High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.     

JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from “mixture spectra” to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based¹ proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2–6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS² (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples contain unknown protein modifications, mutations, and contaminants, the related MS/MS spectra also result in false positives, as their corresponding peptides are not in the database. Other false positives may be generated simply by random matches. Therefore, it is of importance to remove these false PSMs to improve dataset quality. One common approach is to filter putative PSMs to achieve a final list with a predefined false discovery rate (FDR) via a target-decoy strategy, in which decoy proteins are merged with target proteins in the same database for estimating false PSMs (23–26). However, the true and false PSMs are not always distinguishable based on matching scores. It is a problem to set up an appropriate score threshold to achieve maximal sensitivity and high specificity (13, 27, 28).De novo methods, including Lutefisk (29), PEAKS (30), NovoHMM (31), PepNovo (32), pNovo (33), Vonovo (34), and UniNovo (35), identify peptide sequences directly from MS/MS spectra. These methods can be used to derive novel peptides and post-translational modifications without a database, which is useful, especially when the related genome is not sequenced. High-resolution MS/MS spectra greatly facilitate the generation of peptide sequences in these de novo methods. However, because MS/MS fragmentation cannot always produce all predicted product ions, only a portion of collected MS/MS spectra have sufficient quality to extract partial or full peptide sequences, leading to lower sensitivity than achieved with the database search methods.To improve the sensitivity of the de novo methods, a hybrid approach has been proposed to integrate peptide sequence tags into PSM scoring during database searches (36). Numerous software packages have been developed, such as GutenTag (37), InsPecT (38), Byonic (39), DirecTag (40), and PEAKS DB (41). These methods use peptide tag sequences to filter a protein database, followed by error-tolerant database searching. One restriction in most of these algorithms is the requirement of a minimum tag length of three amino acids for matching protein sequences in the database. This restriction reduces the sensitivity of the database search, because it filters out some high-quality spectra in which consecutive tags cannot be generated.In this paper, we describe JUMP, a novel tag-based hybrid algorithm for peptide identification. The program is optimized to balance sensitivity and specificity during tag derivation and MS/MS pattern matching. JUMP can use all potential sequence tags, including tags consisting of only one amino acid. When we compared its performance to that of two widely used search algorithms, SEQUEST and Mascot, JUMP identified ∼30% more PSMs at the same FDR threshold. In addition, the program provides two additional features: (i) using tag sequences to improve modification site assignment, and (ii) analyzing co-fragmented peptides from mixture MS/MS spectra.     

Peptide Identification from Mixture Tandem Mass Spectra

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

Targeted Proteomic Quantification on Quadrupole-Orbitrap Mass Spectrometer

There is an immediate need for improved methods to systematically and precisely quantify large sets of peptides in complex biological samples. To date protein quantification in biological samples has been routinely performed on triple quadrupole instruments operated in selected reaction monitoring mode (SRM), and two major challenges remain. Firstly, the number of peptides to be included in one survey experiment needs to be increased to routinely reach several hundreds, and secondly, the degree of selectivity should be improved so as to reliably discriminate the targeted analytes from background interferences. High resolution and accurate mass (HR/AM) analysis on the recently developed Q-Exactive mass spectrometer can potentially address these issues. This instrument presents a unique configuration: it is constituted of an orbitrap mass analyzer equipped with a quadrupole mass filter as the front-end for precursor ion mass selection. This configuration enables new quantitative methods based on HR/AM measurements, including targeted analysis in MS mode (single ion monitoring) and in MS/MS mode (parallel reaction monitoring). The ability of the quadrupole to select a restricted m/z range allows one to overcome the dynamic range limitations associated with trapping devices, and the MS/MS mode provides an additional stage of selectivity. When applied to targeted protein quantification in urine samples and benchmarked with the reference SRM technique, the quadrupole-orbitrap instrument exhibits similar or better performance in terms of selectivity, dynamic range, and sensitivity. This high performance is further enhanced by leveraging the multiplexing capability of the instrument to design novel acquisition methods and apply them to large targeted proteomic studies for the first time, as demonstrated on 770 tryptic yeast peptides analyzed in one 60-min experiment. The increased quality of quadrupole-orbitrap data has the potential to improve existing protein quantification methods in complex samples and address the pressing demand of systems biology or biomarker evaluation studies.Shotgun proteomics has emerged over the past decade as the most effective method for the qualitative study of complex proteomes (i.e., the identification of the protein content), as illustrated by a wealth of publications (1, 2). In this approach, after enzymatic digestion of the proteins, the generated peptides are analyzed by means of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)¹ in a data dependent mode. However, the complexity of the digested proteomes under investigation and the wide range of protein abundances limit the reproducibility and the sensitivity of this stochastic approach (3), which is critical if one aims at the systematic quantification of the proteins. Thus, alternative MS approaches have emerged for the systematic quantitative study of complex proteomes, the MS-based targeted proteomics (4). In this hypothesis-driven approach, only specific subsets of analytes (a few targeted peptides used as surrogates for the proteins of interest) are selectively measured in predefined m/z ranges and retention time windows, which overcomes the bias toward most abundant compounds commonly observed with shotgun proteomics. When applied to complex biological samples—for example, bodily fluids such as urine or plasma—targeted proteomics requires high performance instruments allowing measurements of a wide dynamic range (many orders of magnitude), with high sensitivity in order to detect peptides in the low amol range and sufficient selectivity to cope with massive biochemical background (5). Selected reaction monitoring (SRM) on triple quadrupole (6) or triple quadrupole-linear ion trap mass spectrometers (7) has emerged as a means to conduct such analyses (8). Initially applied in the MS analysis of small molecules (9, 10), SRM has gradually emerged as the reference quantitative technique for analyzing proteins (or peptides) in biological samples. When coupled with the isotope dilution strategy (11, 12), this very effective technique allows the precise quantification of proteins (13–18). However, despite the increased selectivity provided by the two-stage mass filtering of SRM (at the precursor and fragment ion levels), the low resolution of mass selection does not allow the systematic removal of interferences (19, 20). Moreover, in proteomics, the biochemical background has a composition similar to that of the analytes of interest, which remains a major hurdle limiting the sensitivity of assays, especially in a bodily fluid matrix. High resolution/accurate mass (HR/AM) analysis represents a promising alternative approach that might more efficiently distinguish the compounds of interest from interferences in targeted proteomics. Such analyses can be conducted on orbitrap-based mass spectrometers because of their high sensitivity and high mass accuracy capabilities (21). The introduction of the benchtop standalone orbitrap mass spectrometer (Exactive) (22) further strengthened the attractiveness of the approach, especially in the field of small molecule analysis (23, 24). However, as quantification using trapping devices intrinsically suffers from a limited dynamic range because of the overall ion capacity, the complexity of biological samples remains very challenging even with the HR/AM approach (25). Targeted protein analysis with triple quadrupole mass spectrometers keeps on showing significant superiority for such samples.² The recently developed quadrupole-orbitrap mass spectrometer (Q-Exactive) can potentially address this issue.³ It is constituted of an orbitrap mass analyzer equipped with a quadrupole mass filter as the front-end for precursor ion mass selection (26, 27). This configuration combines advantages of triple quadrupole instruments for mass filtering and orbitrap-based mass spectrometers for HR/AM measurement. The ability of the instrument to select a restricted m/z range or (sequentially) a small number of precursor ions offers new opportunities for quantification in complex samples by selectively enriching low abundant components. The resulting data, acquired in the so-called single ion monitoring (SIM) mode, fully benefit from the trapping capability while keeping a high acquisition rate as a result of the fast switching time between targeted precursor ions of the quadrupole. Although this mode of data acquisition is possible with a configuration combining a linear ion trap with the orbitrap (as in the LTQ-Orbitrap mass spectrometer), its effectiveness is far more limited in this case. The quadrupole-orbitrap configuration presents significant benefits by selectively isolating a narrow population of precursor ions. Other features of the instrument include its multiplexed trapping capability (26) using either the C-trap or the higher energy collisional dissociation (HCD) cell (28, 29), which opens new avenues in the design of innovative acquisition methods for quantification studies. For the first time, a panel of acquisition methods is designed and applied to targeted quantification at the MS and MS/MS levels. In the latter case, the simultaneous monitoring of multiple MS/MS fragmentation channels, also called parallel reaction monitoring⁴ (PRM), is particularly promising for quantifying large sets of peptides with increased selectivity.     

