peptide sequencing is becoming increasingly popular. Historically its performance lagged behind database search methods but with the integration of machine learning, this field of research is gaining momentum. To enable de novo peptide sequencing to realise its full potential, it is critical to explore the mass spectrometry data underpinning peptide identification. In this research we investigate the characteristics of tandem mass spectra using 8 published datasets. We then evaluate two state of the art de novo peptide sequencing algorithms, Novor and DeepNovo, with a particular focus on their performance with regard to missing fragmentation cleavage sites and noise. DeepNovo was found to perform better than Novor overall. However, Novor recalled more correct amino acids when 6 or more cleavage sites were missing. Furthermore, less than 11% of each algorithms’ correct peptide predictions emanate from data with more than one missing cleavage site, highlighting the issues missing cleavages pose. We further investigate how the algorithms manage to correctly identify peptides with many of these missing fragmentation cleavages. We show how noise negatively impacts the performance of both algorithms, when high intensity peaks are considered. Finally, we provide recommendations regarding further algorithms’ improvements and offer potential avenues to overcome current inherent data limitations.

SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions

Historically, the database search algorithms have been the de facto standard for inferring peptides from mass spectrometry (MS) data. Database search algorithms deduce peptides by transforming theoretical peptides into theoretical spectra and matching them to the experimental spectra. Heuristic similarity-scoring functions are used to match an experimental spectrum to a theoretical spectrum. However, the heuristic nature of the scoring functions and the simple transformation of the peptides into theoretical spectra, along with noisy mass spectra for the less abundant peptides, can introduce a cascade of inaccuracies. In this paper, we design and implement a Deep Cross-Modal Similarity Network called SpeCollate, which overcomes these inaccuracies by learning the similarity function between experimental spectra and peptides directly from the labeled MS data. SpeCollate transforms spectra and peptides into a shared Euclidean subspace by learning fixed size embeddings for both. Our proposed deep-learning network trains on sextuplets of positive and negative examples coupled with our custom-designed SNAP-loss function. Online hardest negative mining is used to select the appropriate negative examples for optimal training performance. We use 4.8 million sextuplets obtained from the NIST and MassIVE peptide libraries to train the network and demonstrate that for closed search, SpeCollate is able to perform better than Crux and MSFragger in terms of the number of peptide-spectrum matches (PSMs) and unique peptides identified under 1% FDR for real-world data. SpeCollate also identifies a large number of peptides not reported by either Crux or MSFragger. To the best of our knowledge, our proposed SpeCollate is the first deep-learning network that can determine the cross-modal similarity between peptides and mass-spectra for MS-based proteomics. We believe SpeCollate is significant progress towards developing machine-learning solutions for MS-based omics data analysis. SpeCollate is available at https://deepspecs.github.io/.

ChimST: An Efficient Spectral Library Search Tool for Peptide Identification from Chimeric Spectra in Data-Dependent Acquisition

Accurate and sensitive identification of peptides from MS/MS spectra is a very challenging problem in computational shotgun proteomics. To tackle this problem, spectral library search has been one of the competitive solutions. However, most existing library search tools were developed on the basis of one peptide per spectrum, which prevents them from working properly on chimeric spectra where two or more peptides are co-fragmented. In this work, we present a new library search tool called ChimST, which is particularly capable of reliably identifying multiple peptides from a chimeric spectrum. It starts with associating each query MS/MS spectrum with MS precursor features. For each precursor feature, there is a list of peptide candidates extracted from an input spectral library. Then, it takes one peptide candidate from each associated feature and scores how well they could collectively interpret the query spectrum. The highest-scoring set of peptide candidates are finally reported as the identification of the query spectrum. Our experimental tests show that ChimST could significantly outperform the three state-of-the-art library search tools, SpectraST, reSpect, and MSPLIT, in terms of the numbers of both peptide-spectrum matches and unique peptides, especially when the acquisition isolation window is broad.

Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

De novo sequencing of proteins by mass spectrometry

INTRODUCTION

Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed.

AREAS COVERED

De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data.

EXPERT OPINION

As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.

