Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data

MOTIVATION: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. RESULTS: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.     

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.     

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.     

Spectral networks: a new approach to de novo discovery of protein sequences and posttranslational modifications

Significant technological advances have accelerated high-throughput proteomics to the automated generation of millions of tandem mass spectra on a daily basis. In such a setup, the desire for greater sequence coverage combines with standard experimental procedures to commonly yield multiple tandem mass spectra from overlapping peptides-typical observations include peptides differing by one or two terminal amino acids and spectra from modified and unmodified variants of the same peptides. In a departure from the traditional spectrum identification algorithms that analyze each tandem mass spectrum in isolation, spectral networks define a new computational approach that instead finds and simultaneously interprets sets of spectra from overlapping peptides. In shotgun protein sequencing, spectral networks capitalize on the redundant sequence information in the aligned spectra to deliver the longest and most accurate de novo sequences ever reported for ion trap data. Also, by combining spectra from multiple modified and unmodified variants of the same peptides, spectral networks are able to bypass the dominant guess/confirm approach to the identification of posttranslational modifications and alternatively discover modifications and highly modified peptides directly from experimental data. Open-source implementations of these algorithms may be downloaded from peptide.ucsd.edu.     

Two-dimensional mass spectra generated from the analysis of 15N-labeled and unlabeled peptides for efficient protein identification and de novo peptide sequencing

Protein identification has been greatly facilitated by database searches against protein sequences derived from product ion spectra of peptides. This approach is primarily based on the use of fragment ion mass information contained in a MS/MS spectrum. Unambiguous protein identification from a spectrum with low sequence coverage or poor spectral quality can be a major challenge. We present a two-dimensional (2D) mass spectrometric method in which the numbers of nitrogen atoms in the molecular ion and the fragment ions are used to provide additional discriminating power for much improved protein identification and de novo peptide sequencing. The nitrogen number is determined by analyzing the mass difference of corresponding peak pairs in overlaid spectra of (15)N-labeled and unlabeled peptides. These peptides are produced by enzymatic or chemical cleavage of proteins from cells grown in (15)N-enriched and normal media, respectively. It is demonstrated that, using 2D information, i.e., m/z and its associated nitrogen number, this method can, not only confirm protein identification results generated by MS/MS database searching, but also identify peptides that are not possible to identify by database searching alone. Examples are presented of analyzing Escherichia coli K12 extracts that yielded relatively poor MS/MS spectra, presumably from the digests of low abundance proteins, which can still give positive protein identification using this method. Additionally, this 2D MS method can facilitate spectral interpretation for de novo peptide sequencing and identification of posttranslational or other chemical modifications. We envision that this method should be particularly useful for proteome expression profiling of organelles or cells that can be grown in (15)N-enriched media.     

Complexities and algorithms for glycan sequencing using tandem mass spectrometry

Determining glycan structures is vital to comprehend cell-matrix, cell-cell, and even intracellular biological events. Glycan sequencing, which determines the primary structure of a glycan using tandem mass spectrometry (MS/MS), remains one of the most important tasks in proteomics. Analogous to peptide de novo sequencing, glycan de novo sequencing determines the structure without the aid of a known glycan database. We show in this paper that glycan de novo sequencing is NP-hard. We then provide a heuristic algorithm and develop a software program to solve the problem in practical cases. Experiments on real MS/MS data of glycopeptides demonstrate that our heuristic algorithm gives satisfactory results on practical data.     

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.     

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.     

Constrained de novo sequencing of conotoxins

De novo peptide sequencing by mass spectrometry (MS) can determine the amino acid sequence of an unknown peptide without reference to a protein database. MS-based de novo sequencing assumes special importance in focused studies of families of biologically active peptides and proteins, such as hormones, toxins, and antibodies, for which amino acid sequences may be difficult to obtain through genomic methods. These protein families often exhibit sequence homology or characteristic amino acid content; yet, current de novo sequencing approaches do not take advantage of this prior knowledge and, hence, search an unnecessarily large space of possible sequences. Here, we describe an algorithm for de novo sequencing that incorporates sequence constraints into the core graph algorithm and thereby reduces the search space by many orders of magnitude. We demonstrate our algorithm in a study of cysteine-rich toxins from two cone snail species (Conus textile and Conus stercusmuscarum) and report 13 de novo and about 60 total toxins.     