Online Peptide Fractionation Using a Multiphasic Microfluidic Liquid Chromatography Chip Improves Reproducibility and Detection Limits for Quantitation in Discovery and Targeted Proteomics

Comprehensive proteomic profiling of biological specimens usually requires multidimensional chromatographic peptide fractionation prior to mass spectrometry. However, this approach can suffer from poor reproducibility because of the lack of standardization and automation of the entire workflow, thus compromising performance of quantitative proteomic investigations. To address these variables we developed an online peptide fractionation system comprising a multiphasic liquid chromatography (LC) chip that integrates reversed phase and strong cation exchange chromatography upstream of the mass spectrometer (MS). We showed superiority of this system for standardizing discovery and targeted proteomic workflows using cancer cell lysates and nondepleted human plasma. Five-step multiphase chip LC MS/MS acquisition showed clear advantages over analyses of unfractionated samples by identifying more peptides, consuming less sample and often improving the lower limits of quantitation, all in highly reproducible, automated, online configuration. We further showed that multiphase chip LC fractionation provided a facile means to detect many N- and C-terminal peptides (including acetylated N terminus) that are challenging to identify in complex tryptic peptide matrices because of less favorable ionization characteristics. Given as much as 95% of peptides were detected in only a single salt fraction from cell lysates we exploited this high reproducibility and coupled it with multiple reaction monitoring on a high-resolution MS instrument (MRM-HR). This approach increased target analyte peak area and improved lower limits of quantitation without negatively influencing variance or bias. Further, we showed a strategy to use multiphase LC chip fractionation LC-MS/MS for ion library generation to integrate with SWATH^TM data-independent acquisition quantitative workflows. All MS data are available via ProteomeXchange with identifier PXD001464.Mass spectrometry based proteomic quantitation is an essential technique used for contemporary, integrative biological studies. Whether used in discovery experiments or for targeted biomarker applications, quantitative proteomic studies require high reproducibility at many levels. It requires reproducible run-to-run peptide detection, reproducible peptide quantitation, reproducible depth of proteome coverage, and ideally, a high degree of cross-laboratory analytical reproducibility. Mass spectrometry centered proteomics has evolved steadily over the past decade, now mature enough to derive extensive draft maps of the human proteome (1, 2). Nonetheless, a key requirement yet to be realized is to ensure that quantitative proteomics can be carried out in a timely manner while satisfying the aforementioned challenges associated with reproducibility. This is especially important for recent developments using data independent MS quantitation and multiple reaction monitoring on high-resolution MS (MRM-HR)¹ as they are both highly dependent on LC peptide retention time reproducibility and precursor detectability, while attempting to maximize proteome coverage (3). Strategies usually employed to increase the depth of proteome coverage utilize various sample fractionation methods including gel-based separation, affinity enrichment or depletion, protein or peptide chemical modification-based enrichment, and various peptide chromatography methods, particularly ion exchange chromatography (4–10). In comparison to an unfractionated “naive” sample, the trade-off in using these enrichments/fractionation approaches are higher risk of sample losses, introduction of undesired chemical modifications (e.g. oxidation, deamidation, N-terminal lactam formation), and the potential for result skewing and bias, as well as numerous time and human resources required to perform the sample preparation tasks. Online-coupled approaches aim to minimize those risks and address resource constraints. A widely practiced example of the benefits of online sample fractionation has been the decade long use of combining strong cation exchange chromatography (SCX) with C18 reversed-phase (RP) for peptide fractionation (known as MudPIT – multidimensional protein identification technology), where SCX and RP is performed under the same buffer conditions and the SCX elution performed with volatile organic cations compatible with reversed phase separation (11). This approach greatly increases analyte detection while avoiding sample handling losses. The MudPIT approach has been widely used for discovery proteomics (12–14), and we have previously shown that multiphasic separations also have utility for targeted proteomics when configured for selected reaction monitoring MS (SRM-MS). We showed substantial advantages of MudPIT-SRM-MS with reduced ion suppression, increased peak areas and lower limits of detection (LLOD) compared with conventional RP-SRM-MS (15).To improve the reproducibility of proteomic workflows, increase throughput and minimize sample loss, numerous microfluidic devices have been developed and integrated for proteomic applications (16, 17). These devices can broadly be classified into two groups: (1) microfluidic chips for peptide separation (18–25) and; (2) proteome reactors that combine enzymatic processing with peptide based fractionation (26–30). Because of the small dimension of these devices, they are readily able to integrate into nanoLC workflows. Various applications have been described including increasing proteome coverage (22, 27, 28) and targeting of phosphopeptides (24, 31, 32), glycopeptides and released glycans (29, 33, 34).In this work, we set out to take advantage of the benefits of multiphasic peptide separations and address the reproducibility needs required for high-throughput comparative proteomics using a variety of workflows. We integrated a multiphasic SCX and RP column in a “plug-and-play” microfluidic chip format for online fractionation, eliminating the need for users to make minimal dead volume connections between traps and columns. We show the flexibility of this format to provide robust peptide separation and reproducibility using conventional and topical mass spectrometry workflows. This was undertaken by coupling the multiphase liquid chromatography (LC) chip to a fast scanning Q-ToF mass spectrometer for data dependent MS/MS, data independent MS (SWATH) and for targeted proteomics using MRM-HR, showing clear advantages for repeatable analyses compared with conventional proteomic workflows.     

Development of a Novel Cross-linking Strategy for Fast and Accurate Identification of Cross-linked Peptides of Protein Complexes

Knowledge of elaborate structures of protein complexes is fundamental for understanding their functions and regulations. Although cross-linking coupled with mass spectrometry (MS) has been presented as a feasible strategy for structural elucidation of large multisubunit protein complexes, this method has proven challenging because of technical difficulties in unambiguous identification of cross-linked peptides and determination of cross-linked sites by MS analysis. In this work, we developed a novel cross-linking strategy using a newly designed MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). DSSO contains two symmetric collision-induced dissociation (CID)-cleavable sites that allow effective identification of DSSO-cross-linked peptides based on their distinct fragmentation patterns unique to cross-linking types (i.e. interlink, intralink, and dead end). The CID-induced separation of interlinked peptides in MS/MS permits MS³ analysis of single peptide chain fragment ions with defined modifications (due to DSSO remnants) for easy interpretation and unambiguous identification using existing database searching tools. Integration of data analyses from three generated data sets (MS, MS/MS, and MS³) allows high confidence identification of DSSO cross-linked peptides. The efficacy of the newly developed DSSO-based cross-linking strategy was demonstrated using model peptides and proteins. In addition, this method was successfully used for structural characterization of the yeast 20 S proteasome complex. In total, 13 non-redundant interlinked peptides of the 20 S proteasome were identified, representing the first application of an MS-cleavable cross-linker for the characterization of a multisubunit protein complex. Given its effectiveness and simplicity, this cross-linking strategy can find a broad range of applications in elucidating the structural topology of proteins and protein complexes.Proteins form stable and dynamic multisubunit complexes under different physiological conditions to maintain cell viability and normal cell homeostasis. Detailed knowledge of protein interactions and protein complex structures is fundamental to understanding how individual proteins function within a complex and how the complex functions as a whole. However, structural elucidation of large multisubunit protein complexes has been difficult because of a lack of technologies that can effectively handle their dynamic and heterogeneous nature. Traditional methods such as nuclear magnetic resonance (NMR) analysis and x-ray crystallography can yield detailed information on protein structures; however, NMR spectroscopy requires large quantities of pure protein in a specific solvent, whereas x-ray crystallography is often limited by the crystallization process.In recent years, chemical cross-linking coupled with mass spectrometry (MS) has become a powerful method for studying protein interactions (1–3). Chemical cross-linking stabilizes protein interactions through the formation of covalent bonds and allows the detection of stable, weak, and/or transient protein-protein interactions in native cells or tissues (4–9). In addition to capturing protein interacting partners, many studies have shown that chemical cross-linking can yield low resolution structural information about the constraints within a molecule (2, 3, 10) or protein complex (11–13). The application of chemical cross-linking, enzymatic digestion, and subsequent mass spectrometric and computational analyses for the elucidation of three-dimensional protein structures offers distinct advantages over traditional methods because of its speed, sensitivity, and versatility. Identification of cross-linked peptides provides distance constraints that aid in constructing the structural topology of proteins and/or protein complexes. Although this approach has been successful, effective detection and accurate identification of cross-linked peptides as well as unambiguous assignment of cross-linked sites remain extremely challenging due to their low abundance and complicated fragmentation behavior in MS analysis (2, 3, 10, 14). Therefore, new reagents and methods are urgently needed to allow unambiguous identification of cross-linked products and to improve the speed and accuracy of data analysis to facilitate its application in structural elucidation of large protein complexes.A number of approaches have been developed to facilitate MS detection of low abundance cross-linked peptides from complex mixtures. These include selective enrichment using affinity purification with biotinylated cross-linkers (15–17) and click chemistry with alkyne-tagged (18) or azide-tagged (19, 20) cross-linkers. In addition, Staudinger ligation has recently been shown to be effective for selective enrichment of azide-tagged cross-linked peptides (21). Apart from enrichment, detection of cross-linked peptides can be achieved by isotope-labeled (22–24), fluorescently labeled (25), and mass tag-labeled cross-linking reagents (16, 26). These methods can identify cross-linked peptides with MS analysis, but interpretation of the data generated from interlinked peptides (two peptides connected with the cross-link) by automated database searching remains difficult. Several bioinformatics tools have thus been developed to interpret MS/MS data and determine interlinked peptide sequences from complex mixtures (12, 14, 27–32). Although promising, further developments are still needed to make such data analyses as robust and reliable as analyzing MS/MS data of single peptide sequences using existing database searching tools (e.g. Protein Prospector, Mascot, or SEQUEST).Various types of cleavable cross-linkers with distinct chemical properties have been developed to facilitate MS identification and characterization of cross-linked peptides. These include UV photocleavable (33), chemical cleavable (19), isotopically coded cleavable (24), and MS-cleavable reagents (16, 26, 34–38). MS-cleavable cross-linkers have received considerable attention because the resulting cross-linked products can be identified based on their characteristic fragmentation behavior observed during MS analysis. Gas-phase cleavage sites result in the detection of a “reporter” ion (26), single peptide chain fragment ions (35–38), or both reporter and fragment ions (16, 34). In each case, further structural characterization of the peptide product ions generated during the cleavage reaction can be accomplished by subsequent MSⁿ1 analysis. Among these linkers, the “fixed charge” sulfonium ion-containing cross-linker developed by Lu et al. (37) appears to be the most attractive as it allows specific and selective fragmentation of cross-linked peptides regardless of their charge and amino acid composition based on their studies with model peptides.Despite the availability of multiple types of cleavable cross-linkers, most of the applications have been limited to the study of model peptides and single proteins. Additionally, complicated synthesis and fragmentation patterns have impeded most of the known MS-cleavable cross-linkers from wide adaptation by the community. Here we describe the design and characterization of a novel and simple MS-cleavable cross-linker, DSSO, and its application to model peptides and proteins and the yeast 20 S proteasome complex. In combination with new software developed for data integration, we were able to identify DSSO-cross-linked peptides from complex peptide mixtures with speed and accuracy. Given its effectiveness and simplicity, we anticipate a broader application of this MS-cleavable cross-linker in the study of structural topology of other protein complexes using cross-linking and mass spectrometry.     