Lys-Sequencer: An algorithm for de novo sequencing of peptides by paired single residue transposed Lys-C and Lys-N digestion coupled with high-resolution mass spectrometry

RATIONALE

Database-dependent identification of proteins by mass spectrometry is well established, but has limitations when there are novel proteins, mutations, splice variants, and post-translational modifications (PTMs) not available in the established reference database. De novo sequencing as a database-independent approach could address these limitations by deducing peptide sequences directly from experimental tandem mass spectrometry spectra, while concomitantly yielding residue-by-residue confidence metrics.

METHODS

Equal amounts of bovine serum albumin (BSA) sample aliquots were digested separately with Lys-C and Lys-N complementary peptidases, separated by reversed-phase ultra-high-performance liquid chromatography (UPLC), and analyzed by collision-induced dissociation (CID)-based mass spectrometry on an Orbitrap mass spectrometer. In the Lys-Sequencer algorithm, matched tandem mass spectra with equal precursor ion mass from complementary digestions were paired, and fragment ion types were identified based on the unique mass relationship between fragment ions extracted from a spectrum pair followed by de novo sequencing of peptides with identification confidence assigned at the residue level.

RESULTS

In all the matched spectrum pairs, 34 top-ranked BSA peptides were identified, from which 391 amino acid residues were identified correctly, covering ~67% of the full sequence of BSA (583 residues) with only ~6% (35 residues) exhibiting ambiguity in the sequence order (although amino acid compositions were still correctly assigned). Of note, this approach identified peptide sequences up to 17 amino acids in length without ambiguity, with the exception of the N-terminal or C-terminal peptides containing lysine (18-mer).

CONCLUSIONS

The algorithm ("Lys-Sequencer") developed in this work achieves high precision for de novo sequencing of peptides. This method facilitates the identification of point mutation and new PTMs in the protein characterization and discovery of new peptides and proteins with varying levels of confidence.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.

Visualizing and comparing results of different peptide identification methods

In mass spectrometry-based proteomics, peptides are typically identified from tandem mass spectra using spectrum comparison. A sequence search engine compares experimentally obtained spectra with those predicted from protein sequences, applying enzyme cleavage and fragmentation rules. To this, there are two main alternatives: spectral libraries and de novo sequencing. The former compares measured spectra with a collection of previously acquired and identified spectra in a library. De novo attempts to sequence peptides from the tandem mass spectra alone. We here present a theoretical framework and a data processing workflow for visualizing and comparing the results of these different types of algorithms. The method considers the three search strategies as different dimensions, identifies distinct agreement classes and visualizes the complementarity of the search strategies. We have included X! Tandem, SpectraST and PepNovo, as they are in common use and representative for algorithms of each type. Our method allows advanced investigation of how the three search methods perform relatively to each other and shows the impact of the currently used decoy sequences for evaluating the false discovery rates.

De novo peptide sequencing by deep learning

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.

Algorithms and design strategies towards automated glycoproteomics analysis

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

A better scoring model for de novo peptide sequencing: the symmetric difference between explained and measured masses

BACKGROUND

Given a peptide as a string of amino acids, the masses of all its prefixes and suffixes can be found by a trivial linear scan through the amino acid masses. The inverse problem is the idealde novopeptide sequencing problem: Given all prefix and suffix masses, determine the string of amino acids. In biological reality, the given masses are measured in a lab experiment, and measurements by necessity are noisy. The (real, noisy) de novo peptide sequencing problem therefore has a noisy input: a few of the prefix and suffix masses of the peptide are missing and a few other masses are given in addition. For this setting, we ask for an amino acid string that explains the given masses as accurately as possible.

RESULTS

Past approaches interpreted accuracy by searching for a string that explains as many masses as possible. We feel, however, that it is not only bad to not explain a mass that appears, but also to explain a mass that does not appear. We propose to minimize the symmetric difference between the set of given masses and the set of masses that the string explains. For this new optimization problem, we propose an efficient algorithm that computes both the best and the k best solutions. Proof-of-concept experiments on measurements of synthesized peptides show that our approach leads to better results compared to finding a string that explains as many given masses as possible.