De novo peptide sequencing using CID and HCD spectra pairs

In tandem mass spectrometry (MS/MS), there are several different fragmentation techniques possible, including, collision‐induced dissociation (CID) higher energy collisional dissociation (HCD), electron‐capture dissociation (ECD), and electron transfer dissociation (ETD). When using pairs of spectra for de novo peptide sequencing, the most popular methods are designed for CID (or HCD) and ECD (or ETD) spectra because of the complementarity between them. Less attention has been paid to the use of CID and HCD spectra pairs. In this study, a new de novo peptide sequencing method is proposed for these spectra pairs. This method includes a CID and HCD spectra merging criterion and a parent mass correction step, along with improvements to our previously proposed algorithm for sequencing merged spectra. Three pairs of spectral datasets were used to investigate and compare the performance of the proposed method with other existing methods designed for single spectrum (HCD or CID) sequencing. Experimental results showed that full‐length peptide sequencing accuracy was increased significantly by using spectra pairs in the proposed method, with the highest accuracy reaching 81.31%.     

基于数据非依赖采集的蛋白质组质谱数据解析方法研究进展

数据非依赖采集（DIA）是蛋白质组学领域近年来快速发展的质谱采集技术，其通过无偏碎裂隔离窗口内的所有母离子采集二级谱图，理论上可实现蛋白质样品的深度覆盖，同时具有高通量、高重现性和高灵敏度的优点。现有的DIA数据采集方法可以分为全窗口碎裂方法、隔离窗口序列碎裂方法和四维DIA数据采集方法（4D-DIA）3大类。针对DIA数据的不同特点，主要数据解析方法包括谱库搜索方法、蛋白质序列库直接搜索方法、伪二级谱图鉴定方法和从头测序方法4大类。解析得到的肽段鉴定结果需要进行可信度评估，包括使用机器学习方法的重排序和对报告结果集合的假发现率估计两个步骤，实现对数据解析结果的质控。本文对DIA数据的采集方法、数据解析方法及软件和鉴定结果可信度评估方法进行了整理和综述，并展望了未来的发展方向。     

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.     

Improved de novo peptide sequencing using LC retention time information

Background

Liquid chromatography combined with tandem mass spectrometry is an important tool in proteomics for peptide identification. Liquid chromatography temporally separates the peptides in a sample. The peptides that elute one after another are analyzed via tandem mass spectrometry by measuring the mass-to-charge ratio of a peptide and its fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of a peptide from this measurement data. Past de novo sequencing algorithms solely consider the mass spectrum of the fragments for reconstructing a sequence.

Results

We propose to additionally exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the chromatographic retention time. We consider three models for predicting the retention time and develop algorithms for de novo sequencing for each model.

Conclusions

Based on an evaluation for two prediction models on experimental data from synthesized peptides we conclude that the identification rates are improved by exploiting the chromatographic information. In our evaluation, we compare our algorithms using the retention time information with algorithms using the same scoring model, but not the retention time.

     

电喷雾串联质谱图的叠合与多肽序列分析

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

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/.     

Popitam: towards new heuristic strategies to improve protein identification from tandem mass spectrometry data

In recent years, proteomics research has gained importance due to increasingly powerful techniques in protein purification, mass spectrometry and identification, and due to the development of extensive protein and DNA databases from various organisms. Nevertheless, current identification methods from spectrometric data have difficulties in handling modifications or mutations in the source peptide. Moreover, they have low performance when run on large databases (such as genomic databases), or with low quality data, for example due to bad calibration or low fragmentation of the source peptide. We present a new algorithm dedicated to automated protein identification from tandem mass spectrometry (MS/MS) data by searching a peptide sequence database. Our identification approach shows promising properties for solving the specific difficulties enumerated above. It consists of matching theoretical peptide sequences issued from a database with a structured representation of the source MS/MS spectrum. The representation is similar to the spectrum graphs commonly used by de novo sequencing software. The identification process involves the parsing of the graph in order to emphasize relevant sections for each theoretical sequence, and leads to a list of peptides ranked by a correlation score. The parsing of the graph, which can be a highly combinatorial task, is performed by a bio-inspired algorithm called Ant Colony Optimization algorithm.     