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

In large-scale proteomic experiments, multiple peptide precursors are often cofragmented simultaneously in the same mixture tandem mass (MS/MS) spectrum. These spectra tend to elude current computational tools because of the ubiquitous assumption that each spectrum is generated from only one peptide. Therefore, tools that consider multiple peptide matches to each MS/MS spectrum can potentially improve the relatively low spectrum identification rate often observed in proteomics experiments. More importantly, data independent acquisition protocols promoting the cofragmentation of multiple precursors are emerging as alternative methods that can greatly improve the throughput of peptide identifications but their success also depends on the availability of algorithms to identify multiple peptides from each MS/MS spectrum. Here we address a fundamental question in the identification of mixture MS/MS spectra: determining the statistical significance of multiple peptides matched to a given MS/MS spectrum. We propose the MixGF generating function model to rigorously compute the statistical significance of peptide identifications for mixture spectra and show that this approach improves the sensitivity of current mixture spectra database search tools by a ≈30–390%. Analysis of multiple data sets with MixGF reveals that in complex biological samples the number of identified mixture spectra can be as high as 20% of all the identified spectra and the number of unique peptides identified only in mixture spectra can be up to 35.4% of those identified in single-peptide spectra.The advancement of technology and instrumentation has made tandem mass (MS/MS)¹ spectrometry the leading high-throughput method to analyze proteins (1, 2, 3). In typical experiments, tens of thousands to millions of MS/MS spectra are generated and enable researchers to probe various aspects of the proteome on a large scale. Part of this success hinges on the availability of computational methods that can analyze the large amount of data generated from these experiments. The classical question in computational proteomics asks: given an MS/MS spectrum, what is the peptide that generated the spectrum? However, it is increasingly being recognized that this assumption that each MS/MS spectrum comes from only one peptide is often not valid. Several recent analyses show that as many as 50% of the MS/MS spectra collected in typical proteomics experiments come from more than one peptide precursor (4, 5). The presence of multiple peptides in mixture spectra can decrease their identification rate to as low as one half of that for MS/MS spectra generated from only one peptide (6, 7, 8). In addition, there have been numerous developments in data independent acquisition (DIA) technologies where multiple peptide precursors are intentionally selected to cofragment in each MS/MS spectrum (9, 10, 11, 12, 13, 14, 15). These emerging technologies can address some of the enduring disadvantages of traditional data-dependent acquisition (DDA) methods (e.g. low reproducibility (16)) and potentially increase the throughput of peptide identification 5–10 fold (4, 17). However, despite the growing importance of mixture spectra in various contexts, there are still only a few computational tools that can analyze mixture spectra from more than one peptide (18, 19, 20, 21, 8, 22). Our recent analysis indicated that current database search methods for mixture spectra still have relatively low sensitivity compared with their single-peptide counterpart and the main bottleneck is their limited ability to separate true matches from false positive matches (8). Traditionally problem of peptide identification from MS/MS spectra involves two sub-problems: 1) define a Peptide-Spectrum-Match (PSM) scoring function that assigns each MS/MS spectrum to the peptide sequence that most likely generated the spectrum; and 2) given a set of top-scoring PSMs, select a subset that corresponds to statistical significance PSMs. Here we focus on the second problem, which is still an ongoing research question even for the case of single-peptide spectra (23, 24, 25, 26). Intuitively the second problem is difficult because one needs to consider spectra across the whole data set (instead of comparing different peptide candidates against one spectrum as in the first problem) and PSM scoring functions are often not well-calibrated across different spectra (i.e. a PSM score of 50 may be good for one spectrum but poor for a different spectrum). Ideally, a scoring function will give high scores to all true PSMs and low scores to false PSMs regardless of the peptide or spectrum being considered. However, in practice, some spectra may receive higher scores than others simply because they have more peaks or their precursor mass results in more peptide candidates being considered from the sequence database (27, 28). Therefore, a scoring function that accounts for spectrum or peptide-specific effects can make the scores more comparable and thus help assess the confidence of identifications across different spectra. The MS-GF solution to this problem is to compute the per-spectrum statistical significance of each top-scoring PSM, which can be defined as the probability that a random peptide (out of all possible peptide within parent mass tolerance) will match to the spectrum with a score at least as high as that of the top-scoring PSM. This measures how good the current best match is in relation to all possible peptides matching to the same spectrum, normalizing any spectrum effect from the scoring function. Intuitively, our proposed MixGF approach extends the MS-GF approach to now calculate the statistical significance of the top pair of peptides matched from the database to a given mixture spectrum M (i.e. the significance of the top peptide–peptide spectrum match (PPSM)). As such, MixGF determines the probability that a random pair of peptides (out of all possible peptides within parent mass tolerance) will match a given mixture spectrum with a score at least as high as that of the top-scoring PPSM.Despite the theoretical attractiveness of computing statistical significance, it is generally prohibitive for any database search methods to score all possible peptides against a spectrum. Therefore, earlier works in this direction focus on approximating this probability by assuming the score distribution of all PSMs follows certain analytical form such as the normal, Poisson or hypergeometric distributions (29, 30, 31). In practice, because score distributions are highly data-dependent and spectrum-specific, these model assumptions do not always hold. Other approaches tried to learn the score distribution empirically from the data (29, 27). However, one is most interested in the region of the score distribution where only a small fraction of false positives are allowed (typically at 1% FDR). This usually corresponds to the extreme tail of the distribution where p values are on the order of 10⁻⁹ or lower and thus there is typically lack of sufficient data points to accurately model the tail of the score distribution (32). More recently, Kim et al. (24) and Alves et al. (33), in parallel, proposed a generating function approach to compute the exact score distribution of random peptide matches for any spectra without explicitly matching all peptides to a spectrum. Because it is an exact computation, no assumption is made about the form of score distribution and the tail of the distribution can be computed very accurately. As a result, this approach substantially improved the ability to separate true matches from false positive ones and lead to a significant increase in sensitivity of peptide identification over state-of-the-art database search tools in single-peptide spectra (24).For mixture spectra, it is expected that the scores for the top-scoring match will be even less comparable across different spectra because now more than one peptide and different numbers of peptides can be matched to each spectrum at the same time. We extend the generating function approach (24) to rigorously compute the statistical significance of multiple-Peptide-Spectrum Matches (mPSMs) and demonstrate its utility toward addressing the peptide identification problem in mixture spectra. In particular, we show how to extend the generating approach for mixture from two peptides. We focus on this relatively simple case of mixture spectra because it accounts for a large fraction of mixture spectra presented in traditional DDA workflows (5). This allows us to test and develop algorithmic concepts using readily-available DDA data because data with more complex mixture spectra such as those from DIA workflows (11) is still not widely available in public repositories.     