CONCLUSIONS

We conclude that considering the symmetric difference as optimization goal can improve the identification rates for de novo peptide sequencing. A preliminary version of this work has been presented at WABI 2016.

Tandem Mass Spectrum Sequencing: An Alternative to Database Search Engines in Shotgun Proteomics

Protein identification via database searches has become the gold standard in mass spectrometry based shotgun proteomics. However, as the quality of tandem mass spectra improves, direct mass spectrum sequencing gains interest as a database-independent alternative. In this chapter, the general principle of this so-called de novo sequencing is introduced along with pitfalls and challenges of the technique. The main tools available are presented with a focus on user friendly open source software which can be directly applied in everyday proteomic workflows.

De novo Peptide Sequencing

De novo peptide sequencing refers to the process of determining a peptide’s amino acid sequence from its MS/MS spectrum alone. The principle of this process is fairly straightforward: a high-quality spectrum may present a ladder of fragment ion peaks. The mass difference between every two adjacent peaks in the ladder is used to determine a residue of the peptide. However, most practical spectra do not have sufficient quality to support this straightforward process. Therefore, research in de novo sequencing has largely been a battle against the errors in the data. This chapter reviews some of the major developments in this field. The chapter starts with a quick review of the history in Section 1. Then manual de novo sequencing is examined in Section 2. Section 3 introduces a few commonly used de novo sequencing algorithms. An important aspect of automated de novo sequencing software is a good scoring function that serves as the optimization goal of the algorithm. Thus, Section 4 is devoted for the methods to define good scoring functions. Section 5 reviews a list of relevant software. The chapter concludes with a discussion of the applications and limitations of de novosequencing in Section 6.

Advances in proteomic study of cardiac amyloidosis: progress and potential

INTRODUCTION

More than ten distinct forms of amyloidoses that can involve the heart have been described, classified according to which protein originates the deposits. Cardiac amyloid infiltration translates into progressive and often life-threatening cardiomyopathy, but disease severity, prognosis and treatment drastically differ according to the amyloidosis type. The notion that protein misfolding and aggregation play a more general role in human cardiomyopathies has further raised attention towards the definition of the proteotoxicity mechanisms. Areas covered: Mass spectrometry-based proteomics plays an important role as a diagnostic tool and for understanding the molecular bases of amyloid cardiomyopathies. The landscape of applications of proteomics to the study of cardiac amyloidoses and amyloid-related cardiotoxicity is summarized, with a critical synthesis of the major achievements. Expert commentary: Current strengths and limitations of proteomics in the clinical setting and in translational research on amyloid cardiomyopathy are discussed, with the foreseen potential future directions in the field.

UVnovo: A de Novo Sequencing Algorithm Using Single Series of Fragment Ions via Chromophore Tagging and 351 nm Ultraviolet Photodissociation Mass Spectrometry

De novo peptide sequencing by mass spectrometry represents an important strategy for characterizing novel peptides and proteins, in which a peptide's amino acid sequence is inferred directly from the precursor peptide mass and tandem mass spectrum (MS/MS or MS(3)) fragment ions, without comparison to a reference proteome. This method is ideal for organisms or samples lacking a complete or well-annotated reference sequence set. One of the major barriers to de novo spectral interpretation arises from confusion of N- and C-terminal ion series due to the symmetry between b and y ion pairs created by collisional activation methods (or c, z ions for electron-based activation methods). This is known as the "antisymmetric path problem" and leads to inverted amino acid subsequences within a de novo reconstruction. Here, we combine several key strategies for de novo peptide sequencing into a single high-throughput pipeline: high-efficiency carbamylation blocks lysine side chains, and subsequent tryptic digestion and N-terminal peptide derivatization with the ultraviolet chromophore AMCA yield peptides susceptible to 351 nm ultraviolet photodissociation (UVPD). UVPD-MS/MS of the AMCA-modified peptides then predominantly produces y ions in the MS/MS spectra, specifically addressing the antisymmetric path problem. Finally, the program UVnovo applies a random forest algorithm to automatically learn from and then interpret UVPD mass spectra, passing results to a hidden Markov model for de novo sequence prediction and scoring. We show this combined strategy provides high-performance de novo peptide sequencing, enabling the de novo sequencing of thousands of peptides from an Escherichia coli lysate at high confidence.