De novo mass spectrometry sequencing and characterization of species-specific peptides from nucleoside diphosphate kinase B for the classification of commercial fish species belonging to the family Merlucciidae

The characterization by de novo peptide sequencing of the different protein nucleoside diphosphate kinase B (NDK B) from all the commercial hakes and grenadiers belonging to the family Merlucciidae is reported. A classical proteomics approach, consisting of two-dimmensional gel electrophoresis, tryptic in-gel digestion of the excised spots, MALDI-TOF MS, LC-MS/MS, and nanoESI-MS/MS analyses, was followed for the purification and characterization of the different isoforms of the NDK B. Fragmentation spectra were used for de novo peptide sequence. A high degree of homology was found between the sequences of all the species studied and the NDK B sequence from Gillichthys mirabilis, which is accessible in the protein databases. Particular attention was paid to the differential characterization of species-specific peptides that could be used for fish authentication purposes. These findings allowed us to propose a rapid and effective classification method, based in the detection of these biomarker peptides using the selective ion reaction monitoring (SIRM) scan mode in mass spectrometry.     

Sequence similarity-driven proteomics in organisms with unknown genomes by LC-MS/MS and automated de novo sequencing

LC-MS/MS analysis on a linear ion trap LTQ mass spectrometer, combined with data processing, stringent, and sequence-similarity database searching tools, was employed in a layered manner to identify proteins in organisms with unsequenced genomes. Highly specific stringent searches (MASCOT) were applied as a first layer screen to identify either known (i.e. present in a database) proteins, or unknown proteins sharing identical peptides with related database sequences. Once the confidently matched spectra were removed, the remainder was filtered against a nonannotated library of background spectra that cleaned up the dataset from spectra of common protein and chemical contaminants. The rectified spectral dataset was further subjected to rapid batch de novo interpretation by PepNovo software, followed by the MS BLAST sequence-similarity search that used multiple redundant and partially accurate candidate peptide sequences. Importantly, a single dataset was acquired at the uncompromised sensitivity with no need of manual selection of MS/MS spectra for subsequent de novo interpretation. This approach enabled a completely automated identification of novel proteins that were, otherwise, missed by conventional database searches.     

利用磺基异硫氰酸苯酯化学辅助方法鉴定磷酸化肽修饰位点

利用MALDI-OF质谱结合磺基异硫氰酸苯酯(SPITC)化学辅助的从头测序（de novo sequencing）方法,将用固相金属离子亲和色谱(immobilized metal affinity chromatography,ＩＭＡＣ) 选择性地从混合物中亲和提取的磷酸肽进行磷酸化位点测定,该方法只有肽键断裂产生的带C端的碎片离子系列（y^＋ 离子系列）出现在质谱图中,图谱背景清晰,信噪比高,单纯的y^＋ 片段离子系列使得谱图解析变得非常容易,对于多磷酸化肽的磷酸化位点,不需借助于任何软件,只需简单地计算两峰之间的分子量之差即可确定.     

Sulfonation chemistry as a powerful tool for MALDI TOF/TOF de novo sequencing and post-translational modification analysis.

Mass spectrometry using matrix-assisted laser desorption/ionization (MALDI) is a widespread technique for various types of proteomic analysis. In the identification of proteins using peptide mass fingerprinting, samples are enzymatically digested and resolved into a number of peptides, whose masses are determined and matched with a sequence data-base. However, the presence inside the cell of several splicing variants, protein isoforms, or fusion proteins gives rise to a complex picture, demanding more complete analysis. Moreover, the study of species with yet uncharacterized genomes or the investigation of post-translational modifications are not possible with classical mass fingerprinting, and require specific and accurate de novo sequencing. In the last several years, much effort has been made to improve the performance of peptide sequencing with MALDI. Here we present applications using a fast and robust chemical modification of peptides for improved de novo sequencing. Post-source decay of derivatized peptides generates at the same time peaks with high intensity and simple spectra, leading to a very easy and clear sequence determination.