N-Terminal-oriented Proteogenomics of the Marine Bacterium Roseobacter Denitrificans Och114 using N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) Labeling and Diagonal Chromatography

Given the ease of whole genome sequencing with next-generation sequencers, structural and functional gene annotation is now purely based on automated prediction. However, errors in gene structure are frequent, the correct determination of start codons being one of the main concerns. Here, we combine protein N termini derivatization using (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP Ac-OSu) as a labeling reagent with the COmbined FRActional DIagonal Chromatography (COFRADIC) sorting method to enrich labeled N-terminal peptides for mass spectrometry detection. Protein digestion was performed in parallel with three proteases to obtain a reliable automatic validation of protein N termini. The analysis of these N-terminal enriched fractions by high-resolution tandem mass spectrometry allowed the annotation refinement of 534 proteins of the model marine bacterium Roseobacter denitrificans OCh114. This study is especially efficient regarding mass spectrometry analytical time. From the 534 validated N termini, 480 confirmed existing gene annotations, 41 highlighted erroneous start codon annotations, five revealed totally new mis-annotated genes; the mass spectrometry data also suggested the existence of multiple start sites for eight different genes, a result that challenges the current view of protein translation initiation. Finally, we identified several proteins for which classical genome homology-driven annotation was inconsistent, questioning the validity of automatic annotation pipelines and emphasizing the need for complementary proteomic data. All data have been deposited to the ProteomeXchange with identifier PXD000337.Recent developments in mass spectrometry and bioinformatics have established proteomics as a common and powerful technique for identifying and quantifying proteins at a very broad scale, but also for characterizing their post-translational modifications and interaction networks (1, 2). In addition to the avalanche of proteomic data currently being reported, many genome sequences are established using next-generation sequencing, fostering proteomic investigations of new cellular models. Proteogenomics is a relatively recent field in which high-throughput proteomic data is used to verify coding regions within model genomes to refine the annotation of their sequences (2–8). Because genome annotation is now fully automated, the need for accurate annotation for model organisms with experimental data is crucial. Many projects related to genome re-annotation of microorganisms with the help of proteomics have been recently reported, such as for Mycoplasma pneumoniae (9), Rhodopseudomonas palustris (10), Shewanella oneidensis (11), Thermococcus gammatolerans (12), Deinococcus deserti (13), Salmonella thyphimurium (14), Mycobacterium tuberculosis (15, 16), Shigella flexneri (17), Ruegeria pomeroyi (18), and Candida glabrata (19), as well as for higher organisms such as Anopheles gambiae (20) and Arabidopsis thaliana (4, 5).The most frequently reported problem in automatic annotation systems is the correct identification of the translational start codon (21–23). The error rate depends on the primary annotation system, but also on the organism, as reported for Halobacterium salinarum and Natromonas pharaonis (24), Deinococcus deserti (21), and Ruegeria pomeroyi (18), where the error rate is estimated above 10%. Identification of a correct translational start site is essential for the genetic and biochemical analysis of a protein because errors can seriously impact subsequent biological studies. If the N terminus is not correctly identified, the protein will be considered in either a truncated or extended form, leading to errors in bioinformatic analyses (e.g. during the prediction of its molecular weight, isoelectric point, cellular localization) and major difficulties during its experimental characterization. For example, a truncated protein may be heterologously produced as an unfolded polypeptide recalcitrant to structure determination (25). Moreover, N-terminal modifications, which are poorly documented in annotation databases, may occur (26, 27).Unfortunately, the poor polypeptide sequence coverage obtained for the numerous low abundance proteins in current shotgun MS/MS proteomic studies implies that the overall detection of N-terminal peptides obtained in proteogenomic studies is relatively low. Different methods for establishing the most extensive list of protein N termini, grouped under the so-called “N-terminomics” theme, have been proposed to selectively enrich or improve the detection of these peptides (2, 28, 29). Large N-terminome studies have recently been reported based on resin-assisted enrichment of N-terminal peptides (30) or terminal amine isotopic labeling of substrates (TAILS) coupled to depletion of internal peptides with a water-soluble aldehyde-functionalized polymer (31–35). Among the numerous N-terminal-oriented methods (2), specific labeling of the N terminus of intact proteins with N-tris(2,4,6-trimethoxyphenyl)phosphonium acetyl succinamide (TMPP-Ac-OSu)¹ has proven reliable (21, 36–39). TMPP-derivatized N-terminal peptides have interesting properties for further LC-MS/MS mass spectrometry: (1) an increase in hydrophobicity because of the trimethoxyphenyl moiety added to the peptides, increasing their retention times in reverse phase chromatography, (2) improvement of their ionization because of the introduction of a positively charged group, and (3) a much simpler fragmentation pattern in tandem mass spectrometry. Other reported approaches rely on acetylation, followed by trypsin digestion, and then biotinylation of free amino groups (40); guanidination of lysine lateral chains followed by N-biotinylation of the N termini and trypsin digestion (41); or reductive amination of all free amino groups with formaldehyde preceeding trypsin digestion (42). Recently, we applied the TMPP method to the proteome of the Deinococcus deserti bacterium isolated from upper sand layers of the Sahara desert (13). This method enabled the detection of N-terminal peptides allowing the confirmation of 278 translation initiation codons, the correction of 73 translation starts, and the identification of non-canonical translation initiation codons (21). However, most TMPP-labeled N-terminal peptides are hidden among the more abundant internal peptides generated after proteolysis of a complex proteome, precluding their detection. This results in disproportionately fewer N-terminal validations, that is, 5 and 8% of total polypeptides coded in the theoretical proteomes of Mycobacterium smegmatis (37) and Deinococcus deserti (21) with a total of 342 and 278 validations, respectively.An interesting chromatographic method to fractionate peptide mixtures for gel-free high-throughput proteome analysis has been developed over the last years and applied to various topics (43, 44). This technique, known as COmbined FRActional DIagonal Chromatography (COFRADIC), uses a double chromatographic separation with a chemical reaction in between to change the physico-chemical properties of the extraneous peptides to be resolved from the peptides of interest. Its previous applications include the separation of methionine-containing peptides (43), N-terminal peptide enrichment (45, 46), sulfur amino acid-containing peptides (47), and phosphorylated peptides (48). COFRADIC was identified as the best method for identification of N-terminal peptides of two archaea, resulting in the identification of 240 polypeptides (9% of the theoretical proteome) for Halobacterium salinarum and 220 (8%) for Natronomonas pharaonis (24).Taking advantage of both the specificity of TMPP labeling, the resolving power of COFRADIC for enrichment, and the increase in information through the use of multiple proteases, we performed the proteogenomic analysis of a marine bacterium from the Roseobacter clade, namely Roseobacter denitrificans OCh114. This novel approach allowed us to validate and correct 534 unique proteins (13% of the theoretical proteome) with TMPP-labeled N-terminal signatures obtained using high-resolution tandem mass spectrometry. We corrected 41 annotations and detected five new open reading frames in the R. denitrificans genome. We further identified eight distinct proteins showing direct evidence for multiple start sites.     

Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance

Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform performance for analysis of complex biological proteomes across different laboratories in the community. Individual preparations of the yeast Saccharomyces cerevisiae proteome have been used extensively by laboratories in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance standard because it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins. In this study, we describe a standard operating protocol for large scale production of the yeast performance standard and offer aliquots to the community through the National Institute of Standards and Technology where the yeast proteome is under development as a certified reference material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a reference data set demonstrating typical performance of commonly used ion trap instrument platforms in expert laboratories; the results provide a basis for laboratories to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Additionally, we demonstrate how the yeast reference, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix, thereby providing a metric to evaluate and minimize preanalytical and analytical variation in comparative proteomics experiments.Access to proteomics performance standards is essential for several reasons. First, to generate the highest quality data possible, proteomics laboratories routinely benchmark and perform quality control (QC)1 monitoring of the performance of their instrumentation using standards. Second, appropriate standards greatly facilitate the development of improvements in technologies by providing a timeless standard with which to evaluate new protocols or instruments that claim to improve performance. For example, it is common practice for an individual laboratory considering purchase of a new instrument to require the vendor to run “demo” samples so that data from the new instrument can be compared head to head with existing instruments in the laboratory. Third, large scale proteomics studies designed to aggregate data across laboratories can be facilitated by the use of a performance standard to measure reproducibility across sites or to compare the performance of different LC-MS configurations or sample processing protocols used between laboratories to facilitate development of optimized standard operating procedures (SOPs).Most individual laboratories have adopted their own QC standards, which range from mixtures of known synthetic peptides to digests of bovine serum albumin or more complex mixtures of several recombinant proteins (1). However, because each laboratory performs QC monitoring in isolation, it is difficult to compare the performance of LC-MS platforms throughout the community.Several standards for proteomics are available for request or purchase (2, 3). RM8327 is a mixture of three peptides developed as a reference material in collaboration between the National Institute of Standards and Technology (NIST) and the Association of Biomolecular Resource Facilities. Mixtures of 15–48 purified human proteins are also available, such as the HUPO (Human Proteome Organisation) Gold MS Protein Standard (Invitrogen), the Universal Proteomics Standard (UPS1; Sigma), and CRM470 from the European Union Institute for Reference Materials and Measurements. Although defined mixtures of peptides or proteins can address some benchmarking and QC needs, there is an additional need for more complex reference materials to fully represent the challenges of LC-MS data acquisition in complex matrices encountered in biological samples (2, 3).Although it has not been widely distributed as a reference material, the yeast Saccharomyces cerevisiae proteome has been extensively used by the proteomics community to characterize the capabilities of a variety of LC-MS-based approaches (4–15). Yeast provides a uniquely attractive complex performance standard for several reasons. Yeast encodes a complex proteome consisting of ∼4,500 proteins expressed during normal growth conditions (7, 16–18). The concentration range of yeast proteins is sufficient to challenge the dynamic range of conventional mass spectrometers; the abundance of proteins ranges from fewer than 50 to more than 10⁶ molecules per cell (4, 15, 16). Additionally, it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins (5, 9, 16, 17, 19, 20) as well as LC-MS/MS data sets showing good correlation between LC-MS/MS detection efficiency and the protein abundance estimates (4, 11, 12, 15). Finally, it is inexpensive and easy to produce large quantities of yeast protein extract for distribution.In this study, we describe large scale production of a yeast S. cerevisiae performance standard, which we offer to the community through NIST. Through a series of interlaboratory studies, we created a reference data set characterizing the yeast performance standard and defining reasonable performance of ion trap-based LC-MS platforms in expert laboratories using a series of performance metrics. This publicly available data set provides a basis for additional laboratories using the yeast standard to benchmark their own performance as well as to improve upon the current status by evolving protocols, improving instrumentation, or developing new technologies. Finally, we demonstrate how the yeast performance standard, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix.     

Unique Ion Signature Mass Spectrometry, a Deterministic Method to Assign Peptide Identity

The growing use of selected reaction monitoring (SRM) mass spectrometry in proteomic analyses led us to investigate how to identify peptides by SRM using only a minimal number of fragment ions. By using a computational model of the SRM work flow we computed the potential interferences from other peptides in a given proteome. From these results, we selected the deterministic SRM addresses that contained sufficient information to confer peptide and protein identity that we termed unique ion signatures (UIS). We computationally showed that UIS comprised of only two transitions are diagnostic for >99% of Escherichia coli proteins and >96% of human proteins that possess a sequence-unique peptide. We demonstrated an example of experimental use of UIS using a modified SRM methodology to profile the E. coli tricarboxylic acid cycle from a single injection of cell lysate. In addition, we showed the potential of UIS to form the first functionally orthogonal approach to validate peptide assignments obtained from conventional analyses of MS/MS spectra. The UIS methodology is a novel deterministic peptide identification method for MS/MS spectra based on information content. These robust theoretical assays will have widespread use when integrated with previously collected MS/MS data and conventional proteomics technologies.Shotgun proteomic analyses using multidimensional LC/MS/MS show great capacity for rapid protein analysis. This is arguably the most prevalent work flow for high throughput comparative proteomics, utilizing information-dependent acquisition (IDA)¹ to acquire MS/MS triggered by the signals generated from incoming peptides (1–3). Despite the utility and widespread use of this approach, there remain inherent problems including a relatively high level of ambiguous and false peptide assignments (∼5%) as well as high numbers of unassigned mass spectra (4–6). The reason for this level of ambiguity stems in part from the non-deterministic nature of the identification algorithms. Without the use of reference standards the only way to know a spectrum was generated by a given peptide with absolute certainty is for the spectrum to contain a fragment pattern that conclusively demonstrates the presence of each amino acid. Unfortunately this level of coverage is extremely rare in proteomics data.More recently, selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) mass spectrometry methods have been deployed for proteomic analyses (7–20). This has occurred as proteomics has matured from a discovery-oriented discipline into a more targeted and quantitative field. The method is conventionally conducted using triple quadrupole mass spectrometers where two rounds of mass selection provide excellent fidelity and sensitivity to monitor one or more predetermined target peptides generally in the context of a complex sample such as a cell lysate. Using this approach the mass spectrometer continually monitors the selected precursor ion m/z (Q1) and a subsequent product ion m/z (Q3) from the target analyte. SRM experiments can be used to conduct several rounds of these scans targeting different product ions in an attempt to bolster the confidence that the Q1 → Q3 transitions monitor the intended analyte with fidelity. A key point of contrast with IDA experiments is the need to preselect target analytes for monitoring. This can be achieved by harvesting data from previous discovery-based experiments or by in silico predictions such as MRM-initiated detection and sequencing (MIDAS) (10, 12). Regardless the key underlying principle of SRM in proteomics applications is that the selected set of precursor and product ions contain sufficient information to proxy for the target peptide and thereby its protein of origin. Given that proteomics SRM experiments are conducted with a minimal set of transitions, one must accept that a degree of uncertainty resides in any such assay. To date, the magnitude of this uncertainty has not been studied. This remains a key point even with MS instruments capable of conducting subsequent full MS/MS scans triggered by SRM (e.g. QTrap) as these are lower sensitivity scans that may contain insufficient fragmentation data to conclusively confer peptide identity.The problem of interference is also present in SRM experiments. To achieve acceptable sensitivity a large Q1 m/z window (±0.3–1.0 m/z) is needed. This in turn allows other peptides with similar Q1 m/z and elution properties to interfere with detection of the desired target. The frequency of these interferences would likely increase as the complexity of the sample increases creating a greater likelihood of false positives. Clearly this is not an unexpected result as conventional peptide identification strategies utilizing tandem MS result in some false assignments. Therefore, it would be unreasonable to expect that SRM assays that typically utilize fewer product ions than MS/MS experiments would not also encounter similar interference (21).In this study we investigated the information content of SRM assays and in doing so exposed the potential redundancy. Computational simulations of the experiment enabled us to demonstrate that directed selection of SRM precursor and product ions can avoid the pitfalls of interference by selecting ion combinations that uniquely map to target peptides within the context of the simulation. We used these unique ion signatures (UIS) in a proof of concept study to direct SRM data acquisition for the exclusive detection of enzymes in the Escherichia coli tricarboxylic acid cycle. In addition, given that UIS have been calculated to uniquely define target peptides in the experimental context, we demonstrated the applicability of UIS as an orthogonal validation of peptide identity for traditional MS/MS experiments.     