De Novo Sequencing Assisted Approach for Characterizing Mixture MS/MS Spectra

Extensive research has been conducted for the computational analysis of mass spectrometry based proteomics data. However, there are still remaining challenges, among which, one particular challenge is the low identification rate of the collected spectral data. A specific contributing factor is the existence of mixture spectra in the collected MS/MS spectra which are generated by the concurrent fragmentation of multiple precursors in one sequencing attempt. The quite frequently observed mixture spectra necessitates the development of effective computational approaches to characterize those non-conventional spectral data. In this research, we proposed an approach for matching the query mixture spectra with a pair of peptide sequences acquired from the protein database by incorporating a special de novo assisted filtration strategy. The experiment results on two different datasets of MS/MS spectra containing mixed ion fragments from multiple peptides demonstrated the efficiency of the integrated filtration strategy in reducing examination space and verified the effectiveness of the proposed matching scheme as well.

Novor: real-time peptide de novo sequencing software

De novo sequencing software has been widely used in proteomics to sequence new peptides from tandem mass spectrometry data. This study presents a new software tool, Novor, to greatly improve both the speed and accuracy of today's peptide de novo sequencing analyses. To improve the accuracy, Novor's scoring functions are based on two large decision trees built from a peptide spectral library with more than 300,000 spectra with machine learning. Important knowledge about peptide fragmentation is extracted automatically from the library and incorporated into the scoring functions. The decision tree model also enables efficient score calculation and contributes to the speed improvement. To further improve the speed, a two-stage algorithmic approach, namely dynamic programming and refinement, is used. The software program was also carefully optimized. On the testing datasets, Novor sequenced 7%-37% more correct residues than the state-of-the-art de novo sequencing tool, PEAKS, while being an order of magnitude faster. Novor can de novo sequence more than 300 MS/MS spectra per second on a laptop computer. The speed surpasses the acquisition speed of today's mass spectrometer and, therefore, opens a new possibility to de novo sequence in real time while the spectrometer is acquiring the spectral data. Graphical Abstract ᅟ.

Mass spectrometric de novo sequencing of natural non-tryptic peptides: comparing peculiarities of collision-induced dissociation (CID) and high energy collision dissociation (HCD)

RATIONALE

Mass spectrometry has shown itself as the most efficient tool for the sequencing of peptides. However, de novo sequencing of novel natural peptides is significantly more challenging in comparison with the same procedure applied for the tryptic peptides. To reach the goal in this case it is essential to select the most useful methods of triggering fragmentation and combine complementary techniques.

METHODS

Comparison of low-energy collision-induced dissociation (CID) and higher energy collision-induced dissociation (HCD) modes for sequencing of the natural non-tryptic peptides with disulfide bonds and/or several proline residues in the backbone was achieved using an LTQ FT Ultra Fourier transform ion cyclotron resonance (FTICR) mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) equipped with a 7 T magnet and an LTQ Orbitrap Velos ETD (Thermo Fisher Scientific, Bremen, Germany) instrument. Peptide fractions were obtained by high-performance liquid chromatography (HPLC) separation of frog skin secretion samples from ten species of Rana temporaria, caught in the Kolomna district of Moscow region (Russia).

RESULTS

HCD makes the b/y series longer and more pronounced, thus increasing sequence coverage. Fragment ions due to cleavages at the C-termini of proline residues make the sequencing more reliable and may be used to detect missed cleavages in the case of tryptic peptides. Another HCD peculiarity involves formation of pronounced inner fragment ions (secondary y(n)b(m) ion series formed from the abundant primary y-ions). Differences in de novo sequencing of natural non-tryptic peptides with CID and HCD, involving thorough manual expert interpretation of spectra and two automatic sequencing algorithms, are discussed.