Sialic Acid-focused Quantitative Mouse Serum Glycoproteomics by Multiple Reaction Monitoring Assay

Despite increasing importance of protein glycosylation, most of the large-scale glycoproteomics have been limited to profiling the sites of N-glycosylation. However, in-depth knowledge of protein glycosylation to uncover functions and their clinical applications requires quantitative glycoproteomics eliciting both peptide and glycan sequences concurrently. Here we describe a novel strategy for the multiplexed quantitative mouse serum glycoproteomics based on a specific chemical ligation, namely, reverse glycoblotting technique, focusing sialic acids and multiple reaction monitoring (MRM). LC-MS/MS analysis of de-glycosylated peptides identified 270 mouse serum peptides (95 glycoproteins) as sialylated glycopeptides, of which 67 glycopeptides were fully characterized by MS/MS analyses in a straightforward manner. We revealed the importance of a fragment ion containing innermost N-acetylglucosamine (GlcNAc) residue as MRM transitions regardless the sequence of the peptides. Versatility of the reverse glycoblotting-assisted MRM assays was demonstrated by quantitative comparison of 25 targeted glycopeptides from 16 proteins between mice with homo and hetero types of diabetes disease model.Clinical proteomics focusing on the identification and validation of biomarkers and the discovery of proteins as therapeutic targets is an emerging and highly important area of proteomics. Biomarkers are measurable indicators of a specific biological state (particularly one relevant to the risk of contraction) and the presence or the stage of disease, and are thus expected to be useful for the prediction, detection, and diagnosis of disease as well as to follow the efficacy, toxicology, and side effects of drug treatment, and to provide new functional insights into biological processes.At present, proteomics methods based on mass spectrometry (MS) have emerged as the preferred strategy for discovery of diagnostic, prognostic, and therapeutic protein biomarkers. Most biomarker discovery studies use unbiased, “identified-based” approaches that rely on high performance mass spectrometers and extensive sample processing. Semiquantitative comparisons of protein relative abundance between disease and control patient samples are used to identify proteins that are differentially expressed and, thus, to populate lists of potential biomarkers. De novo proteomics discovery experiments often result in tens to hundreds of candidate biomarkers that must be subsequently verified in serum. However, despite the large numbers of putative biomarkers, only a small number of them are passed through the development and validation process into clinical practice, and their rate of introduction is declining. The first non-standard abbreviation (MS above is standard) must be footnoted the same as the abbreviation footnote, and MRM must be the first abbreviation in the list because it is the one footnoted. After that the order does not matter.Targeted proteomics using multiple reaction monitoring (MRM)1 is emerging as a technology that complements the discovery capabilities of shotgun strategies as well as an alternative powerful novel MS-based approach to measure a series of candidate biomarkers (1–7). Therefore, MRM is expected to provide a powerful high throughput platform for biomarker validation, although clinical validation of novel biomarkers has been traditionally relying on immunoassays (8, 9). MRM exploits the unique capabilities of triple quadrupoles (QQQ) MS for quantitative analysis. In MRM, the first and the third quadrupoles act as filters to specifically select predefined m/z values corresponding to the peptide precursor ion and specific fragment ion of the peptide, whereas the second quadrupole serves as collision cell. Several such transitions (precursor/fragment ion pairs) are monitored over time, yielding a set of chromatographic traces with retention time and signal intensity for a specific transition as coordinates. These measurements have been multiplexed to provide 30 or more specific assays in one run. Such methods are slowly gaining acceptance in the clinical laboratory for the routine measurement of endogenous metabolites (10) (e.g. in screening newborns for a panel of inborn errors of metabolism) some drugs (11) (e.g. immunosuppressants), and the component analysis of sugars (12).One of the profound challenges in clinical proteomics is the need to handle highly complex biological mixtures. This complexity presents unique analytical challenges that are further magnified with the use of clinical serum/plasma samples to search for novel biomarkers of human disease. The serum proteome is composed of tens of thousands of unique proteins, of which concentrations may exceed 10 orders of magnitude. Protein glycosylation, one of the most common post-translational modifications, generates tremendous diversity, complexity, and heterogeneity of gene products. It changes the biological and physical properties of proteins, which include functions as signals or ligands to control their distribution, antigenicity, metabolic fate, stability, and solubility. Protein glycosylation, in particular by N-linked glycans, is prevalent in proteins destined for extracellular environments. These include proteins on the extracellular side of the plasma membrane, secreted proteins, and proteins contained in body fluids (such as blood serum, cerebrospinal fluid, urine, breast milk, saliva, lung lavage fluid, or pancreatic juice). Considering that such body fluids are most easily accessible for diagnostic and therapeutic purposes, it is not surprising that many clinical biomarkers and therapeutic targets are glycoproteins. These include, for example, cancer antigen 125 (CA125) in ovarian cancer, human epidermal growth factor receptor 2 (Her2/neu) in breast cancer, and prostate-specific antigen (PSA) in prostate cancer. In addition, changes in the extent of glycosylation and the structure of N-glycans or O-glycans attached to proteins on the cell surface and in body fluids have been shown to correlate with cancer and other disease states, highlighting the clinical importance of this modification as an indicator or effector of pathologic mechanisms (13–16). Thus, clinical proteomic platforms should have capability to provide protein glycosylation information as well as sufficient analytical depth to reliably detect and quantify specific proteins with sufficient accuracy and throughput.To improve the detection limits to the required sensitivities, one needs to dramatically reduce the complexity of the sera samples. For focused glycoproteomics, several techniques using lectins or antibodies enabling the large-scale identification of glycoproteins have recently been developed (17–19). Notably, Zhang et al. reported a method for the selective isolation of peptides based on chemical oxidation of the carbohydrate moiety and subsequent conjugation to a solid support using hydrazide chemistry (20–26). However, it is not possible to provide any structural information about N-glycans because the MS analysis is performed on peptides of which N-glycans are removed preferentially by treating with peptide N-glycanase (PNGase). In 2007, we developed a method for rapid enrichment analysis of peptides bearing sialylated N-glycans on the MALDI-TOF-MS platform (27). The method involves highly selective oxidation of sialic acid residues of glycopeptides to elaborate terminal aldehyde group and subsequent enrichment by chemical ligation with a polymer reagent, namely, reverse glycoblotting technique inspired from an original concept of glycoblotting method (28). This method, in principle, is capable identifying both glycan and peptide sequences concurrently. Recently, Nilsson et al. reported that glycopeptides from human cerebrospinal fluid can be enriched on the basis of the same principle as the reverse glycoblotting protocol, and captured glycopeptides were analyzed with ESI FT-ICR MS (29). Because it is well known that sialic acids play important roles in various biological processes including cell differentiation, immune response, and oncogenesis (30–34), our attention has been directed toward feasibility of the reverse glycoblotting technique in quantitative analysis of the specific glycopeptides carrying sialic acid(s) by combining with multiplexed MRM-based MS.     

Preprocessing Significantly Improves the Peptide/Protein Identification Sensitivity of High-resolution Isobarically Labeled Tandem Mass Spectrometry Data