CONCLUSIONS

Although HCD provides better results, a combination of CID and HCD data may notably increase reliability of de novo sequencing. Several pairs of b2 /a2 -ions may be formed in HCD, complicating the spectra. Automatic de novo sequencing with the available programs remains less efficient than the manual one, independently of the collision energy.

Metaproteomics: extracting and mining proteome information to characterize metabolic activities in microbial communities

Contemporary microbial ecology studies usually employ one or more "omics" approaches to investigate the structure and function of microbial communities. Among these, metaproteomics aims to characterize the metabolic activities of the microbial membership, providing a direct link between the genetic potential and functional metabolism. The successful deployment of metaproteomics research depends on the integration of high-quality experimental and bioinformatic techniques for uncovering the metabolic activities of a microbial community in a way that is complementary to other "meta-omic" approaches. The essential, quality-defining informatics steps in metaproteomics investigations are: (1) construction of the metagenome, (2) functional annotation of predicted protein-coding genes, (3) protein database searching, (4) protein inference, and (5) extraction of metabolic information. In this article, we provide an overview of current bioinformatic approaches and software implementations in metaproteome studies in order to highlight the key considerations needed for successful implementation of this powerful community-biology tool.

Identification of b-/y-ions in MS/MS spectra using a two stage neural network

Independent of the approach used, the ability to correctly interpret tandem MS data depends on the quality of the original spectra. Even in the case of the highest quality spectra, the majority of spectral peaks can not be reliably interpreted. The accuracy of sequencing algorithms can be improved by filtering out such 'noise' peaks. Preprocessing MS/MS spectra to select informative ion peaks increases accuracy and reduces the processing time. Intuitively, the mix of informative versus non-informative peaks has a direct effect on the quality and size of the resulting candidate peptide search space. As the number of selected peaks increases, the corresponding search space increases exponentially. If we select too few peaks then the ion-ladder interpretation of the spectrum will contain gaps that can only be explained by permutations of combinations of amino acids. This will result in a larger candidate peptide search space and poorer quality candidates. The dependency that peptide sequencing accuracy has on an initial peak selection regime makes this preprocessing step a crucial facet of any approach, whether de novo or not, to MS/MS spectra interpretation.

We have developed a novel approach to address this problem. Our approach uses a staged neural network to model ion fragmentation patterns and estimate the posterior probability of each ion type. Our method improves upon other preprocessing techniques and shows a significant reduction in the search space for candidate peptides without sacrificing candidate peptide quality.

De novo sequencing of peptides using selective 351 nm ultraviolet photodissociation mass spectrometry

Although in silico database search methods remain more popular for shotgun proteomics methods, de novo sequencing offers the ability to identify peptides derived from proteins lacking sequenced genomes and ones with subtle splice variants or truncations. Ultraviolet photodissociation (UVPD) of peptides derivatized by selective attachment of a chromophore at the N-terminus generates a characteristic series of y ions. The UVPD spectra of the chromophore-labeled peptides are simplified and thus amenable to de novo sequencing. This method resulted in an observed sequence coverage of 79% for cytochrome C (eight peptides), 47% for β-lactoglobulin (five peptides), 25% for carbonic anhydrase (six peptides), and 51% for bovine serum albumin (33 peptides). This strategy also allowed differentiation of proteins with high sequence homology as evidenced by de novo sequencing of two variants of green fluorescent protein.

Neutron-encoded signatures enable product ion annotation from tandem mass spectra