Isobaric labeling techniques coupled with high-resolution mass spectrometry have been widely employed in proteomic workflows requiring relative quantification. For each high-resolution tandem mass spectrum (MS/MS), isobaric labeling techniques can be used not only to quantify the peptide from different samples by reporter ions, but also to identify the peptide it is derived from. Because the ions related to isobaric labeling may act as noise in database searching, the MS/MS spectrum should be preprocessed before peptide or protein identification. In this article, we demonstrate that there are a lot of high-frequency, high-abundance isobaric related ions in the MS/MS spectrum, and removing isobaric related ions combined with deisotoping and deconvolution in MS/MS preprocessing procedures significantly improves the peptide/protein identification sensitivity. The user-friendly software package TurboRaw2MGF (v2.0) has been implemented for converting raw TIC data files to mascot generic format files and can be downloaded for free from https://github.com/shengqh/RCPA.Tools/releases as part of the software suite ProteomicsTools. The data have been deposited to the ProteomeXchange with identifier PXD000994.Mass spectrometry-based proteomics has been widely applied to investigate protein mixtures derived from tissue, cell lysates, or from body fluids (1, 2). Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)¹ is the most popular strategy for protein/peptide mixtures analysis in shotgun proteomics (3). Large-scale protein/peptide mixtures are separated by liquid chromatography followed by online detection by tandem mass spectrometry. The capabilities of proteomics rely greatly on the performance of the mass spectrometer. With the improvement of MS technology, proteomics has benefited significantly from the high-resolution and excellent mass accuracy (4). In recent years, based on the higher efficiency of higher energy collision dissociation (HCD), a new “high–high” strategy (high-resolution MS as well as MS/MS(tandem MS)) has been applied instead of the “high–low” strategy (high-resolution MS, i.e. in Orbitrap, and low-resolution MS/MS, i.e. in ion trap) to obtain high quality tandem MS/MS data as well as full MS in shotgun proteomics. Both full MS scans and MS/MS scans can be performed, and the whole cycle time of MS detection is very compatible with the chromatographic time scale (5).High-resolution measurement is one of the most important features in mass spectrometric application. In this high–high strategy, high-resolution and accurate spectra will be achieved in tandem MS/MS scans as well as full MS scans, which makes isotopic peaks distinguishable from one another, thus enabling the easy calculation of precise charge states and monoisotopic mass. During an LC-MS/MS experiment, a multiply charged precursor ion (peptide) is usually isolated and fragmented, and then the multiple charge states of the fragment ions are generated and collected. After full extraction of peak lists from original tandem mass spectra, the commonly used search engines (i.e. Mascot (6), Sequest (7)) have no capability to distinguish isotopic peaks and recognize charge states, so all of the product ions are considered as all charge state hypotheses during the database search for protein identification. These multiple charge states of fragment ions and their isotopic cluster peaks can be incorrectly assigned by the search engine, which can cause false peptide identification. To overcome this issue, data preprocessing of the high-resolution MS/MS spectra is required before submitting them for identification. There are usually two major preprocessing steps used for high-resolution MS/MS data: deisotoping and deconvolution (8, 9). Deisotoping of spectra removes all isotopic peaks except monoisotopic peaks from multi-isotopic peaks. Deconvolution of spectra translates multiply charged ions to singly charged ions and also accumulates the intensity of fragment ions by summing up all the intensities from their multiply charged states. After performing these two data-preprocessing steps, the resulting spectra is simpler and cleaner and allows more precise database searching and accurate bioinformatics analysis.With the capacity to analyze multiple samples simultaneously, stable isotope labeling approaches have been widely used in quantitative proteomics. Stable isotope labeling approaches are categorized as metabolic labeling (SILAC, stable isotope labeling by amino acids in cell culture) and chemical labeling (10, 11). The peptides labeled by the SILAC approach are quantified by precursor ions in full MS spectra, whereas peptides that have been isobarically labeled using chemical means are quantified by reporter ions in MS/MS spectra. There are two similar isobaric chemical labeling methods: (1) isobaric tag for relative and absolute quantification (iTRAQ), and (2) tandem mass tag (TMT) (12, 13). These reagents contain an amino-reactive group that specifically reacts with N-terminal amino groups and epilson-amino groups of lysine residues to label digested peptides in a typical shotgun proteomics experiment. There are four different channels of isobaric tags: TMT two-plex, iTRAQ four-plex, TMT six-plex, and iTRAQ eight-plex (12–16). The number before “plex” denotes the number of samples that can be analyzed by the mass spectrum simultaneously. Peptides labeled with different isotopic variants of the tag show identical or similar mass and appear as a single peak in full scans. This single peak may be selected for subsequent MS/MS analysis. In an MS/MS scan, the mass of reporter ions (114 to 117 for iTRAQ four-plex, 113 to 121 for iTRAQ eight-plex, and 126 to 131for TMT six-plex upon CID or HCD activation) are associated with corresponding samples, and the intensities represent the relative abundances of the labeled peptides. Meanwhile, the other ions from the MS/MS spectra can be used for peptide identification. Because of the multiplexing capability, isobaric labeling methods combined with bottom-up proteomics have been widely applied for accurate quantification of proteins on a global scale (14, 17–19). Although mostly associated with peptide labeling, these isobaric labeling methods have also been applied at protein level (20–23).For the proteomic analysis of isobarically labeled peptides/proteins in “high–high” MS strategy, the common consensus is that accurate reporter ions can contribute to more accurate quantification. However, there is no evidence to show how the ions related to isobaric labeling affect the peptide/protein identification and what preprocessing steps should be taken for high-resolution isobarically labeled MS/MS. To demonstrate the effectiveness and importance of preprocessing, we examined how the combination of preprocessing steps improved peptide/protein sensitivity in database searching. Several combinatorial ways of data-preprocessing were applied for high-throughput data analysis including deisotoping to keep simple monoisotopic mass peaks, deconvolution of ions with multiple charge states, and preservation of top 10 peaks in every 100 Dalton mass range. After systematic analysis of high-resolution isobarically labeled spectra, we further processed the spectra and removed interferential ions that were not related to the peptide. Our results suggested that the preprocessing of isobarically labeled high-resolution tandem mass spectra significantly improved the peptide/protein identification sensitivity.     

Tools for Label-free Peptide Quantification

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.Over recent decades, mass spectrometry has become the analytical method of choice in most proteomics studies (e.g. Refs. 1–4). A standard mass spectrometric workflow allows for both protein identification and protein quantification (5) in some form. For a long time, the technology has been used mainly for qualitative assessments of protein mixtures, namely, to assess whether a specific protein is in the sample or not. However, for the majority of interesting research questions, especially in the field of systems biology, this binary information (present or not) is not sufficient (6). The necessity of more detailed information on protein expression levels drives the field of quantitative proteomics (7, 8), which enables the integration of proteomics data with other data sources and allows network-centered studies, as reviewed in Ref. 9. Recent studies show that mass-spectrometry-based quantitative proteomics experiments can provide quantitative information (relative or absolute) for large parts, if not the entire set, of expressed proteins (10–12).Since the isotope-coded affinity tag protocol was first published in 1999 (13), numerous labeling strategies have found their way into the field of quantitative proteomics (14). These include isotope-coded protein labeling (15), metabolic labeling (16, 17), and isobaric tags (18, 19). Comprehensive overviews of different quantification strategies can be found in Refs. 20 and 21. Because of the shortcomings of labeling strategies, label-free methods are increasingly gaining the interest of proteomics researchers (22, 23). In label-free quantification, no label is introduced to either of the samples. All samples are analyzed in separate LC/MS experiments, and the individual peptide properties of the individual measurements are then compared. Regardless of the quantification strategy, computational approaches for data analyses have become the critical final step of the proteomics workflow. Overviews of existing computational approaches in proteomics are provided in Refs. 24 and 25. The computational label-free quantification workflow in visualized in Fig. 1. Comparing peptide quantities using mass spectrometry remains a difficult task, because mass spectrometers have different response values for different chemical entities, and thus a direct comparison of different peptides is not possible. The computational analysis of a label-free quantitative data set consists of several steps that are mainly split in raw data signal processing and quantification. Signal processing steps comprise data reduction procedures such as baseline removal, denoising, and centroiding.Open in a separate windowFig. 1.The sample cohort that can be analyzed via label-free proteomics is not limited in size. Each sample is processed separately through the sample preparation and data acquisition pipeline. For data analysis, the data from the different LC/MS runs are combined.These steps can be accomplished in modular building blocks, or the entire analysis can be performed using monolithic analysis software. Recently, it has been shown that it is beneficial to combine modular blocks from different software tools to a consensus pipeline (26). The same study also illustrates the diversity of methods that are modularized by different software tools. In another recent publication, monolithic software packages are compared (27). In that study, the authors identify a set of seven metrics: detection sensitivity, detection consistency, intensity consistency, intensity accuracy, detection accuracy, statistical capability, and quantification accuracy. Despite the missing independence of these metrics and the loose reporting of software parameter settings, such comparative studies are of great interest to the field of quantitative proteomics. A general conclusion from these studies is that the choice of software might, to a certain degree, affect the final results of the study.Absolute quantification of peptides and proteins using intensity-based label-free methods is possible and can be done with excellent accuracy, if standard addition is used. With the help of known concentrations, calibration lines can be drawn, and absolute protein quantities can be directly inferred from these calibration measurements (28). Furthermore, it has been suggested that peptide peak intensities can be predicted and absolute quantities can be derived from these predictions (29). However, the limited accuracy of predictions or the need for peptides of known concentrations limits these approaches to selected proteins/peptides only and prevents their use on a proteome-wide scale.Spectral counting methods have also been used for the estimation of absolute concentrations on a global scale (30), albeit at drastically reduced accuracy relative to intensity-based methods. In one study, the authors used a mixture of 48 proteins with known concentrations and predicted the absolute copy number amounts of thousands of proteins based on that mixture. Despite the fact that large, proteome-wide data sets will dilute the effects of different peptide detectabilities on the individual protein level, such methods will always be limited in their accuracy of quantification.The generic nature of label-free quantification is not restricted to any model system and can also be employed with tissue or body fluids (31, 32). However, the label-free approach is more sensitive to technical deviations between LC/MS runs as information is compared between different measurements. Therefore, the reproducibility of the analytical platform is crucial for successful label-free quantification. The recent success of label-free quantification could only be accomplished through significant improvements of algorithms (33–36). An increasingly large collection of software tools for label-free proteomics have been published as open source applications or have entered the market as commercially available packages. This review aims at outlining the computational methods that are generally implemented by these software tools. Furthermore, we illustrate strengths and weaknesses of different tools. The review provides an information resource for the broad proteomics audience and does not illustrate all algorithmic details of the individual tools.     