We report the use of neutron-encoded (NeuCode) stable isotope labeling of amino acids in cell culture for the purpose of C-terminal product ion annotation. Two NeuCode labeling isotopologues of lysine, (13)C6(15)N2 and (2)H8, which differ by 36 mDa, were metabolically embedded in a sample proteome, and the resultant labeled proteins were combined, digested, and analyzed via liquid chromatography and mass spectrometry. With MS/MS scan resolving powers of ~50,000 or higher, product ions containing the C terminus (i.e. lysine) appear as a doublet spaced by exactly 36 mDa, whereas N-terminal fragments exist as a single m/z peak. Through theory and experiment, we demonstrate that over 90% of all y-type product ions have detectable doublets. We report on an algorithm that can extract these neutron signatures with high sensitivity and specificity. In other words, of 15,503 y-type product ion peaks, the y-type ion identification algorithm correctly identified 14,552 (93.2%) based on detection of the NeuCode doublet; 6.8% were misclassified (i.e. other ion types that were assigned as y-type products). Searching NeuCode labeled yeast with PepNovo(+) resulted in a 34% increase in correct de novo identifications relative to searching through MS/MS only. We use this tool to simplify spectra prior to database searching, to sort unmatched tandem mass spectra for spectral richness, for correlation of co-fragmented ions to their parent precursor, and for de novo sequence identification.

DE NOVO SEQUENCING WITH LIMITED NUMBER OF POST-TRANSLATIONAL MODIFICATIONS PER PEPTIDE

De novo sequencing derives the peptide sequence from a tandem mass spectrum without the assistance of protein databases. This analysis has been indispensable for the identification of novel or modified peptides in a biological sample. Currently, the speed of de novo sequencing algorithms is not heavily affected by the number of post-translational modification (PTM) types in consideration. However, the accuracy of the algorithms can be degraded due to the increased search space. Most peptides in a proteomics research contain only a small number of PTMs per peptide, yet the types of PTMs can come from a large number of choices. Therefore, it is desirable to include a large number of PTM types in a de novo sequencing algorithm, yet to limit the number of PTM occurrences in each peptide to increase the accuracy. In this paper, we present an efficient de novo sequencing algorithm, DeNovoPTM, for such a purpose. The implemented software is downloadable from http://www.cs.uwaterloo.ca/~l22he/denovo_ptm .

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.

Plant proteogenomics: from protein extraction to improved gene predictions

Historically many genome annotation strategies have lacked experimental evidence at the protein level, which and have instead relied heavily on ab initio gene prediction tools, which consequently resulted in many incorrectly annotated genomic sequences. Proteogenomics aims to address these issues using mass spectrometry (MS)-based proteomics, genomic mapping, and providing statistical significance measures such as false discovery rates (FDRs) to validate the mapped peptides. Presented here is a tool capable of meeting this goal, the UCSD proteogenomic pipeline, which maps peptide-spectrum matches (PSMs) to the genome using the Inspect MS/MS database search tool and assigns a statistical significance to the match using a target-decoy search approach to assign estimated FDRs. This pipeline also provides the option of using a more reliable approach to proteogenomics by determining the precise false-positive rates (FPRs) and p-values of each PSM by calculating their spectral probabilities and rescoring each PSM accordingly. In addition to the protein prediction challenges in the rapidly growing number of sequenced plant genomes, it is difficult to extract high-quality protein samples from many plant species. For that reason, this chapter contains methods for protein extraction and trypsin digestion that reliably produce samples suitable for proteogenomic analysis.

Unifying protein inference and peptide identification with feedback to update consistency between peptides

We first propose a new method to process peptide identification reports from databases search engines. Then via it we develop a method for unifying protein inference and peptide identification by adding a feedback from protein inference to peptide identification. The feedback information is a list of high-confidence proteins, which is used to update an adjacency matrix between peptides. The adjacency matrix is used in the regularization of peptide scores. Logistic regression (LR) is used to compute the probability of peptide identification with the regularized scores. Protein scores are then calculated with the LR probability of peptides. Instead of selecting the best peptide match for each MS/MS, we select multiple peptides. By testing on two datasets, the results have shown that the proposed method can robustly assign accurate probabilities to peptides, and have a higher discrimination power than PeptideProphet to distinguish correct and incorrect identified peptides. Additionally, not only can our method infer more true positive proteins but also infer less false positive proteins than ProteinProphet at the same false positive rate. The coverage of inferred proteins is also significantly increased due to the selection of multiple peptides for each MS/MS and the improvement of their scores by the feedback from the inferred proteins.