DeMix Workflow for Efficient Identification of Cofragmented Peptides in High Resolution Data-dependent Tandem Mass Spectrometry

Based on conventional data-dependent acquisition strategy of shotgun proteomics, we present a new workflow DeMix, which significantly increases the efficiency of peptide identification for in-depth shotgun analysis of complex proteomes. Capitalizing on the high resolution and mass accuracy of Orbitrap-based tandem mass spectrometry, we developed a simple deconvolution method of “cloning” chimeric tandem spectra for cofragmented peptides. Additional to a database search, a simple rescoring scheme utilizes mass accuracy and converts the unwanted cofragmenting events into a surprising advantage of multiplexing. With the combination of cloning and rescoring, we obtained on average nine peptide-spectrum matches per second on a Q-Exactive workbench, whereas the actual MS/MS acquisition rate was close to seven spectra per second. This efficiency boost to 1.24 identified peptides per MS/MS spectrum enabled analysis of over 5000 human proteins in single-dimensional LC-MS/MS shotgun experiments with an only two-hour gradient. These findings suggest a change in the dominant “one MS/MS spectrum - one peptide” paradigm for data acquisition and analysis in shotgun data-dependent proteomics. DeMix also demonstrated higher robustness than conventional approaches in terms of lower variation among the results of consecutive LC-MS/MS runs.Shotgun proteomics analysis based on a combination of high performance liquid chromatography and tandem mass spectrometry (MS/MS) (1) has achieved remarkable speed and efficiency (2–7). In a single four-hour long high performance liquid chromatography-MS/MS run, over 40,000 peptides and 5000 proteins can be identified using a high-resolution Orbitrap mass spectrometer with data-dependent acquisition (DDA)¹ (2, 3). However, in a typical LC-MS analysis of unfractionated human cell lysate, over 100,000 individual peptide isotopic patterns can be detected (4), which corresponds to simultaneous elution of hundreds of peptides. With this complexity, a mass spectrometer needs to achieve ≥25 Hz MS/MS acquisition rate to fully sample all the detectable peptides, and ≥17 Hz to cover reasonably abundant ones (4). Although this acquisition rate is reachable by modern time-of-flight (TOF) instruments, the reported DDA identification results do not encompass all expected peptides. Recently, the next-generation Orbitrap instrument, working at 20 Hz MS/MS acquisition rate, demonstrated nearly full profiling of yeast proteome using an 80 min gradient, which opened the way for comprehensive analysis of human proteome in a time efficient manner (5).During the high performance liquid chromatography-MS/MS DDA analysis of complex samples, high density of co-eluting peptides results in a high probability for two or more peptides to overlap within an MS/MS isolation window. With the commonly used ±1.0–2.0 Th isolation windows, most MS/MS spectra are chimeric (4, 8–10), with cofragmenting precursors being naturally multiplexed. However, as has been discussed previously (9, 10), the cofragmentation events are currently ignored in most of the conventional analysis workflows. According to the prevailing assumption of “one MS/MS spectrum–one peptide,” chimeric MS/MS spectra are generally unwelcome in DDA, because the product ions from different precursors may interfere with the assignment of MS/MS fragment identities, increasing the rate of false discoveries in database search (8, 9). In some studies, the precursor isolation width was set as narrow as ±0.35 Th to prevent unwanted ions from being coselected, fragmented or detected (4, 5).On the contrary, multiplexing by cofragmentation is considered to be one of the solid advantages in data-independent acquisition (DIA) (10–13). In several commonly used DIA methods, the precursor ion selection windows are set much wider than in DDA: from 25 Th as in SWATH (12), to extremely broad range as in AIF (13). In order to use the benefit of MS/MS multiplexing in DDA, several approaches have been proposed to deconvolute chimeric MS/MS spectra. In “alternative peptide identification” method implemented in Percolator (14), a machine learning algorithm reranks and rescores peptide-spectrum matches (PSMs) obtained from one or more MS/MS search engines. But the deconvolution in Percolator is limited to cofragmented peptides with masses differing from the target peptide by the tolerance of the database search, which can be as narrow as a few ppm. The “active demultiplexing” method proposed by Ledvina et al. (15) actively separates MS/MS data from several precursors using masses of complementary fragments. However, higher-energy collisional dissociation often produces MS/MS spectra with too few complementary pairs for reliable peptide identification. The “MixDB” method introduces a sophisticated new search engine, also with a machine learning algorithm (9). And the “second peptide identification” method implemented in Andromeda/MaxQuant workflow (16) submits the same dataset to the search engine several times based on the list of chromatographic peptide features, subtracting assigned MS/MS peaks after each identification round. This approach is similar to the ProbIDTree search engine that also performed iterative identification while removing assigned peaks after each round of identification (17).One important factor for spectral deconvolution that has not been fully utilized in most conventional workflows is the excellent mass accuracy achievable with modern high-resolution mass spectrometry (18). An Orbitrap Fourier-transform mass spectrometer can provide mass accuracy in the range of hundreds of ppb (parts per billion) for mass peaks with high signal-to-noise (S/N) ratio (19). However, the mass error of peaks with lower S/N ratios can be significantly higher and exceed 1 ppm. Despite this dependence of the mass accuracy from the S/N level, most MS and MS/MS search engines only allow users to set hard cut-off values for the mass error tolerances. Moreover, some search engines do not provide the option of choosing a relative error tolerance for MS/MS fragments. Such negligent treatment of mass accuracy reduces the analytical power of high accuracy experiments (18).Identification results coming from different MS/MS search engines are sometimes not consistent because of different statistical assumptions used in scoring PSMs. Introduction of tools integrating the results of different search engines (14, 20, 21) makes the data interpretation even more complex and opaque for the user. The opposite trend—simplification of MS/MS data interpretation—is therefore a welcome development. For example, an extremely straightforward algorithm recently proposed by Wenger et al. (22) demonstrated a surprisingly high performance in peptide identification, even though it is only marginally more complex than simply counting the number of matches of theoretical fragment peaks in high resolution MS/MS, without any a priori statistical assumption.In order to take advantage of natural multiplexing of MS/MS spectra in DDA, as well as properly utilize high accuracy of Orbitrap-based mass spectrometry, we developed a simple and robust data analysis workflow DeMix. It is presented in Fig. 1 as an expansion of the conventional workflow. Principles of some of the processes used by the workflow are borrowed from other approaches, including the custom-made mass peak centroiding (20), chromatographic feature detection (19, 20), and two-pass database search with the first limited pass to provide a “software lock mass” for mass scale recalibration (23).Open in a separate windowFig. 1.An overview of the DeMix workflow that expands the conventional workflow, shown by the dashed line. Processes are colored in purple for TOPP, red for search engine (Morpheus/Mascot/MS-GF+), and blue for in-house programs.In DeMix workflow, the deconvolution of chimeric MS/MS spectra consists of simply “cloning” an MS/MS spectrum if a potential cofragmented peptide is detected. The list of candidate peptide precursors is generated from chromatographic feature detection, as in the MaxQuant/Andromeda workflow (16, 19), but using The OpenMS Proteomics Pipeline (TOPP) (20, 24). During the cloning, the precursor is replaced by the new candidate, but no changes in the MS/MS fragment list are made, and therefore the cloned MS/MS spectra remain chimeric. Processing such spectra requires a search engine tolerant to the presence of unassigned peaks, as such peaks are always expected when multiple precursors cofragment. Thus, we chose Morpheus (22) as a search engine. Based on the original search algorithm, we implement a reformed scoring scheme: Morpheus-AS (advanced scoring). It inherits all the basic principles from Morpheus but deeper utilizes the high mass accuracy of the data. This kind of database search removes the necessity of spectral processing for physical separation of MS/MS data into multiple subspectra (15), or consecutive subtraction of peaks (16, 17).Despite the fact that DeMix workflow is largely a combination of known approaches, it provides remarkable improvement compared with the state-of-the-art. On our Orbitrap Q-Exactive workbench, testing on a benchmark dataset of two-hour single-dimension LC-MS/MS experiments from HeLa cell lysate, we identified on average 1.24 peptide per MS/MS spectrum, breaking the “one MS/MS spectrum–one peptide” paradigm on the level of whole data set. At 1% false discovery rate (FDR), we obtained on average nine PSMs per second (at the actual acquisition rate of ca. seven MS/MS spectra per second), and detected 40 human proteins per minute.