A feedback framework for protein inference with peptides identified from tandem mass spectra

BACKGROUND

Protein inference is an important computational step in proteomics. There exists a natural nest relationship between protein inference and peptide identification, but these two steps are usually performed separately in existing methods. We believe that both peptide identification and protein inference can be improved by exploring such nest relationship.

RESULTS

In this study, a feedback framework is proposed to process peptide identification reports from search engines, and an iterative method is implemented to exemplify the processing of Sequest peptide identification reports according to the framework. The iterative method is verified on two datasets with known validity of proteins and peptides, and compared with ProteinProphet and PeptideProphet. The results have shown that not only can the iterative method infer more true positive and less false positive proteins than ProteinProphet, but also identify more true positive and less false positive peptides than PeptideProphet.

CONCLUSIONS

The proposed iterative method implemented according to the feedback framework can unify and improve the results of peptide identification and protein inference.

TUTORIAL ON DE NOVO PEPTIDE SEQUENCING USING MS/MS MASS SPECTROMETRY

This paper is a self-contained introductory tutorial on the problem in proteomics known as peptide sequencing using tandem mass spectrometry. This tutorial deals specifically with de novo sequencing methods (as opposed to database search methods). We first give an introduction to peptide sequencing, its importance and history and some background on proteins. Next we show the relationship between a peptide and the final spectrum produced from a tandem mass spectrometer, together with a description of the various sources of complications that arise during the process of generating the mass spectrum. From there we model the computational problem of de novo peptide sequencing, which is basically the reverse problem of identifying the peptide which produced the spectrum. We then present several major approaches to solve it (including reviewing some of the current algorithms in each approach), and also discuss related problems and post-processing approaches.

193 nm ultraviolet photodissociation of imidazolinylated Lys-N peptides for de novo sequencing

The goal of many MS/MS de novo sequencing strategies is to generate a single product ion series that can be used to determine the precursor ion sequence. Most methods fall short of achieving such simplified spectra, and the presence of additional ion series impede peptide identification. The present study aims to solve the problem of confounding ion series by enhancing the formation of "golden" sets of a, b, and c ions for sequencing. Taking advantage of the characteristic mass differences between the golden ions allows N-terminal fragments to be readily identified while other ion series are excluded. By combining the use of Lys-N, an alternate protease, to produce peptides with lysine residues at each N-terminus with subsequent imidazolinylation of the ε-amino group of each lysine, peptides with highly basic sites localized at each N-terminus are generated. Subsequent MS/MS analysis by using 193 nm ultraviolet photodissociation (UVPD) results in enhanced formation of the diagnostic golden pairs and golden triplets that are ideal for de novo sequencing.

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.

De novo sequencing and homology searching

In proteomics, de novo sequencing is the process of deriving peptide sequences from tandem mass spectra without the assistance of a sequence database. Such analyses have traditionally been performed manually by human experts, and more recently by computer programs that have been developed because of the need for higher throughput. Although powerful, de novo sequencing often can only determine partially correct sequence tags because of imperfect tandem mass spectra. However, these sequence tags can then be searched in a sequence database to identify the exact or a homologous peptide. Homology searches are particularly useful for the study of organisms whose genomes have not been sequenced. This tutorial will present background important to understanding de novo sequencing, suggestions on how to do this manually, plus descriptions of computer algorithms used to automate this process and to subsequently carryout homology-based database searches. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 1).

Database independent proteomics analysis of the ostrich and human proteome

Mass spectrometry (MS)-based proteome analysis relies heavily on the presence of complete protein databases. Such a strategy is extremely powerful, albeit not adequate in the analysis of unpredicted postgenome events, such as posttranslational modifications, which exponentially increase the search space. Therefore, it is of interest to explore "database-free" approaches. Here, we sampled the ostrich and human proteomes with a method facilitating de novo sequencing, utilizing the protease Lys-N in combination with electron transfer dissociation. By implementing several validation steps, including the combined use of collision-induced dissociation/electron transfer dissociation data and a cross-validation with conventional database search strategies, we identified approximately 2,500 unique de novo peptide sequences from the ostrich sample with over 900 peptides generating full backbone sequence coverage. This dataset allowed the appropriate positioning of ostrich in the evolutionary tree. The described database-free sequencing approach is generically applicable and has great potential in important proteomics applications such as in the analysis of variable parts of endogenous antibodies or proteins modified by a plethora of complex posttranslational modifications.

Investigation of VUV Photodissociation Propensities Using Peptide Libraries

PSD does not usually generate a complete series of y-type ions, particularly at high mass, and this is a limitation for de novo sequencing algorithms. It is demonstrated that b(2) and b(3) ions can be used to help assign high mass x(N-2) and x(N-3) fragments that are found in vacuum ultraviolet (VUV) photofragmentation experiments. In addition, v(N)-type ion fragments with side chain loss from the N-terminal residue often enable confirmation of N-terminal amino acids. Libraries containing several thousand peptides were examined using photodissociation in a MALDI-TOF/TOF instrument. 1345 photodissociation spectra with a high S/N ratio were interpreted.

Improving proteomics mass accuracy by dynamic offline lock mass

Several methods to obtain low-ppm mass accuracy have been described. In particular, online or offline lock mass approaches can use background ions, produced by electrospray under ambient conditions, as calibrants. However, background ions such as protonated and ammoniated polydimethylcyclosiloxane ions have relatively weak and fluctuating intensity. To address this issue, we implemented dynamic offline lock mass (DOLM). Within every MS1 survey spectrum, DOLM dynamically selected the strongest n background ions for statistical treatments and m/z recalibration. We systematically optimized the mass profile abstraction method to find one single m/z value to represent an ion and the number of calibrants. To assess the influence of the intensity of the analyte ions, we used tandem mass spectroscopy (MS/MS) datasets obtained from MudPIT analyses of two protein samples with different dynamic ranges. DOLM outperformed both external mass calibration and offline lock mass that used predetermined calibrant ions, especially in the low-ppm range. The unique dynamic feature of DOLM was able to adapt to wide variations in calibrant intensities, leading to averaged mass error center at 0.03 ± 0.50 ppm for precursor ions. Such consistently tight mass accuracies meant that a precursor mass tolerance as low as 1.5 ppm could be used to search or filter post-search DOLM-recalibrated MS/MS datasets.

Proteomics in molecular diagnosis: typing of amyloidosis

Amyloidosis is a group of disorders caused by deposition of misfolded proteins as aggregates in the extracellular tissues of the body, leading to impairment of organ function. Correct identification of the causal amyloid protein is absolutely crucial for clinical management in order to avoid misdiagnosis and inappropriate, potentially harmful treatment, to assess prognosis and to offer genetic counselling if relevant. Current diagnostic methods, including antibody-based amyloid typing, have limited ability to detect the full range of amyloid forming proteins. Recent investigations into proteomic identification of amyloid protein have shown promise. This paper will review the current state of the art in proteomic analysis of amyloidosis, discuss the suitability of techniques based on the properties of amyloidosis, and further suggest potential areas of development. Establishment of mass spectrometry aided amyloid typing procedures in the pathology laboratory will allow accurate amyloidosis diagnosis in a timely manner and greatly facilitate clinical management of the disease.

ProbPS: a new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity

Background

The analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent; however, this assumption is contrary to real data and prone to error.

Results

In this paper, we present a statistical model to quantitatively measure the dependence of the derivative peak's existence on the primary peak's intensity. Here, we propose a statistical model, named ProbPS, to capture the dependence in a quantitative manner and describe a statistical model for peak selection. Our results show that the quantitative understanding can successfully guide the peak selection process. By comparing ProbPS with AuDeNS we demonstrate the advantages of our method in both filtering out noise peaks and in improving de novo identification. In addition, we present a tag identification approach based on our peak selection method. Our results, using a test data set, suggest that our tag identification method (876 correct tags in 1000 spectra) outperforms PepNovoTag (790 correct tags in 1000 spectra).

Conclusions

We have shown that ProbPS improves the accuracy of peak selection which further enhances the performance of de novo sequencing and tag identification. Thus, our model saves valuable computation time and improving the accuracy of the results.